A Predicate Connection Graph Based Logic With Flexible Control

LaTeX实战经验：如何写算法LaTeX中实现的呈现主要有两种⽅式：使⽤宏包algorithm2e, 这个宏包有很多可选项进⾏设定。

使⽤宏包algorithm与algorithmic,使⽤宏包algorithm2e：\usepackage[linesnumbered,boxed,ruled,commentsnumbered]{algorithm2e}%%算法包，注意设置所需可选项例⼦:\IncMargin{1em} % 使得⾏号不向外突出\begin{algorithm}\SetAlgoNoLine % 不要算法中的竖线\SetKwInOut{Input}{\textbf{输⼊}}\SetKwInOut{Output}{\textbf{输出}} % 替换关键词\Input{\\The observed user-item pair set $S$\;\\The feature matrix of items $F$\;\\The content features entities $A := \{A^u,A^v\}$\;\\}\Output{\\$\Theta \ := \{Y^u,Y^v\}$\;\\$W := \{W^u,W^v\}$\;\\}\BlankLineinitialize the model parameter $\Theta$ and $W$ with uniform $\left(-\sqrt{6}/{k},\sqrt{6}/{k}\right)$\; % 分号 \; 区分⼀⾏结束 standarized $\Theta$\;Initialize the popularity of categories $\rho$ randomly\;\Repeat{\text{convergence}}{Draw a triple $\left(m,i,j\right)$ with 算法\ref{al2}\;\For {each latent vector $\theta \in \Theta$}{$\theta \leftarrow \theta - \eta\frac{\partial L}{\partial \theta}$}\For {each $W^e \in W$}{Update $W^e$ with the rule defined in Eq.\ref{equ:W}\;}}\caption{Learning paramters for BPR\label{al3}}\end{algorithm}\DecMargin{1em}使⽤宏包algorithm与algorithmic\usepackage{algorithm, algorithmic}\begin{algorithm}\renewcommand{\algorithmicrequire}{\textbf{Input:}}\renewcommand{\algorithmicensure}{\textbf{Output:}}\caption{Bayesian Personalized Ranking Based Latent Feature Embedding Model}\label{alg:1}\begin{algorithmic}[1]\REQUIRE latent dimension $K$, $G$, target predicate $p$\ENSURE $U^{p}$, $V^{p}$, $b^{p}$\STATE Given target predicate $p$ and entire knowledge graph $G$, construct its bipartite subgraph, $G_{p}$\STATE $m$ = number of subject entities in $G_{p}$\STATE $n$ = number of object entities in $G_{p}$\STATE Generate a set of training samples $D_{p} = \{(s_p, o^{+}_{p}, o^{-}_{p})\}$ using uniform sampling technique \STATE Initialize $U^{p}$ as size $m \times K$ matrix with $0$ mean and standard deviation $0.1$\STATE Initialize $V^{p}$ as size $n \times K$ matrix with $0$ mean and stardard deviation $0.1$\STATE Initialize $b^{p}$ as size $n \times 1$ column vector with $0$ mean and stardard deviation $0.1$\FORALL{$(s_p, o^{+}_{p}, o^{-}_{p}) \in D_{p}$}\STATE Update $U_{s}^{p}$ based on Equation~\ref{eq:sgd1}\STATE Update $V_{o^{+}}^{p}$ based on Equation~\ref{eq:sgd2}\STATE Update $V_{o^{-}}^{p}$ based on Equation~\ref{eq:sgd3}\STATE Update $b_{o^{+}}^{p}$ based on Equation~\ref{eq:sgd4}\STATE Update $b_{o^{-}}^{p}$ based on Equation~\ref{eq:sgd5}\ENDFOR\STATE \textbf{return} $U^{p}$, $V^{p}$, $b^{p}$\end{algorithmic}\end{algorithm}重新定义require和ensure命令对应的关键字(此处将默认的Require/Ensure⾃定义为Input/Output)\renewcommand{\algorithmicrequire}{\textbf{Input:}}\renewcommand{\algorithmicensure}{\textbf{Output:}}。

