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Abstract Similarity-Based Unification A Multi-Adjoint Approach

Similarity-Based Uni?cation:

A Multi-Adjoint Approach

Jes′u s Medina,Manuel Ojeda-Aciego1

Dept.Matem′a tica Aplicada.Universidad de M′a laga

{jmedina,aciego}@ctima.uma.es

Peter Vojt′aˇs2

Institute of Informatics.P.J.ˇSaf′a rik University

vojtas@kosice.upjs.sk

Abstract

The aim of this paper is to build a formal model for similarity-based fuzzy uni?cation in multi-adjoint logic programs.Speci?cally,a general framework of logic program-ming which allows the simultaneous use of di?erent implications in the rules and rather general connectives in the bodies is introduced,then a procedural semantics for this framework is presented,and an aproximative-completeness theorem proved. On this computational model,a similarity-based uni?cation approach is constructed by simply adding axioms of fuzzy similarities and using classical crisp uni?cation which provides a semantic framework for logic programming with di?erent notions of similarity.

Key words:Similarity,fuzzy uni?cation.

1Introduction

It is usual practice in mathematical logic for a formal model to have clearly de?ned its syntactical part(which deals with proofs)and its semantical part (dealing with truth and/or satisfaction).When applying logic to computer science,mainly in logic programming,a di?erent terminology is used,and we speak about the declarative part of the formal model(corresponding to truth 1Partially supported by Spanish DGI project BFM2000-1054-C02-02.

2Partially supported by Slovak project VEGA1/7557/20

Preprint submitted to Elsevier Science23April2002

and satisfaction)and the procedural part,more focused on algorithmic aspects of?nding proofs(automated deduction).

Uni?cation is an important part of procedural semantics for many formal models,because it helps to identify instantiations of di?erent statements,that is,to make them syntactically(letter by letter)equal;and uni?ed(identical) statements can be handled identically.In the classical case,on the one hand, there was no need to clearly specify the declarative and procedural parts of a formal model of uni?cation because,on the declarative part,there was the requirement of being syntactically equal(and hence equal also from the point of view of truth and satisfaction);on the other hand,the procedural part of classical uni?cation is well developed,namely,di?erent uni?cation algorithms have been studied.This is no longer case when considering fuzzy uni?cation, where both the declarative and the procedural part of a formal model of fuzzy uni?cation are needed.

We mention below some possible examples of problems from the real world, whose solution needs an adequate treatment of similarity,but one can imagine many other syntactical,linguistic and conceptual sources,see e.g.[9].For illustration purposes consider the following situations:

?Firstly,imagine a hotel name“Salaˇs”in Slovak,which can in Hungarian pronunciation can be written as“Sz′a l′a s”,in Polish“Sza l as”and in English (omitting the“check sign”from Slovak)as“Salas”.Does the query us-ing“Salaˇs”unify with a database fact about“Sza l as”?,do subgoals about “Sz′a l′a s”and“Salas”?t together?A common-sense intuition suggests that, without the knowledge of any additional fact,the system itself cannot glue together information about objects with di?erent syntactical form.So the main question is how to describe this additional information.?Another source of problems comes from the need of inter-operability,in which a client requires apparently homogeneous access to heterogeneous servers.This heterogeneity causes,for instance,that web users accessing these information sources usually require a multi-step process utilizing the intelligence of the end-user to navigate and to resolve heterogeneity by ap-plying several similarity criteria[7].

?A third interesting example comes again from internet,whose initial design was made as an initiative for connecting sites containing information stored for direct human processing,but its next generation(the semantic web)is aimed at storing machine-processable information.For instance,implement-ing search engines which use ontologies to?nd pages with words that are syntactically di?erent but semantically similar[3].

?Our?nal motivating example has to do with multimedia database queries.

A user might want to query a multimedia database system over several properties he/she is interested in.For instance,consider the case that one is searching for a movie clip that has a predominantly red scene with a loud

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noise in the sound track,this example comes from[5].There is likely to be a score giving the redness of the scene and a di?erent score giving the loudness of the sound,and these two scores have to be somehow aggregated to be able to obtain their similarity degree and provide an ordered list of results.

Several approaches have been proposed for dealing with these problems;in this paper we choose the way of including additional information about fuzzy similarities of di?erent objects and using axioms of equality to transfer prop-erties between these objects,our main aim being not to give practical advice on how to detect and handle similarities in practical applications,but to give a formal model for both the declarative and the procedural part of similarity-based fuzzy uni?cation.Here we stress the fact that,for fuzzy uni?cation, both a procedural and declarative semantics are needed,as opposed to the two valued case.