Temporal RDFClaudio Gutierrez1,Carlos Hurtado1,and Alejandro Vaisman21Department of Computer ScienceUniversidad de Chile{cgutierr,churtado}@dcc.uchile.cl2Department of Computer ScienceUniversidad de Buenos Airesavaisman@dc.uba.arAbstract.The Resource Description Framework(RDF)is a metadatamodel and language recommended by the W3C.This paper presents aframework to incorporate temporal reasoning into RDF,yielding tem-poral RDF graphs.We present a semantics for temporal RDF graphs,asyntax to incorporate temporality into standard RDF graphs,an infer-ence system for temporal RDF graphs,complexity bounds showing thatentailment in temporal RDF graphs does not yield extra asymptoticcomplexity with respect to standard RDF graphs and sketch a temporalquery language for RDF.1IntroductionThe Resource Description Framework(RDF)[14]is a metadata model and lan-guage recommended by the W3C for building an infrastructure of machine-readable semantics for the data on the Web,a long-term vision known as Se-mantic Web.In the RDF model,the universe to be modeled is a set of resources, essentially anything that can have a universal resource identifier,URI.The lan-guage to describe them is a set of properties,technically binary predicates.De-scriptions are statements very much in the subject-predicate-object structure. Both subject and object can be anonymous objects,known as blank nodes.In addition,the RDF specification includes a built-in vocabulary with a norma-tive semantics(RDFS)[4].This vocabulary deals with inheritance of classes and properties,as well as typing,among other features allowing the descriptions of concepts and relationships that can exist for a community of people and software agents,enabling knowledge sharing and reuse.Although some studies exist about addressing changes in an ontology[15],or the need for temporal annotations on Web documents[22],little attention has deserved the problem of representing,updating and querying temporal informa-tion in RDF.Time is present in almost any Web application.Indeed,as pointed out by Abiteboul[1]the modeling of time is one of the key primitives needed in a query language for Web and semistructured data.Thus,there is a clear need of applying temporal database concepts to RDF to allow metadata navigation across time.Fig.1.Initial RDF graph(left)and after some changes(right).Consider an RDF graph describing information about a university,as of its creation time,Figure1(left).Students were classified as technical,graduate or undergraduate,and the only graduate programs offered were at the level of ‘Master’studies,like MBA or MSc;‘Professional Diploma’was the only program offered at the technical level.As the university evolved,a Ph.D program was created.Figure1(right)illustrates the new situation.Notice the dynamics of this example:students(e.g.,John)can enroll in one program(e.g.,Undergraduate), then shift to another one(e.g.,Master),and so on.Thefigures show that the impact of disregarding the time dimension is twofold:on the one hand,when a change occurs,a new metadata document must be created(and the current document dropped).On the other hand,queries asking for past states of the metadata cannot be supported.For instance,we cannot ask for the programs offered at the time when the university was created.1.1Problem Statement:Introducing Time into RDFGenerally speaking,a temporal database is a repository of temporal information. Although temporal databases were initially studied for adding the time dimen-sion to relational databases,as new data models emerged,temporal extensions to these models were also proposed(see Section1.2).We next discuss the main issues that arise when extending RDF with temporal information.Versioning vs.Time Labeling There are two mechanisms for adding the time dimension to non-temporal RDF graphs:labeling and versioning(following the timestamp and snapshot models,respectively).The former consists in labeling the elements subject to changes(i.e.triples).The latter is based on maintaining a snapshot of each state of the graph.For instance,each time a triple changes,a new version of the RDF graph is created,and the past state is stored somewhere. Although both models are equivalent,versioning appears to be not suitable for queries of the form:“all time instants whereΦholds in the database”.There are at least two temporal dimensions to consider when dealing with temporal databases:valid and transaction times.Valid time is the time when data is valid is the modeled world;transaction time is the time when data is actually stored in the database.The versioning approach captures transaction time,while labeling is mostly used when representing valid time.The approach we present in this paper supports both time dimensions.Fig.2.A temporal RDF graph accounting for the evolution of the university ontology.In summary,we believe that for RDF data,labeling is better than versioning, because(a)it preserves the spirit of the distributed and extensible nature of RDF,and(b)in scenarios where changes are frequent and only affecting a few elements of the document,creating a new physical version of the graph each time an update occurs may lead to large overheads when processing temporal queries that span multiple versions.Time Points vs.Time Intervals We will work with the point-based temporal domain for defining our data model and query language,but we will encode time-points in intervals when possible,for the sake of clarity.We will consider time as a discrete,linearly ordered domain,as usual in virtually all temporal database applications.An ordered pair[a,b]of time points,with a≤b,denotes the closed interval from a to b.Figure2shows a temporal RDF graph for the university example above.The arcs in the graph are labeled with their interval of validity.3 For example,the interval[0,Now]says that the triple(technical,sc,student)is valid from the document’s creation time to the current time.Also,note that the figure shows that John was an Undergraduate student in the interval[0,10],and now he is a PhD student.Vocabulary for Temporal Labeling Temporal labeling can be implemented within the RDF specification,making use of a simple additional vocabulary,as Figure3 shows.As we adopted the point-based,discrete and linearly ordered temporal domain,the left and right hand sides of Figure3are equivalent.We will use both representations indistinctly.Moreover,we define constructs that allow moving between intervals and time instants as follows:the instants depicted in Figure3 (left)can be encoded in an interval as shown in Figure3(right).Both alternatives will be used in the query language.3Note that the standard graph(ical)representation of an RDF graph is not the most faithful to convey the idea of statements(triples)being labeled by a temporal ele-ment.Technically,temporal labels should be attached to a whole subgraph u p→v, and not only to an arc.aFig.3.Point-based labeling(left)and Interval-based labeling(right). Temporal Entailment An RDF graph can be regarded as a knowledge base from which new knowledge,i.e.,other graphs,may be entailed.Entailment in a tem-poral setting is a slightly more involved in the RDF case than in the standard database case.In principle,one may be tempted to define the semantics as in temporal relational databases,i.e.,defining the temporal database as the union of all of its snapshots.(A snapshot at time t of a temporal RDF graph G is the corresponding subgraph formed by triples labeled by and instant t.)Blank nodes impose some constraints to this naive approach.For example,each of the three snapshots of Figure4(right)entails the corresponding snapshots of Figure4 (left).However,the whole graph of Figure4(left)cannot be entailed by the graph of Figure4(right).Indeed,the graph of Figure4(left)states that there is an anonymous object X in the triple(a,b,X)at times3and4,which is not the case for the other graph.Temporal Query Language Regarding query languages in temporal databases, basically two choices for defining the temporal domains exist:the point-based and the interval based temporal domains,yielding different query languages[20, 3].In the point-based approach,temporal variables in query languages refer to individual time instants,while in the interval-based domain,variables in the queries range over intervals,making queries more complicated and unnatural. Anyway,one can move easily between these two domains.1.2Related WorkThe RDF model was introducedfive years ago as a W3C recommendation[14]. Formal work in RDF includes the study of formal aspects of RDF data and query languages[10,21],considering RDF features like the entailment,presence of blank nodes,reification,premises in queries,and the RDFS vocabulary with predefined semantics.Several languages for querying RDF data have been pro-posed and implemented.Some of them in the lines of traditional database query languages(e.g.SQL,OQL),others based on logic and rule languages.Good sur-veys are[13,16].To the best of our knowledge,there is still no formal study of temporality issues in RDF graphs and RDF query languages.Temporal database management has been extensively studied,including data models,mostly based on the relational model and query languages[19],leading to the TSQL2language[18].Beyond the relational model,managing historicalFig.4.Temporal entailment:for each t the corresponding snapshots at t are equivalent, but the graph on the left is not entailed by the graph on the right. semistructured data wasfirst proposed by Chawathe et al[6],who extended the Object Exchange Model(OEM)with the ability to represent updates and to keep track of them by means of“deltas.”Later,Dyreson et al[7]allowed anno-tations on the edges of the database graph.In the XML world,Amagasa et al[2] introduced a temporal data model based on XPath for thefirst time.Dyreson[8] proposed an extension of XPath with support for transaction time by means of the addition of several temporal axes for specifying temporal directions,focus-ing on document versioning over the web in the absence of explicit time stamps. Chien et al[5]proposed update and versioning schemes for XML through an edit-based schema in which the most current version of the document is main-tained,and reverse edit scripts allow moving backward in time.Gao et al[9] introducedτXQuery,an extension to XQuery supporting valid time while main-taining the data model unchanged.Mendelzon et al[17]proposed a temporal model for XML,a temporal extension to XPath,and a novel indexing strategy for temporal XML documents.Like in our approach,they use labeling,and a point-based temporal domain and query language.Finally,Visser et al[22]pro-posed a temporal reasoning framework for the Semantic Web,which has been applied in BUSTER,an ontology-based prototype developed at the University of Bremen,supporting the so-called concept@location in time type of query.1.3ContributionsIn this paper we present a framework to incorporate temporal reasoning into RDF,yielding temporal RDF graphs.In particular,we present the following con-tributions:(a)a semantics for temporal RDF graphs in terms of the semantics of non-temporal RDF and RDFS graphs;(b)a study of properties of temporal RDF graphs and the interplay between timestamp and snapshot semantics in temporal RDF graphs;(c)a syntax to incorporate this framework into standard RDF graphs,which includes a vocabulary and rules.The syntax uses the stan-dard RDF vocabulary plus temporal labels;(d)a sound and complete inference system for temporal RDF graphs;(e)complexity bounds which show that entail-ment in temporal RDF graphs does not yield extra asymptotic time complexity with respect to standard RDF graphs;(f)a sketch for a temporal query language for RDF.For the sake of space,we do not include proofs in this version of the paper.2RDF PreliminariesIn this section we present a streamlined formalization of the RDF model following the W3C documents[14,12,4],along the lines of[10].2.1RDF Graphs.Assume there is an infinite set U(RDF URI references);an infinite set B= {N j:j∈N}(Blank nodes);and an infinite set L(RDF literals).A triple (v1,v2,v3)∈(U∪B)×U×(U∪B∪L)is called an RDF triple.In such a triple, v1is called the subject,v2the predicate and v3the object.We often denote by UBL the union of the sets U,B and L.An RDF graph(just graph from now on)is a set of RDF triples.A subgraph is a subset of a graph.The universe of a graph G,universe(G),is the set of elements of UBL that occur in the triples of G.The vocabulary of G is the set universe(G)∩(U∪L).We will use letters N,X,Y,...to denote blank nodes,and a,b,c,... for URIs and literals.A graph is ground if it has no blank nodes.Graphically we represent RDF graphs as follows:each triple(a,b,c)is represented by the labeled graph a b→c.Note that the set of arc labels can have non-empty intersection with the set of node labels.A map is a functionµ:UBL→UBL preserving URIs and literals,i.e.,µ(u)=u andµ(l)=l for all u∈U and l∈L.Given a graph G,we define µ(G)as the set of all(µ(s),µ(p),µ(o))such that(s,p,o)∈G.A mapµis consistent with G ifµ(G)is an RDF graph,i.e.,if s is the subject of a triple, thenµ(s)∈UB,and if p is the predicate of a triple,thenµ(p)∈U.In this case, we say that the graphµ(G)is an instance of the graph G.An instance of G is proper ifµ(G)has fewer blank nodes than G.This means that eitherµsends a blank node to an URI or a literal,or identifies two blank nodes of G.We will overload the meaning of map and speak of a mapµ:G1→G2if there is a map µsuch thatµ(G1)is a subgraph of G2.Two graphs G1,G2are isomorphic,denoted G1∼=G2,if there are maps µ1,µ2such thatµ1(G1)=G2andµ2(G2)=G1.We define two operations on graphs.The union of G1,G2,denoted G1∪G2, is the set theoretical union of their sets of triples.The merge of G1,G2,denoted G1+G2,is the union G1∪G 2,where G 2is an isomorphic copy of G2whose set of blank nodes is disjoint with that of G1.Note that G1+G2is unique up to isomorphism.2.2RDFS VocabularyThere is a set of reserved words defined in the RDF vocabulary description language,RDF Schema[4],–just rdfs-vocabulary for us–that may be used to de-scribe properties like attributes of resources(traditional attribute-value pairs),and also to represent relationships between resources.It defines classes and prop-erties that may be used for describing groups of related resources and relation-ships between resources.4Classes are sets of resources.Elements of a class are known as instances of that class.To state that a resource is an instance of a class, the property rdf:type may be used.The following are the most important classes (in brackets the name we will use in this paper)rdfs:Resource[res],rdfs:Class [class],rdfs:Literal[literal],rdfs:Datatype[datatype],rdf:XMLLiteral[xmlLit], rdf:Property[property].Properties are binary relations between subject re-sources and object resources.The built-in properties are:rdfs:range[range], rdfs:domain[dom],rdf:type[type],rdfs:subClassOf[sc],rdfs:subPropertyOf[sp].3Temporal RDF GraphsIn this paper we extend RDF graphs by allowing temporal elements to label triples.A temporal label is a temporal element t labeling a triple(a,b,c).For simplicity,without loss of generality,we will work with single intervals insteadof temporal elements.In an RDF graph,given a triple(a,b,c),the temporal element t represents the time period when the triple was valid,i.e.the valid time of the triple.At this time we do not deal with transaction time,which can be addressed in an analogous way.3.1Basic DefinitionsIn this section we define the notion of temporal RDF at a conceptual level.Definition1(Temporal graph).1.A temporal triple is an RDF triple with a temporal label(a natural number).We will use the notation(a,b,c):[t].The expression(a,b,c):[t1,t2]is a notation for{(a,b,c):[t]|t1≤t≤t2}.2.A temporal graph is a set of temporal triples.A subgraph is a subset of thegraph.For a temporal graph G,define the snapshot at time t as the RDF graphG(t)={(a,b,c)|(a,b,c):[t]∈G}The underlying RDF graph of a temporal RDF graph G,denoted u(G),istG(t),the union of the graphs G(t).For an RDF graph,define G t as the temporalization of all its triples by a temporal mark t,that is,G t={(a,b,c):[t]|(a,b,c)∈G}.4We omit in this paper vocabulary intended to describe lists,collections,some varia-tions on these,as well as vocabulary to help document and describe other function-alities for which there is no normative semantics.The complete vocabulary can be consulted in[4].The above definitions give the following elementary consequences about the relationship between RDF graphs and temporal RDF graphs.Lemma 1.Let G be an RDF graph,and G be a temporalRDF graph.Then:(1)G t (t )=G ;(2)(G (t ))t ⊆G ,and (3)G = t (G (t ))t .Several issues on the definition of temporal RDF graph are in order:–Recall we use a temporal model where an interval [a,b ]is of the form [a,a +1,...,b ]for a given unit of time that we will assume to be universal in this paper.The natural way to approach this issue is to specify,together with the temporal mark,the unit of time it represents.All the results given here extend without difficulties to this setting.–Temporal triples do not belong to the RDF syntax.In the next section we introduce an RDF-complying syntax for temporal triples,using a small temporal vocabulary.–Source of a temporal statement:Due to the extensible nature of the RDF model,it is possible to include the source of a temporal statement (i.e.who is the author of the temporal statement),and other properties that apply.Although our model (see next section)allows this,we will not study the semantic consequences of this extra information in this paper,but rather stay in the classic setting of temporal models.3.2SemanticsIn what follows,we present the semantics for the notion of entailment for tem-poral graphs based on the corresponding notion for RDF graphs.Definition 2(Temporal Entailment).Let G 1,G 2be RDF temporal graphs.Define–For ground temporal RDF graphs G 1,G 2define G 1|=t G 2as G 1(t )|=G 2(t )for each t ;–For general graphs,G 1|=t G 2iffthere exist ground instances µ1(G 1)and µ2(G 2)such that (µ1(G 1))(t )|=(µ2(G 2))(t )for each t .Note that the definition for ground graphs resembles classical temporal defi-nitions:Proposition 1.Let G 1,G 2be temporal graphs.Then,G 1|=t G 2implies G 1(t )|=G 2(t )for all t ,and the converse is true for ground graphs.In fact,the problems for general graphs are introduced by blank nodes and the notion of entailment.For example,G 1(t )|=G 2(t )for all t does not imply G 1|=t G 2(see Figure 4).We have the following issues:–A blank node represents the same (unnamed)resource throughout the time range,rather than a sequence of different resources.This makes the behavior of temporal marks in Temporal RDF different from the classical setting.Temporal marks here –contrary to temporal XML for example–are not only a relation among fixed objects ,but also among time-varying objects ,the blank nodes.See example in Figure 4.–The notion of entailment for temporal RDF needs a basic arithmetic of inter-vals in order to combine the notion of temporality and deductive properties.For example if we have (a,sc ,c ):[2,3],(c,sc ,d ):[2],then we should be able to derive (a,sc ,d ):[2],but not (a,sc ,d ):[3].In the rest of this section,we show that the notions of closure,lean graph,core –fundamental to define notions of normalization of this data–can be extended without difficulty to the temporal setting.