Our approach to fuzzy uni?cation is based on a theory of fuzzy logic pro-gramming with crisp unification constructed on the multi-adjoint framework recently introduced in[12,13].We recall de?nitions of declarative and proced-ural semantics of multi-adjoint logic programming and show how its soundness and completeness,and especially the?x-point theorem and the minimal model obtained by the iteration of the immediate consequences operator,can give a base for a sound and complete model of fuzzy uni?cation.The fact that this theory of fuzzy uni?cation is developed inside the realm of fuzzy logic programming is very important for later integration of fuzzy similarity-based uni?cation and fuzzy logic programming deduction.

The structure of the paper is as follows:in Sections2and3,a general theory of logic programming which allows the simultaneous use of di?erent implications in the rules and rather general connectives in the bodies can be constructed. Models of multi-adjoint logic programs are post?x-points of the immediate consequences operator,which is proved to be monotonic under very general hypotheses.The continuity of the immediate consequences operator is proved under the general assumption of continuity of all the operators in the pro-gram(but,possibly,implications).Later,in Section4,a procedural semantics for the general theory of multi-adjoint logic programming is presented,in the spirit of[13],and a quasi-completeness theorem is proved.In Section5,our similarity-based approach to uni?cation is introduced.Roughly speaking,we add axioms of fuzzy similarities and,using the computational model provided by the procedural semantics which uses classical crisp uni?cation,we provide a semantic framework for logic programming with di?erent notions of similarity. Finally,we give some comparisons with other approaches.It is worth to men-tion that the weak uni?cation algorithm introduced in[15]can be completely emulated by our similarity-based uni?cation model.

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2Multi-adjoint logic programming

The intuition behind multi-adjoint logic programs is as follows:Considering di?erent implication operators,such as L ukasiewicz,G¨o del or product implic-ation in the same logic program,naturally leads to the allowance of several adjoint pairs in the lattice of truth-values.This idea is used in[12]to introduce multi-adjoint logic programs as an extension of monotonic and residuated logic programs,presented in[2],so that it is possible to use a number of di?erent implications in the rules of our programs.Speci?cally,the language and se-mantics of de?nite logic programs are generalized in order to encompass more complex rules.

The semantical framework of multi-adjoint logic programs is based on the so-called multi-adjoint lattices.The semantic basis of the notion of consequence in generalized logic programs is that of adjoint pair,which allows that fairly general conjunctors and their adjoints are used as,for instance,in[17].

The concept of adjoint pair was?rstly introduced in a logical context by Pavelka[14],who interpreted the poset structure of the set of truth-values as a category,and the relation between the connectives of implication and conjunction as functors in this category.The result turned out to be another example of the well-known concept of adjunction,introduced by Kan in the general setting of category theory in1950.

De?nition1Let P, be a partially ordered set and(←,&)a pair of binary operations in P such that:

(1)Operation&is increasing in both arguments,i.e.if x1,x2,y∈P such

that x1 x2then(x1&y) (x2&y)and(y&x1) (y&x2);

(2)Operation←is increasing in the?rst argument(the consequent)and

decreasing in the second argument(the antecedent),i.e.if x1,x2,y∈P such that x1 x2then(x1←y) (x2←y)and(y←x2) (y←x1);

(3)Adjoint property.For any x,y,z∈P,we have that x (y←z)holds

if and only if(x&z) y holds.

Then we say that(←,&)forms an adjoint pair in P, .

The main point in the extension of the results in[2,17,18]to a more gen-eral setting,in which di?erent implications( L ukasiewicz,G¨o del,product)and several modus ponens-like inference rules are used,naturally leads to consid-ering several adjoint pairs in the residuated lattice,leading to what we call a multi-adjoint lattice.More formally,

De?nition2Let L, be a complete lattice.A multi-adjoint lattice L is a tuple(L, ,←1,&1,...,←n,&n)satisfying the following items:

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(1) L, is bounded,i.e.it has bottom(⊥)and top( )elements;

(2)(←i,&i)is an adjoint pair in L, for i=1,...,n;

(3) &i?=?&i =?for all?∈L for i=1,...,n.

Note that residuated lattices[4]are a special case of multi-adjoint lattice,in which only one adjoint pair is present,and the underlying lattice has monoidal structure wrt&and (in general we are assuming neither associativity nor commutativity of&).

From the point of view of expressiveness,it is convenient to allow extra op-erators to be involved with the operators in the multi-adjoint lattice.The structure which captures this possibility is that of a multi-adjoint algebra.

De?nition3Let?be a graded set containing operators←i and&i for i= 1,...,n and possibly some extra operators,and let L= L,I be an?-algebra whose carrier set L is a complete lattice under .