(Compare discussion in [10]).The closure of a temporal graph G ,denoted tcl(G ),is a maximal set of temporal triples G over universe(G )plus the RDF vocabulary such that G contains G and is equivalent to it.Proposition 2(Entailment for Temporal graphs).Let G,G 1,G 2be temporal RDF graphs.Then 1.tcl(G )= t (cl(G (t )))t ;2.G 1|=t G 2if and only if tcl(G 1)|=t G 2.A temporal graph G is lean if and only if there is no proper temporal subgraph Gof G such that G |=t G .The core of G ,core(G )is a lean subgraph of G equivalent to it.With these notions,for a temporal RDF graph G we can define –as in the case of RDF graphs–a notion of normal form ,denoted by nf t (G ),as follows:nf t (G )=core t (G )for a temporal closure G of G .The computational complexities of computing the core and testing whether a graph is lean,are asymptotically the same as the case of standard RDF graphs [10].Proposition 3.Let G,G be graphs.1.The problem of deciding if G is the closure of G is DP-complete.2.The problem of deciding if G is the normal form of G is DP-complete.4Syntax and Deductive System for Temporal GraphsWe present a deductive system for temporal RDF.It is based on a sound and complete set of rules given in [12],plus three rules capturing temporal issues.4.1RDF syntax of temporal triplesDefinition 3(Temporal vocabulary).The temporal vocabulary is the follow-ing:temporal (abbreviated as tpl ),instant ,interval ,initial and final ,all of type property ,and now of type plain literal.The range of instant ,initial and final is the set of natural numbers.We will use the following notation shortcuts:reif(a,b,c,X):the set of triples (X,tsubj,a),(X,tpred,b),(X,tobj,c)(a kind of“temporal reification”of(a,b,c)).5 Definition4(Temporal triples and graphs).Temporal triples are the fol-lowing graphs using the temporal vocabulary.–(a,b,c),reif(a,b,c,X),(X,tpl,Y),(Y,instant,n)where n is a natural num-ber;we will summarize this as(a,b,c):[X,Y,n];–(a,b,c),reif(a,b,c,X),(X,tpl,Y),(Y,interval,Z),(Z,initial,I), (Z,final,F);where I,F are natural number;we will summarize this as (a,b,c):[X,Y,I,F];A temporal graph will be defined as a merge of a set of temporal triples.Because RDF is extensible,nothing prevents the use of the blank nodes included in the definition as target or source of other properties beyond the temporal vocabulary.We want to have a definition of temporal triple independent of the blank nodes occurring in the proposed syntactic definition of temporal triples,e.g.,we want that(a,b,c):[X,Y,n]be essentially equivalent to(a,b,c): [n].Both previous issues are overcame in our syntax by adding certain rules, which regulate the temporal vocabulary.4.2RulesThe set of rules is arranged in four groups.Groups A,B,C,and D are intended to describe the classical RDFS semantics,and we follow the approach in[10]. We omit another group of rules that has to do with internal relationships of the RDF model itself and that we do not consider in this paper.The novelty here is Group T(temporal rules),whose main objective is to be able to standardize the interval version and the instant version as well as help defining“absolute”temporal marks.GROUP A(Existential)For a mapµ:G →G,GGGROUP B(Subproperty)(a,type,property),(a,sp,b)(b,sp,c),(a,sp,b)(x,a,y)GROUP C(Subclass)(a,type,class),(a,sc,b)(b,sc,c),(a,sc,b)(x,type,a)GROUP D(Typing)(a,dom,c)(x,a,y) (x,type,c)(a,range,d)(x,a,y) (y,type,d)5We could have used here the standard reification vocabulary of RDF.We chose not to in order to stress the fact that the notions presented in this paper are independent of any view one may have about the concept of reification in RDF.GROUP T(Temporal)(i2t){(X,tpl,Y),(Y,instant,n):n∈[t1,t2]} (X,tpl,Y),(Y,int,Z),(Z,initial,t1),(Z,final,t2)(t2i)(X,tpl,Y),(Y,int,Z),(Z,initial,t1),(Z,final,t2),n∈[t1,t2] (abs)(a,b,c):[X1,Y1,n1],(a,b,c):[X2,Y2,n2](a,b,c):[U,V,n1],(a,b,c):[U,V,n2],U,V freshRules(i2t)(interval to instants)and(t2i)are needed to standardize the interval version and the instant version,by making them equivalent.Rule(abs) essentially says that marks(instants)can be collected in a single node.This permits to concentrate on temporal marks independent of other contexts in which the variables involving temporal vocabulary are immersed.The definition behaves well in the sense of the following lemma.Lemma2. 1.There exist blanks X,Y such that G|=t(a,b,c):[X,Y,t1,t2] if and only if there exist blanks U,V such that∀j with t1≤j≤t2G|=t (a,b,c):[U,V,t j]2.There exist blanks X1,Y1,X2,Y2such that G|=t(a,b,c):[X1,Y1,t1]andG|=t(a,b,c):[X2,Y2,t2]if and only if there exist blanks U,V such that G|=t(a,b,c):[U,V,t1]and G|=t(a,b,c):[U,V,t2].For a temporal RDF graph G,define G∗as the RDF graph{(a,b,c): [X t,Y t,t]|(a,b,c):[t]∈G},where X t,Y t are free blank variables,different for each t.Conversely,for each RDF graph G with temporal vocabulary,define G∗as the temporal graph defined as{(a,b,c):[t]|∃X∃Y(a,b,c):[X,Y,t]∈G}. Theorem1. 1.Let G1,G2be temporal RDF graphs.Then G1|=t G2implies G∗1|=G∗2.2.Let G1,G2be RDF graphs with temporal vocabulary.Then G1|=G2implies(G1)∗|=t(G2)∗.3.Let G be a temporal RDF graph,and G an RDF graph with temporal vocab-ulary.Then(G∗)∗=G and G |=(G ∗)∗.Now we can show that the syntax introduced captures the semantics of tem-poral RDF.The following deductive system based on the rules presented,is sound and complete for entailment of RDF graphs with rdfs vocabulary.Definition5.Let G be a graph.For each rule r:AB above,define G r G∪µ(B)iffthere is a mapµ:A→G.Also define G s G if and only if G is a subgraph of G.Define G G if there is afinite sequence of graphs G1,...,G n such that(1) G=G1;(2)G =G n;and(3)for each i,either,G i r G i+1for some r,or G i s G i+1.The following theorem shows that one can give a syntactic characterization over RDF graphs with temporal vocabulary for entailment of temporal RDF graphs:Theorem2.For any pair of temporal RDF graphs G1,G2:G1|=t G2if and only if G∗1 G∗2.Note that that we cannot establish the theorem in its complete generality, namely,to prove that for RDF graphs G1,G2with temporal vocabulary,G1 G2 if and only if(G1)∗|=t(G2)∗.(Both graphs in Figure4have identical()∗-images but are not -equivalent.)The previous theorem permits to concentrate for the following sections in temporal RDF(instead of diving into syntactic issues).5Query languageIn this section we present query language for temporal RDF graphs,along with its semantics.We also present a brief study of the complexity of query processing.5.1The Query Language by ExampleWe will give theflavor of the query language using our running example,the database of Figure2.Let us begin with a simple query:“Find students who have taken a Master course between year2000and now and return them qualified by 21-century-student”.This query can be expressed as:(?X,type,21-century-student)←(?X,takes,?C):[?T],(?C,type,Master):[?T],2000≤?T,?T≤Now.This example query illustrates the need of a built-in arithmetic language in order to reason about time and intervals.Another important observation is that temporal queries may output non-temporal RDF graph,as the previous query does.For the query asking for a snapshot of the graph at t1,we have:(?X,?Y,?Z)←(?X,?Y,?Y):[t1].Now consider the query“Students taking Ph.D courses together,and the time instants when this occurred.”For simplicity we expressed this as a point-based query.The translation of the result into intervals is straightforward.(?X,together,?Y)[?T]←(?X,type,P h.D):[?T],(?Y,type,P h.D):[?T].Next,we give examples of queries that use temporal triples with intervals. The query“Time intervals when the IT Master was offered”can be expressed as follows:(X,interval,Y),(Y,initial,T i),(Y,final,T f)←(IT Master,sc,Prof.Master):[T i,T f].Observe that the previous query returns a set of intervals.In order to retrieve maximal intervals we need a more subtle query,since their computation do not follow from the temporal rules.For the query“Compute the maximal interval when the triple(a,b,c)holds”,we need aggregate operators MAX and MIN. (a,b,c):[?T1,?T2]←(a,b,c):[?T i,?T f],?T1=MIN(?T i),?T2=MAX(?T f)For a query asking for“Students applying for jobs at time t afterfinishing their Ph.D.program in no more than4years”,we have:(?X,apply,job)←(X,type,Ph.D): t i,t f ,t f−t i<4,t f<t.Here,the notation t i,t f stands that t i and t f match with the maximal interval for the corresponding triple computed with the query given above.5.2Semantics and ComplexityLet V be a set of variables(disjoint from UBLT).Individual variables will be denoted?X,?Y,?Z,etc.There is also a set of temporal variables V t⊂V.The query language we define is analogous to the one presented by Gutierrez et al.[11].A query is a temporal tableau,which is a pair(H,B∪A),where H and B are temporal RDF graphs with some elements of UBL replaced by variables in V,and with some elements of T replaced with variables in V t,B has no blank nodes and all the variables in H occur also in B.The set A has the usual arithmetic built-in predicates such as<,>,=,.over elements in V t and T.We adopt the usual notion of safe rule from Datalog to prevent operations on infinite predicates.A rule is safe if all its variables are limited.A variable is limited if one of the following hold:a variable appears as an argument in a non-built-in predicate of the body;the variable X appears in a subgoal X=t (or t=X),where t is a constant in T;or the variable X appears in a subgoal X=Y(or Y=X),where Y is limited.The semantics is the usual in these cases.Given a temporal tableau(H,B∪A) and a temporal RDF graph G,for each matching of the graph pattern B in G, pick up the values of the variables and check whether they satisfy the built-in predicates in A.If this is the case,construct a pre-answer,which is the graph resulting by substituting the values of the variables in the head.Finally,the answer of the query is the union of all pre-answers.We end this section by showing that the additional time dimension in our model does not play any relevant role in the complexity of query answering,that is,the query language preserves the tractability of answers.In order to do this, we consider the simpler problem of testing emptiness of the query answer set in the following forms:(1)Query complexity version:For afixed database D,given a query q,is q(D)non-empty?(2)Data complexity version:For afixed query q, given a database D,is q(D)non-empty?Theorem3.The evaluation problem is NP-complete for the query complexity version,and polynomial for the data complexity version.。

ELSEVIER PII: SOOlO-4485(97)00101-2 Computer-Aided Design, Vol. 30, No. 5, pp. 377-389, 1998 8 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0010~4485/98/$19.00+0.cm

Current research in the

conceptual design of mechanical products

Wynne Hsu* and Irene M Y Woon Decisions made at the conceptual design stage have significant influence on factors such as costs, performance, reliability, safety and environmental impact of a product. However, knowledge of all the design requirements and constraints during this early phase of a product’s life cycle is usually imprecise, approximate or unknown. Faced with such complexity, individual designers have restricted themselves to narrow, well-defined sub-tasks and as a result, progress in this area has been patchy and spasmodic. The purpose of this survey is to document the current state of research and development in this crucial design activity and in doing so, to

Inference is a good guess heuristics (based on logic, statistics etc.) to observations or by interpolating the next logical step in an intuited pattern. The conclusion drawn is also called an inference. The laws of valid inference are studied in the field of logic.Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference. Statistical inference allows for inference from quantitative data.Contents[hide]∙ 1 Accuracy of inductive inferences∙ 2 Examples of deductive inference∙ 3 Incorrect inference∙ 4 Automatic logical inferenceo 4.1 Example using Prologo 4.2 Use with the semantic webo 4.3 Bayesian statistics and probability logico 4.4 Nonmonotonic logic[1]∙ 5 See also∙ 6 References[edit] Accuracy of inductive inferencesThe process by which a conclusion is inferred from multiple observations is called inductive reasoning. The conclusion may be correct or incorrect, or correct to within a certain degree of accuracy, or correct in certain situations. Conclusions inferred from multiple observations may be tested by additional observations.[edit] Examples of deductive inferenceGreek philosophers defined a number of syllogisms, correct three part inferences, that can be used as building blocks for more complex reasoning. We begin with the most famous of them all:1.All men are mortal2.Socrates is a man3.Therefore, Socrates is mortal.The reader can check that the premises and conclusion are true, but Logic is concerned with inference: does the truth of the conclusion follow from that of the premises?The validity of an inference depends on the form of the inference. That is, the word "valid" does not refer to the truth of the premises or the conclusion, but rather to the form of the inference. An inference can be valid even if the parts are false, and can be invalid even if the parts are true. But a valid form with true premises will always have a true conclusion.For example, consider the form of the following symbological track:1.All apples are blue.2. A banana is an apple.3.Therefore, a banana is blue.For the conclusion to be necessarily true, the premises need to be true. Now we turn to an invalid form.1.All A are B.2. C is a B.3.Therefore, C is an A.To show that this form is invalid, we demonstrate how it can lead from true premises to a false conclusion.1.All apples are fruit. (True)2.Bananas are fruit. (True)3.Therefore, bananas are apples. (False)A valid argument with false premises may lead to a false conclusion:1.All fat people are Greek.2.John Lennon was fat.3.Therefore, John Lennon was Greek.When a valid argument is used to derive a false conclusion from false premises, the inference is valid because it follows the form of a correct inference.A valid argument can also be used to derive a true conclusion from false premises:1.All fat people are musicians2.John Lennon was fat3.Therefore, John Lennon was a musicianIn this case we have two false premises that imply a true conclusion. [edit] Incorrect inferenceAn incorrect inference is known as a fallacy. Philosophers who study informal logic have compiled large lists of them, and cognitive psychologists have documented many biases in human reasoning that favor incorrect reasoning.[edit] Automatic logical inferenceAI systems first provided automated logical inference and these were once extremely popular research topics, leading to industrial applications under the form of expert systems and later business rule engines.An inference system's job is to extend a knowledge base automatically. The knowledge base (KB) is a set of propositions that represent what the system knows about the world. Several techniques can be used by that system to extend KB by means of valid inferences. An additional requirement is that the conclusions the system arrives at are relevant to its task.[edit] Example using PrologProlog (for "Programming in Logic") is a programming language based on a subset of predicate calculus. Its main job is to check whether a certain proposition can be inferred from a KB (knowledge base) using an algorithm called backward chaining.Let us return to our Socrates syllogism. We enter into our Knowledge Base the following piece of code:mortal(X) :- man(X).man(socrates).( Here :-can be read as if. Generally, if P Q(if P then Q) then in Prolog we would code Q:-P (Q if P).)This states that all men are mortal and that Socrates is a man. Now we can ask the Prolog system about Socrates:?- mortal(socrates).(where ?- signifies a query: Can mortal(socrates). be deduced from the KB using the rules) gives the answer "Yes".On the other hand, asking the Prolog system the following:?- mortal(plato).gives the answer "No".This is because Prolog does not know anything about Plato, and hence defaults to any property about Plato being false (the so-called closed world assumption). Finally ?- mortal(X) (Is anything mortal) would result in "Yes" (and in some implementations: "Yes": X=socrates)Prolog can be used for vastly more complicated inference tasks. See the corresponding article for further examples.[edit] Use with the semantic webRecently automatic reasoners found in semantic web a new field of application. Being based upon first-order logic, knowledge expressed using one variant of OWL can be logically processed, i.e., inferences can be made upon it.[edit] Bayesian statistics and probability logicPhilosophers and scientists who follow the Bayesian framework for inference use the mathematical rules of probability to find this best explanation. The Bayesian view has a number of desirable features—one of them is that it embeds deductive (certain) logic as a subset (this prompts some writers to call Bayesian probability "probability logic", following E. T. Jaynes).Bayesians identify probabilities with degrees of beliefs, with certainly true propositions having probability 1, and certainly false propositions having probability 0. To say that "it's going to rain tomorrow" has a 0.9probability is to say that you consider the possibility of rain tomorrow as extremely likely.Through the rules of probability, the probability of a conclusion and of alternatives can be calculated. The best explanation is most often identified with the most probable (see Bayesian decision theory). A central rule of Bayesian inference is Bayes' theorem, which gave its name to the field.See Bayesian inference for examples.[edit] Nonmonotonic logic[1]A relation of inference is monotonic if the addition of premises does not undermine previously reached conclusions; otherwise the relation is nonmonotonic. Deductive inference, is monotonic: if a conclusion is reached on the basis of a certain set of premises, then that conclusion still holds if more premises are added.By contrast, everyday reasoning is mostly nonmonotonic because it involves risk: we jump to conclusions from deductively insufficient premises. We know when it is worth or even necessary (e.g. in medical diagnosis) to take the risk. Yet we are also aware that such inference is defeasible—that new information may undermine old conclusions. Various kinds of defeasible but remarkably successful inference have traditionally captured the attention of philosophers (theories of induction, Peirce’s theory of abduction, inference to the best explanation, etc.). More recently logicians have begun to approach the phenomenon from a formal point of view. The result is a large body of theories at the interface of philosophy, logic and artificial intelligence.[edit] See also∙Reasoningo Abductive reasoningo Deductive reasoningo Inductive reasoningo Retroductive reasoning ∙Analogy∙Axiom∙Bayesian inference∙Business rule ∙Fuzzy logic∙Immediateinference∙Inference engine∙Inquiry∙Logic∙Logic ofinformation∙Logical assertion∙Business rules engine ∙Expert system ∙Logical graph∙Nonmonotonic logic ∙Rule of inference ∙List of rules of inference∙Theorem∙Sherlock Holmes[edit] References1.^Fuhrmann, André. Nonmonotonic Logic.http://www.uni-konstanz.de/FuF/Philo/Philosophie/Fuhrmann/papers/nomoLog.pdf.∙Hacking, Ian (2000). An Introduction to Probability and Inductive Logic. Cambridge University Press. ISBN0521775019.∙Jaynes, Edwin Thompson (2003). Probability Theory: The Logic of Science. Cambridge University Press. ISBN0-521-59271-2./catalogue.asp?isbn=0521592712.∙McKay, David J.C. (2003). Information Theory, Inference, and Learning Algorithms. Cambridge University Press.ISBN0-521-64298-1./mackay/itila/book.html.∙Russell, Stuart J.; Norvig, Peter(2003), Artificial Intelligence:A Modern Approach (2nd ed.), Upper Saddle River, New Jersey:Prentice Hall, ISBN0-13-790395-2, /∙Tijms, Henk (2004). Understanding Probability. Cambridge University Press. ISBN0521701724.Retrieved from "/wiki/Inference"Categories: Logic and statistics | Sources of knowledge | Reasoning | Concepts in logic | Logical consequenceHidden categories: Logic articles needing expert attention | Articles needing expert attention from November 2008| All articles needing expert attention | Articles lacking in-text citations from April 2010 | All articles lacking in-text citationsPersonal tools∙Log in / create accountNamespaces∙Article∙DiscussionVariantsViews∙Read∙Edit∙View historyActionsSearchNavigation∙Main page∙Contents∙Featured content∙Current events∙Random article∙Donate to Wikipedia Interaction∙Help∙About Wikipedia∙Community portal∙Recent changes∙Contact Wikipedia Toolbox∙What links here∙Related changes∙Upload file∙Special pages∙Permanent link∙Cite this pagePrint/export∙Create a book∙Download as PDF∙Printable version Languages∙ةيبرعلا∙Български∙Català∙Deutsch∙Español∙Esperanto∙Français∙한국어∙Hrvatski∙Italiano∙Nederlands∙日本語∙Polski∙Português∙Română∙Русский∙This page was last modified on 4 December 2010 at 00:02.∙Text is available under the Creative CommonsAttribution-ShareAlike License; additional terms may apply. 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Inference is the act of drawing a conclusion by deductive reasoning from given facts. The conclusion drawn is also called an inference. The laws of valid inference are studied in the field of logic.Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference. Statistical inference allows for inference from quantitative data.Contents[hide]∙ 1 Accuracy of inductive inferences∙ 2 Examples of deductive inference∙ 3 Incorrect inference∙ 4 Automatic logical inferenceo 4.1 Example using Prologo 4.2 Use with the semantic webo 4.3 Bayesian statistics and probability logico 4.4 Nonmonotonic logic[1]∙ 5 See also∙ 6 References[edit] Accuracy of inductive inferencesThe process by which a conclusion is inferred from multiple observations is called inductive reasoning. The conclusion may be correct or incorrect, or correct to within a certain degree of accuracy, or correct in certain situations. Conclusions inferred from multiple observations may be tested by additional observations.[edit] Examples of deductive inferenceGreek philosophers defined a number of syllogisms, correct three part inferences, that can be used as building blocks for more complex reasoning. We begin with the most famous of them all:1.All men are mortal2.Socrates is a man3.Therefore, Socrates is mortal.The reader can check that the premises and conclusion are true, but Logic is concerned with inference: does the truth of the conclusion follow from that of the premises?The validity of an inference depends on the form of the inference. That is, the word "valid" does not refer to the truth of the premises or the conclusion, but rather to the form of the inference. An inference can be valid even if the parts are false, and can be invalid even if the parts are true. But a valid form with true premises will always have a true conclusion.For example, consider the form of the following symbological track:1.All apples are blue.2. A banana is an apple.3.Therefore, a banana is blue.For the conclusion to be necessarily true, the premises need to be true.Now we turn to an invalid form.1.All A are B.2. C is a B.3.Therefore, C is an A.To show that this form is invalid, we demonstrate how it can lead from true premises to a false conclusion.1.All apples are fruit. (True)2.Bananas are fruit. (True)3.Therefore, bananas are apples. (False)A valid argument with false premises may lead to a false conclusion:1.All fat people are Greek.2.John Lennon was fat.3.Therefore, John Lennon was Greek.When a valid argument is used to derive a false conclusion from false premises, the inference is valid because it follows the form of a correct inference.A valid argument can also be used to derive a true conclusion from false premises:1.All fat people are musicians2.John Lennon was fat3.Therefore, John Lennon was a musicianIn this case we have two false premises that imply a true conclusion.[edit] Incorrect inferenceAn incorrect inference is known as a fallacy. Philosophers who study informal logic have compiled large lists of them, and cognitive psychologists have documented many biases in human reasoning that favor incorrect reasoning.[edit] Automatic logical inferenceAI systems first provided automated logical inference and these were once extremely popular research topics, leading to industrial applications under the form of expert systems and later business rule engines.An inference system's job is to extend a knowledge base automatically. The knowledge base (KB) is a set of propositions that represent what the system knows about the world. Several techniques can be used by that system to extend KB by means of valid inferences. An additional requirement is that the conclusions the system arrives at are relevant to its task.[edit] Example using PrologProlog (for "Programming in Logic") is a programming language based on a subset of predicate calculus. Its main job is to check whether a certain proposition can be inferred from a KB (knowledge base) using an algorithm called backward chaining.Let us return to our Socrates syllogism. We enter into our Knowledge Base the following piece of code:mortal(X) :- man(X).man(socrates).( Here :-can be read as if. Generally, if P Q(if P then Q) then in Prolog we would code Q:-P (Q if P).)This states that all men are mortal and that Socrates is a man. Now we can ask the Prolog system about Socrates:?- mortal(socrates).(where ?- signifies a query: Can mortal(socrates). be deduced from the KB using the rules) gives the answer "Yes".On the other hand, asking the Prolog system the following:?- mortal(plato).gives the answer "No".This is because Prolog does not know anything about Plato, and hence defaults to any property about Plato being false (the so-called closed world assumption). Finally ?- mortal(X) (Is anything mortal) would result in "Yes" (and in some implementations: "Yes": X=socrates)Prolog can be used for vastly more complicated inference tasks. See the corresponding article for further examples.[edit] Use with the semantic webRecently automatic reasoners found in semantic web a new field of application. Being based upon first-order logic, knowledge expressed using one variant of OWL can be logically processed, i.e., inferences can be made upon it.[edit] Bayesian statistics and probability logicPhilosophers and scientists who follow the Bayesian framework for inference use the mathematical rules of probability to find this best explanation. The Bayesian view has a number of desirable features—one of them is that it embeds deductive (certain) logic as a subset (this prompts some writers to call Bayesian probability "probability logic", following E. T. Jaynes).Bayesians identify probabilities with degrees of beliefs, with certainly true propositions having probability 1, and certainly false propositions having probability 0. To say that "it's going to rain tomorrow" has a 0.9 probability is to say that you consider the possibility of rain tomorrow as extremely likely.Through the rules of probability, the probability of a conclusion and of alternatives can be calculated. The best explanation is most often identified with the most probable (see Bayesian decision theory). Acentral rule of Bayesian inference is Bayes' theorem, which gave its name to the field.See Bayesian inference for examples.[edit] Nonmonotonic logic[1]A relation of inference is monotonic if the addition of premises does not undermine previously reached conclusions; otherwise the relation is nonmonotonic. Deductive inference, is monotonic: if a conclusion is reached on the basis of a certain set of premises, then that conclusion still holds if more premises are added.By contrast, everyday reasoning is mostly nonmonotonic because it involves risk: we jump to conclusions from deductively insufficient premises. We know when it is worth or even necessary (e.g. in medical diagnosis) to take the risk. Yet we are also aware that such inference is defeasible—that new information may undermine old conclusions. Various kinds of defeasible but remarkably successful inference have traditionally captured the attention of philosophers (theories of induction, Peirce’s theory of abduction, inference to the best explanation, etc.). More recently logicians have begun to approach the phenomenon from a formal point of view. The result is a large body of theories at the interface of philosophy, logic and artificial intelligence.[edit] See also∙Reasoningo Abductive reasoningo Deductive reasoningo Inductive reasoningo Retroductive reasoning ∙Entailment∙Analogy∙Axiom∙Bayesian inference∙Business rule∙Business rules engine ∙Fuzzy logic∙Immediate inference∙Inference engine∙Inquiry∙Logic∙Logic of information∙Logical assertion∙Logical graph∙Nonmonotonic logic∙Rule of inference∙List of rules of inference ∙Theorem∙Expert system∙Sherlock Holmes。

《离散数学》双语专业词汇表set：集合subset：子集element, member：成员，元素well-defined：良定，完全确定brace：花括号representation：表示sensible：有意义的rational number：有理数empty set：空集Venn diagram：文氏图contain(in)：包含（于）universal set：全集finite (infinite) set：有限（无限）集cardinality：基数，势power set：幂集operation on sets：集合运算disjoint sets：不相交集intersection：交union：并complement of B with respect to A：A与B的差集symmetric difference：对称差commutative：可交换的associative：可结合的distributive：可分配的idempotent：等幂的de Morgan’s laws：德摩根律inclusion-exclusion principle：容斥原理sequence：序列subscript：下标recursive：递归explicit：显式的string：串，字符串set corresponding to a sequence：对应于序列的集合linear array(list)：线性表characteristic function：特征函数countable(uncountable)：可数（不可数）alphabet：字母表word：词empty sequence(string)：空串catenation：合并，拼接regular expression：正则表达式division：除法multiple：倍数prime：素（数）algorithm：算法common divisor：公因子GCD(greatest common divisor)：最大公因子LCM(least common multiple)：最小公倍数Euclidian algorithm：欧几里得算法，辗转相除法pseudocode：伪码（拟码）matrix：矩阵square matrix：方阵row：行column：列entry(element)：元素diagonal matrix：对角阵Boolean matrix：布尔矩阵join：并meet：交Boolean product：布尔乘积mathematical structure(system)：数学结构（系统）closed with respect to：对…是封闭的binary operation：二元运算unary operation：一元运算identity：么元，单位元inverse：逆元statement, proposition：命题logical connective：命题联结词compound statement：复合命题propositional variable：命题变元negation：否定（式）truth table：真值表conjunction：合取disjunction：析取quantifier：量词universal quantification：全称量词化propositional function：命题公式predicate：谓词existential quantification：存在量词化converse：逆命题conditional statement, implication：条件式，蕴涵式consequent, conclusion：结论，后件contrapositive：逆否命题hypothesis：假设，前提，前件biconditional, equivalence：双条件式，等价logically equivalent：（逻辑）等价的contingency：可满足式tautology：永真(重言)式contradiction, absurdity：永假(矛盾)式logically follow：是…的逻辑结论rules of reference：推理规则modus ponens：肯定律m odus tollens：否定律indirect method：间接证明法proof by contradiction：反证法counterexample；反例basic step：基础步principle of mathematical induction：（第一）数学归纳法induction step：归纳步strong induction：第二数学归纳法relation：关系digraph：有向图ordered pair：有序对，序偶product set, Caretesian set：叉积，笛partition, quotient set：划分，商集block, cell：划分块，单元domain：定义域range：值域R-relative set：R相关集vertex(vertices)：结点，顶点edge：边in-degree：入度out-degree：出度path：通路，路径cycle：回路connectivity relation：连通性关系reachability relation：可达性关系composition：复合reflexive：自反的irreflexive：反自反的empty relation：空关系symmetric：对称的asymmetric：非对称的antisymmetric：反对称的graph：无向图undirected edge：无向边adjacent vertices：邻接结点connected：连通的transitive：传递的equivalent relation：等价关系congruent to：与…同余modulus：模equivalence class：等价类linked list：链表storage cell：存储单元pointer：指针complementary relation：补关系inverse：逆关系closure：闭包symmetric closure：对称闭包reflexive closure：自反闭包composition：关系的复合transitive closure：传递闭包Warshal’s algorithm：Warshall算法function, mapping, transformation：函数，映射，变换argument：自变量value, image：值，像，应变量labeled digraph：标记有向图identity function on A：A上的恒等函数everywhere defined：处处有定义的onto：到上函数，满射one to one：单射，一对一函数bijection, one-to-one correspondence：双射，一一对应invertible function：可逆函数floor function：下取整函数ceiling function：上取整函数Boolean function：布尔函数base 2 exponential function：以2为底的指数函数logarithm function to the base n：以n为底的对数hashing function：杂凑函数key：键growth of function：函数增长same order：同阶lower order：低阶running time：运行时间permutation：置换，排列cyclic permutation：循环置换，轮换transposition：对换odd(even) permutation：奇（偶）置换order relation：序关系partial order：偏序关系partially ordered set, poset：偏序集dual：对偶comparable：可比较的linear order(total order)：线序，全序linearly ordered set, chain：线（全）序集，链product partial order：积偏序lexicographic order：字典序Hasse diagram：哈斯图topological sorting：拓扑排序isomorphism：同构maximal(minimal) element：极大(小)元extremal element：极值元素greatest(least) element：最大(小)元unit element：么(单位)元zero element：零元upper(lower) bound：上(下)界least upper(greatest lower) bound：上(下)确界lattice：格join：，保联，并meet：保交，交sublattice：子格absorption property：吸收律bounded lattice：有界格distributive lattice：分配格complement：补元modular lattice：模格Boolean algebra：布尔代数involution property：对合律Boolean polynomial, Boolean expression：布尔多项式（表达式）or(and, not) gate：或(与，非)门inverter：反向器circuit design：线路设计minterm：极小项Karnaugh map：卡诺图tree：树root：根，根结点rooted tree：（有）根树level：层，parent：父结点offspring：子女结点siblings：兄弟结点height：树高leaf(leave)：叶结点ordered tree：有序树n-tree：n-元树complete n-tree：完全n-元树(complete) binary tree：(完全)二元(叉)树descendant：后代subtree：子树positional tree：位置树positional binary tree：位置二元(叉)树doubly linked list：双向链表tree searching：树的搜索（遍历）traverse：遍历，周游preorder search：前序遍历Polish form：（表达式的）波兰表示inorder search：中序遍历postorder search：后序遍历reverse Polish form：（表达式的）逆波兰表示linked-list representation：链表表示undirected tree：无向树undirected edge：无向边adjacent vertices：邻接结点simple path：简单路径（通路）simple cycle：简单回路acyclic：无（简单）回路的spanning tree：生成树，支撑树Prim’s algorithm：Prim算法minimal spanning tree：最小生成树weighted graph：（赋）权图weight：树distance：距离nearest neighbor：最邻近结点greedy algorithm：贪婪算法optimal solution：最佳方法Kruskal’s algorithm：Kruskal算法graph：(无向)图vertex(vertices)：结点edge：边end point：端点relationship：关系connection：连接degree of a vertex：结点的度loop：自回路path：路径isolated vertex：孤立结点adjacent vertices：邻接结点circuit：回路simple path(circuit)：基本路径(回路) connected：连通的disconnected：不连通的component：分图discrete graph(null graph)：零图complete graph：完全图regular graph：正规图，正则图linear graph：线性图subgraph：子图Euler path(circuit)：欧拉路径(回路) Konisberg Bridge problem：哥尼斯堡七桥问题ordinance：法规recycle：回收，再循环bridge：桥，割边Hamiltonian path(circuit)：哈密尔顿路径(回路)dodecahedron：正十二面体weight：权TSP(traveling salesperson problem)：货郎担问题transport network：运输网络capacity：容量maximum flow：最大流source：源sink：汇conversation of flow：流的守恒value of a flow：流的值excess capacity：增值容量cut：割the capacity of a cut：割的容量matching problems：匹配问题matching function：匹配函数compatible with：与…相容maximal match：最大匹配complete match：完全匹配coloring graphs：图的着色proper coloring：正规着色chromatic number of G：G的色数map-coloring problem：地图着色问题conjecture：猜想planar graph：(可)平面图bland meats：未加调料的肉chromatic polynomial：着色多项式binary operation on a set A：集合A上的二元运算closed under the operation：运算对…是封闭的commutative：可交换的associative：可结合的idempotent：幂等的distributive：可分配的semigroup：半群product：积free semigroup generated by A：由A生成的自由半群identity(element)：么（单位）元monoid：含么半群，独异点subsemigroup：子半群submonoid：子含么半群isomorphism：同构homomorphism：同态homomorphic image：同态像Kernel：同态核congruence relation：同余关系natural homomorphism：自然同态group：群inverse：逆元quotient group：商群Abelian group：交换（阿贝尔）群cancellation property：消去律multiplication table：运算表finite group：有限（阶）群order of a group：群的阶symmetric group：对称群subgroup：子群alternating group：交替群Klein 4 group：Klein四元群coset：陪集(left) right coset：(左)右陪集normal subgroup：正规(不变)子群prerequisite：预备知识virtually：几乎informal brand：不严格的那种notation：标记sensible：有意义的logician：逻辑学家extensively：广泛地，全面地commuter：经常往来于两地的人by convention：按常规，按惯例dimension：维(数) compatible：相容的discipline：学科reasoning：推理declarative sentence：陈述句n-tuple：n-元组component sentence：分句tacitly：默认generic element：任一元素algorithm verification：算法证明counting：计数factorial：阶乘combination：组合pigeonhole principle：鸽巢原理existence proof：存在性证明constructive proof：构造性证明category：类别，分类factor：因子consecutively：相继地probability(theory)：概率(论) die：骰子probabilistic：概率性的sample space：样本空间event：事件certain event：必然事件impossible event：不可能事件mutually exclusive：互斥的，不相交的likelihood：可能性frequency of occurrence：出现次数(频率) summarize：总结，概括plausible：似乎可能的equally likely：等可能的，等概率的random selection(choose an object at random)：随机选择terminology：术语expected value：期望值backtracking：回溯characteristic equation：特征方程linear homogeneous relation of degree k：k阶线性齐次关系binary relation：二元关系prescribe：命令，规定coordinate：坐标criteria：标准，准则gender：性别graduate school：研究生院generalize：推广notion：概念intuitively：直觉地verbally：用言语approach：方法，方式conversely：相反地pictorially：以图形方式restriction：限制direct flight：直飞航班tedious：冗长乏味的main diagonal：主对角线remainder：余数random access：随机访问sequential access：顺序访问custom：惯例polynomial：多项式substitution：替换multi-valued function：多值函数collision：冲突analysis of algorithm：算法分析sophisticated：复杂的set inclusion(containment)：集合包含distinguish：区分analogous：类似的ordered triple：有序三元组recreational area：游乐场所multigraph：多重图pumping station：抽水站depot：货站，仓库relay station：转送站。

《×××××》教学大纲（如是双语或全英课程应提供中文和英文教学大纲）总学时：理论课学时：实验课学时：学分：一、课程的性质二、课程的目的与教学基本要求三、课程适用专业四、课程的教学内容、要求与学时分配（一）1.（1）a.1．理论教学部分按各章节列出主要内容，注明课程教学的难点和重点，对学生掌握知识的要求，以及学时的分配。

2．实验教学部分列出实验名称（实验项目）、学时分配，明确具体内容，对学生的要求等。

五、教材和主要参考资料六、课程考核方式备注：（如果有要求先修课程请在此处列出）《×××××》Course DescriptionTotal Hours：Lecture Hours：Experimental Hours：1.Characteristic of the Course“必修课”翻译为：Compulsory Course“选修课”翻译为：Elective Course2.Aim of the Course and the Basic Requirements3.Applicable Speciality4.Course content, Requirements and Hours Allocation（1）a.(1) Lecture按各章节列出主要内容，注明课程教学的难点和重点，对学生掌握知识的要求，以及学时的分配。

(2) Experiment列出实验名称（实验项目）、学时分配，明确具体内容，对学生的要求等。

5.Textbooks and Main Reference6.Evaluation MethodNotes：（如果有要求先修课程请在此处列出）示例：《线性代数》教学大纲总学时：32 学分：2一、课程的性质公共基础必修课程。

二、课程的目的与教学基本要求线性代数是高等学校本科生重要的公共基础课之一，通过这门课的学习，使学生获得比较全面的线性代数的基本知识和必要的基本运算技能；同时培养学生在运用数学理论分析问题和解决问题的能力，增加数学软件的学习，为学习有关专业基础知识和专业课程及扩大数学知识方面提供必要的数学基础。

英语专业研究生试卷班级__________ 学生名字__________ 分数______《英语专业研究生英语语言文学基础试卷》一、单项选择题（每题2分，共30分）1. Which of the following is NOT a feature of Old English?A. Rich inflectional systemB. Use of the runic alphabetC. Mostly monosyllabic wordsD. Influence from Latin2. The term “metaphor” refers to ________.A. a figure of speech that makes a comparison between two unlike things without using “like” or “as”B. a repetition of initial consonant sounds in neighboring wordsC. a word or phrase that has lost its original meaningD. a story or narrative used to illustrate a moral or religious lesson3. In the sentence “I saw the man with a telescope.”, the prepositional phrase “with a telescope” is ________.A. an adverbial modifierB. an attributive modifierC. a complementD. a part of the predicate4. Which of the following playwrights is associated with the Elizabethan theater?A. Christopher MarloweB. Samuel BeckettC. Henrik IbsenD. Anton Chekhov5. The study of the origin and history of words is called ________.A. semanticsB. morphologyC. etymologyD. syntax6. “To be or not to be, that is the question.” is from which of Shakespeare’s plays?A. HamletB. Romeo and JulietC. MacbethD. Othello7. In English phonetics, the sound /θ/ is produced by ________.A. placing the tip of the tongue between the teethB. vibrating the vocal cords while the tongue is in a certain positionC. raising the back of the tongue towards the soft palateD. rounding the lips and forcing air through a narrow opening8. Which of the following is an example of a compound word?A. beautifulB. blackboardC. runningD. teacher9. The concept of “linguistic relativity” was proposed by ________.A. Noam ChomskyB. Ferdinand de SaussureC. Benjamin Lee WhorfD. Edward Sapir10. A sonnet is a poem that usually consists of ________ lines.A. 12B. 14C. 16D. 1811. Which of the following is a modal verb?A. runB. canC. happyD. book12. In the English language, the stress pattern in the word “photograph” is ________.A. 'photographB. pho'tographC. photo'graphD. pho to'graph13. Which of the following languages has the most influence on the development of Modern English?A. FrenchB. GermanC. GreekD. Latin14. The sentence “She made the boy clean his room.” is an example of ________.A. a simple sentenceB. a compound sentenceC. a complex sentenceD. a catenative construction15. Which of the following literary movements emphasized individualism and the beauty of nature?A. RomanticismB. RealismC. ModernismD. Post - modernism二、阅读理解（每题3分，共30分）Read the following passage and answer the questions:The history of the English language really started with the arrival of three Germanic tribes who invaded Britain during the 5th century AD. These tribes, the Angles, the Saxons and the Jutes, crossed the North Sea from what today is Denmark and northern Germany. At that time the inhabitants of Britain spoke a Celtic language. But most of the Celtic speakers were pushed west and north by the invaders - mainly into what is now Wales, Scotland and Ireland.The Angles came from "Englaland" and their language was called "Englisc" - from which the words "England" and "English" are derived. The invading Germanic tribes spoke similar languages, which in Britain developed into what we now call Old English. Old English did not sound or look like English today. Native English speakers now would have great difficulty understanding Old English. Nevertheless, about half of the most commonly used words in Modern English have Old English roots.Old English was a highly inflected language. This means that words changed their endings depending on their function in the sentence. For example, the word for "stone" was "stan" in the nominative case (when it was the subject of the sentence), but "stane" in the dative case (when it was the indirect object).Over time, the English language has changed a great deal. One of the major influences on English was the Norman Conquest in 1066. The Normans were from France and they brought with them the French language. For a long time, French was the language of the ruling class in England, while English was spoken by the common people. This led to the addition of a large number of French words to the English vocabulary.1. Who invaded Britain in the 5th century AD?2. What happened to the Celtic speakers when the Germanic tribes invaded?3. Where did the Angles come from?4. Why is Old English difficult for modern native English speakers to understand?5. What is meant by "Old English was a highly inflected language"?6. What was the major influence on English after the Norman Conquest in 1066?7. Who spoke French and who spoke English after the Norman Conquest?8. What is the origin of the words "England" and "English"?9. How has the English language changed over time?10. Give an example of how words changed in Old English depending on their function in the sentence.三、简答题（每题10分，共30分）1. Briefly explain the difference between a simile and a metaphor.2. Describe the main features of the Romantic literary movement.3. Explain how language acquisition in children occurs according to one of the major theories.四、Essay Question (10分)Discuss the importance of English as a global language.答案与解析：一、单项选择题1. C. Old English had a rich inflectional system, used the runic alphabet initially and was influenced by Latin. It had many polysyllabic words as well as monosyllabic ones.2. A. A metaphor is a figure of speech making a comparison without using “like” or “as”. B is allite ration, C is semantic change, D is parable.3. A. “with a telescope” in this sentence modifies the verb “saw” and tells how the seeing was done, so it is an adverbial modifier.4. A. Christopher Marlowe was an Elizabethan playwright. Beckett is a modernist, Ibsen is Norwegian and Chekhov is Russian.5. C. Etymology is the study of word origins. Semantics is about meaning, morphology is about word structure and syntax is about sentence structure.6. A. This famous line is from Hamlet.7. A. The sound /θ/ is produced by placing the tip of the tongue between the teeth.8. B. “blackboard” is made up of two words “black” and “board” and isa compound word.9. C. Benjamin Lee Whorf proposed the concept of “linguistic relativity” along with Edward Sapir.10. B. A sonnet typically has 14 lines.11. B. “can” is a modal verb which is used to express ability, permission etc.12. A. In “photograph” the stress is on the first syllable 'photograph.13. A. French has had the most influence on the development of Modern English, especially after the Norman Conquest.14. D. A catenative construction involves a verb followed by another verb in a non - finite form, here “made” followed by “clean”.15. A. Romanticism emphasized individualism and the beauty of nature.二、阅读理解1. The Angles, the Saxons and the Jutes invaded Britain in the 5th century AD.2. Most of the Celtic speakers were pushed west and north by the invaders - mainly into what is now Wales, Scotland and Ireland.3. The Angles came from "Englaland".4. Old English did not sound or look like English today. It was highly inflected and had different word forms for different functions in a sentence.5. It means that words changed their endings depending on their function in the sentence. For example, “stan” for nominative and “stane” for dative case of the word for “stone”.6. The Norman Conquest in 1066 brought the French language which had a major influence on English. French words were added to the English vocabulary.7. After the Norman Conquest, the ruling class spoke French and the common people spoke English.8. The Angles came from "Englaland" and their language was called "Englisc", from which the words "England" and "English" are derived.9. It has changed through invasions like the Germanic tribes in the 5th century and the Norman Conquest in 1066. It has added words from other languages, and has changed in terms of grammar, for example from being highly inflected in Old English.10. As mentioned before, the word for “stone” was “stan” in the nominati ve case (when it was the subject of the sentence), but “stane” in the dative case (when it was the indirect object).三、简答题1. A simile is a figure of speech that makes a comparison between two different things using “like” or “as”, for example “My heart is like a singing bird”. A metaphor makes a comparison without using these words, for example “The road is a ribbon of moonlight”. The simile is more explicit in its comparison, while the metaphor is more implicit and often creates a more vivid and imaginative connection.2. The Romantic literary movement had several main features. It emphasized emotion over reason. Writers focused on the individual and the individual's experience of the world. There was a great love for nature, seeing it as a source of inspiration, beauty and spiritual renewal. It also had an interest in the supernatural, the past and the exotic. There was a celebration of the imagination and a rejection of some of the strict rules of neoclassical literature.3. According to the nativist theory proposed by Noam Chomsky, children are born with an innate language acquisition device (LAD). This LAD allows children to have an in - built knowledge of the basic principles of language. They are able to acquire language quickly because they have this pre - wired system. They are exposed to the language in their environment and use the LAD to make sense of the input, formulating rules and gradually developing their language skills. Another theory, the behaviorist theory, proposed by Skinner, believes that language acquisition is a result of conditioning. Children learn language by imitating thelanguage they hear around them and are rewarded when they use language correctly.四、Essay QuestionEnglish has become a global language for several important reasons. Firstly, the history of British colonialism spread the language all over the world. Britain had colonies in many parts of the globe and as a result, English was introduced and became an important language in these areas. For example, in India, English is still widely used in education, business and government.Secondly, the United States, which is a superpower, has English as its main language. American influence in the fields of business, entertainment (Hollywood movies, popular music), technology and academia has made English even more widespread. People all over the world need to learn English to access American products, services and knowledge.In the international business world, English is the lingua franca. Multinational companies use English as their common language for communication. This allows people from different countries with different native languages to communicate effectively. For example, a Japanese and a Brazilian can communicate in English in a business meeting.In the field of science and technology, most research papers are published in English. Scientists need to know English to keep up with thelatest research findings and to share their own work. It is also the language of international conferences and seminars.In the field of education, many universities around the world offer courses in English. Students who want to study abroad or get a more international education often need to have a good command of English.However, the dominance of English also has some drawbacks. It can lead to the marginalization of other languages and cultures. But overall, the importance of English as a global language cannot be underestimated as it has become an essential tool for international communication, trade, knowledge sharing and cultural exchange.建议：嗨，同学！咱先来说说这试卷哈。

自然语言处理及计算语言学相关术语中英对译表二自然语言处理及计算语言学相关术语中英对译表二 delimiter 定界符号 [定界符]denotation 外延denotic logic 符号逻辑dependency 依存关系dependency gram r 依存关系语法dependency relation 依存关系depth-first search 深度优先搜寻derivation 派生derivational bound morpheme 派生性附着语素descriptive gram r 描述型语法 [描写语法]descriptive linguistics 描述语言学 [描写语言学] desiderative 意愿的determiner 限定词deterministic algorithm 决定型算法 [确定性算法] deterministic finite state auto ton 决定型有限状态机deterministic parser 决定型语法剖析器 [确定性句法剖析程序] developmental psychology 开展心理学diachronic linguistics 历时语言学diacritic 附加符号dialectology 方言学dictionary database 辞典数据库 [词点数据库]dictionary entry 辞典条目digital pro ssing 数字处理 [数值处理] diglossia 双言digraph 二合字母diminutive 指小词diphone 双连音directed acyclic graph 有向非循环图disambiguation 消除歧义 [歧义消除] discourse 篇章discourse ysis 篇章分析 [言谈分析] discourse planning 篇章规划discourse representation theory 篇章表征理论 [言谈表示理论] discourse strategy 言谈策略discourse structure 言谈结构discrete 离散的disjunction 选言dissimilation 异化distributed 分布式的distributed cooperative reasoning 分布协调型推理distributed text parsing 分布式文本剖析disyllabic 双音节的ditransitive verb 双宾动词 [双宾语动词;双及物动词]divergen 扩散[分化]d-m (determiner-measure) construction 定量结构d-n (determiner-noun) construction 定名结构document retrieval system 文件检索系统 [文献检索系统] do in dependency 领域依存性 [领域依存关系]double insertion 交互中插double-base 双基downgrading 降级dummy 虚位duration 音长{ 学}/时段{语法学/语意学}dynamic programming 动态规划earley algorithm earley 算法echo 回声句egressive 呼气音ejective 紧喉音electronic dictionary 电子词典elementary string 根本字符串 [根本单词串] ellipsis 省略em algorithm em算法embedding 崁入emic 功能关系的empirici 经验论empty category principle 虚范畴原那么 [空范畴原理] empty word 虚词enclitics 后接成份end user 终端用户 [最终用户]endo ntric 同心的endophora 语境照应entailment 蕴涵entity 实体entropy 熵entry 条目episodic memory 情节性记忆epistemological work 认识论网络ergative verb 作格动词ergativity 作格性esperando 世界语etic 无功能关系etymology 词源学eventevent driven control 驱动型控制example-based chine translation 以例句为本的机器翻译excla tion 感慨exclusive disjunction 排它性逻辑“或”experien r case 经验者格expert system 专家系统extension 外延external argument 域外论元extraposition 移外变形 [外置转换]facility value 易度值feature 特征feature bundle 特征束feature co-ourren restriction 特征同现限制 [特性同现限制] feature instantiation 特征表达feature structure 特征结构 [特性结构]feature unification 特征连并 [特性合一]feedback 回馈felicity condition 妥适条件file structure 档案结构finite auto ton 有限状态机 [有限自动机]finite state 有限状态finite state morphology 有限状态构词法 [有限状态词法] finite-state auto ta 有限状态自动机finite-state language 有限状态语言finite-state chine 有限状态机finite-state transdu r 有限状态置换器flap 闪音flat 降音foreground infor tion 前景讯息 [前景信息]for l language theory 形式语言理论for l linguistics 形式语言学for l se ntics 形式语意学forward inferen 前向推理 [向前推理]forward-backward algorithm 前前后后算法frame 框架frame based knowledge representation 框架型知识表示frame theory 框架理论free morpheme 自由语素fregean principle fregean 原那么fricative 擦音f-structure 功能结构full text searching 全文检索function word 功能词functional gram r 功能语法functional programming 函数型程序设计 [函数型程序设计] functional senten perspective 功能句子观functional structure 功能结构functional unification 功能连并 [功能合一]functor 功能符fundamental frequency 基频garden path senten 花园路径句gb (gover ent and binding) 管辖约束geminate 重叠音gender 性generalized phrase structure gram r 概化词组结构语法 [广义短语结构语法]generative gram r 衍生语法generative linguistics 衍生语言学 [生成语言学]generic 泛指geic epistemology 发生认识论geive rker 属格标记genitive 属格gerund 动名词gover ent and binding theory 管辖约束理论gpsg (generalized phrase structure gram r) 概化词组结构语法[广义短语结构语法]gradability 可分级性gram r checker 文法检查器gram tical affix 语法词缀gram tical category 语法范畴gram tical function 语能gram tical inferen 文法推论gram tical relation 语法关系grapheme 字素haplology 类音删略head 中心语head driven phrase structure 中心语驱动词组结构 [中心词驱动词组结构]head feature convention 中心语特征继承原理 [中心词特性继承原理]head-driven phrase structure gram r 中心语驱动词组结构律heteronym 同形heuristic parsing 经验式句法剖析heuristics 经验知识hidden rkov model 隐式马可夫模型hierarchical structure 阶层结构 [层次结构]holophrase 单词句homograph 同形异义词homonym 同音异义词homophone 同音词homophony 同音异义homorganic 同部位音的horn clause horn 子句hpsg (head-driven phrase structure gram r) 中心语驱动词组结构语法hu n- chine inte 人机界面hypernym 上位词hypertext 超文件 [超文本]hyponym 下位词hypotactic 主从结构的ic (immediate constituent) 直接成份icg (infor tion-based case gram r) 讯息为本的格位语法idiom 成语 [熟语]idiosyncrasy 特异性illocutionary 施为性immediate constituent 直接成份imperative 祈使句implicative predicate 蕴含谓词implicature 含意indexical 标引的indirect object 间接宾语indirect speech act 间接言谈行动 [间接言语行为] indo-european language 印欧语言inductional inferen 归纳推理inferen chine 推理机器infinitive 不定词 [to 不定式]infix 中缀inflection/inflexion 屈折变化inflectional affix 屈折词缀infor tion extraction 信息撷取infor tion pro ssing 信息处理 [信息处理]infor tion retrieval 信息检索infor tion scien 信息科学 [信息科学; 情报科学] infor tion theory 信息论 [信息论]inherent feature 固有特征inherit 继承inheritan 继承inheritan hierarchy 继承阶层 [继承层次]inheritan of attribute 属性继承innateness position 语法天生假说insertion 中插inside-outside algorithm 里里外外算法instantiation 表达instrumental (case) 工具格integrated parser 集成句法剖析程序integrated theory of discourse ysis 篇章分析综合理论 [言谈分析综合理论]in igen intensive production 知识密集型生产intensifier 加强成分intensional logic 内含逻辑intensional se ntics 内涵语意学intensional type 内含类型interjection/excla tion 感慨词inter-level 中间成分interlingua 中介语言interlingual 中介语(的）interlocutor 对话者internalise 内化international phoic association (ipa) 国际学会inter 网际网络interpretive se ntics 诠释性语意学intonation 语调intonation unit (iu) 语调单位ipa (international phoic association) 国际学会ir (infor tion retrieval) 信息检索is-a relation is-a 关系isomorphi 同形现象iu (intonation unit) 语调单位junction 连接keyword in context 上下文中关键词[上下文内关键词] kinesics 体势学knowledge acquisition 知识习得knowledge base 知识库knowledge based chine translation 知识为本之机器翻译knowledge extraction 知识撷取 [知识题取]knowledge representation 知识表示kwic (keyword in context) 关键词前后文 [上下文内关键词] label 卷标labial 唇音labio-dental 唇齿音labio-velar 软颚唇音lad (language acquisition devi ) 语言习得装置lag 发声延迟language acquisition 语言习得language acquisition devi 语言习得装置language engineering 语言工程language generation 语言生成language intuition 语感language model 语言模型language technology 语言科技left-corner parsing 左角落剖析 [左角句法剖析] lem 词元lenis 弱辅音letter-to-phone 字转音lexeme 词汇单位lexical ambiguity 词汇歧义lexical category 词类lexical con ptual structure 词汇概念结构lexical entry 词项lexical entry selection standard 选词标准lexical integrity 词语完整性lexical se ntics 词汇语意学lexical-functional gram r 词汇功能语法lexicography 词典学lexicology 词汇学lexicon 词汇库 [词典;词库]lexis 词汇层lf (logical form) 逻辑形式lfg (lexical-functional gram r) 词汇功能语法liaison 连音linear bounded auto ton 线性有限自主机linear pre den 线性次序lingua franca 共通语linguistic decoding 语言译码linguistic unit 语言单位linked list 串行loan 外来语local 局部的locali 方位主义localizer 方位词locus model 轨迹模型locution 惯用语logic 逻辑logic array work 逻辑数组网络logic programming 逻辑程序设计 [逻辑程序设计] logical form 逻辑形式logical operator 逻辑算子 [逻辑算符]logic-based gram r 逻辑为本语法 [基于逻辑的语法] long term memory 记忆longest tch principle 最长匹配原那么 [最长一致法] lr (left-right) parsing lr 剖析chine dictionary 机器词典chine language 机器语言chine learning 机器学习chine translation 机器翻译chine-readable dictionary (mrd) 机读辞典crolinguistics 宏观语言学rkov chart 马可夫图the tical linguistics 数理语言学ximum entropy 最大熵m-d (modifier-head) construction 偏正结构mean length of utteran (mlu) 语句平均长度measure of infor tion 讯习测度 [信息测度] memory based 根据记忆的mental lexicon 心理词汇库mental model 心理模型mental pro ss 心理过程 [智力过程;智力处理] metalanguage 超语言metaphor 隐喻metaphorical extension 隐喻扩展metarule 律上律 [元规那么]metathesis 易位microlinguistics 微观语言学middle structure 中间式结构mini l pair 最小对mini list program 微言主义mlu (mean length of utteran ) 语句平均长度modal 情态词modal auxiliary 情态助动词modal logic 情态逻辑modifier 修饰语modular logic gram r 模块化逻辑语法modular parsing system 模块化句法剖析系统modularity 模块性(理论)module 模块monophthong 单元音monotonic 单调monotonicity 单调性montague gram r 蒙泰究语法 [蒙塔格语法] mood 语气morpheme 词素morphological affix 构词词缀morphological deposition 语素分解morphological pattern 词型morphological pro ssing 词素处理morphological rule 构词律 [词法规那么] morphological segmentation 语素切分morphology 构词学morphophonemics 词音学 [形态音位学;语素音位学] morphophonological rule 形态音位规那么morphosyntax 词句法motor theory 肌动理论movement 移位mrd ( chine-readable dictionary) 机读辞典模板,内容仅供参考。