We say that L is a multi-adjoint?-algebra with respect to the pairs(←i,&i) for i=1,...,n if L=(L, ,I(←1),I(&1),...,I(←n),I(&n))is a multi-adjoint lattice.

In the following,as we will work with a?xed graded set?,the pre?x?-will be dropped and,when necessary,we will talk simply about algebras.

3Syntax and Semantics of Multi-Adjoint Programs

The de?nition of multi-adjoint logic program is given,as usual in fuzzy lo-gic programming,as a set of weighted rules and facts of a given?rst-order language F,constructed as an algebra.Note that we will be allowed to use di?erent implications in our rules.

De?nition4A multi-adjoint logic program is a set P of weighted rules of the form A←i B,? such that:

(1)The consequent of the implication,A,is an atom which is called the head.

(2)The antecedent of the implication,B,is called the body,and is a for-

mula built from atoms B1,...,B n(n≥0)by the use of conjunctors, disjunctors,and aggregators.

(3)The con?dence factor?is an element(a truth-value)of L.

As usual,facts are rules with body ,goals or queries are atoms intended as questions?A prompting the system.Free occurrences of variables in the program are assumed to be universally quanti?ed.

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De?nition 5An interpretation is a mapping from the Herbrand base,B P ,of the program P to the multi-adjoint lattice of truth-values L, .

By using the unique homomorphic extension,it is possible to extend uniquely the mapping I ,de?ned on B P ,to be de?ned on the set of all ground formu-las of the language,this extension will be denoted ?I

.The extension for the non-ground case is also straightforward,due to the fact that all our formulas are considered universally closed;this way,for a non-ground formula A ,the interpretation I is de?ned as follows:3

?I (A )=inf ξ

{?I (Aξ)|Aξis a ground instantiation of A }The ordering in L can be easily extended to the set of interpretations as usual,I 1 I 2i?I 1(A ) I 2(A )for all ground atom.The least interpretation maps every ground atomic formula to the least element ⊥of L .

De?nition 6An interpretation I satis?es a weighted rule A ←i B ,? ,if and only if ? ?I

(A ←i B ).An interpretation I is a model of a multi-adjoint logic program P i?all weighted rules in P are satis?ed by I .

Note that we will be working with two algebras,F for the formulas and L for its interpretations,to avoid the risk of confusion we will introduce a special notation to clarify which algebra an operator belongs to.Let ωbe an oper-ator symbol,its interpretation under L is denoted .ω(a dot on the operator),whereas ωitself will be interpreted in F .

Note the following equalities,where ξis ground

?I ((A ←i B )ξ)=?I (Aξ←i B ξ)=?I (Aξ).←i ?I (B ξ)=I (Aξ).←i ?I (B ξ)

where the evaluation of ?I

(B ξ)proceeds inductively as usual.Similarly,a fact A ←i ,? is satis?ed if

? ?I ((A ←i )) I ((A ←i )ξ)=I (A ).←i

now,by the adjoint property,this is equivalent to ?.&i I (A )and,by the third assumption in De?nition 2,this gives ? I (A ).

De?nition 7A pair (λ;θ)where λ∈L and θis a substitution,is a correct answer for a program P and a query ?A if for any model of P we have

λ inf ξ I (Aθξ)

=?I (Aθ)3

In the rest of the paper,we will always use the notation inf ξto denote the in?mum on the set of all ground instantiations of the corresponding formula.6

3.1Fix-point semantics

The immediate consequences operator,given by van Emden and Kowalski,can be generalized to the framework of multi-adjoint logic programs as follows: De?nition8Let P be a multi-adjoint logic program.The immediate con-sequences operator T P maps interpretations to interpretations and is de?ned in such a way that,given an interpretation I and a ground atom A,

T P(I)(A)=sup

?

.

&i?I(Bθ)| C←i B,? ∈P and A=Cθ

As it is usual in the logic programming framework,the semantics of a multi-adjoint logic program is de?ned as the least?x-point of T P.In[12],the mono-tonicity of T P,and its continuity(granted under continuity of all the operators in the body)were proved in the propositional case.These results about mono-tonicity and continuity of T P are extended to?rst-order multi-adjoint logic programs in the rest of the section.

Theorem9An interpretation I is a model of a multi-adjoint logic program P i?T P(I) I.

PROOF.Firstly,assume that I is a model for P,and let us prove that T P(I) I.

Let A be a ground atom,for all weighted rule C←i B,? matching with A (that is,there is a ground instantiation(C←i B)θsuch that A=Cθ)we have,by hypothesis,the following chain of(in)equalities:

? inf

ξ

{?I((C←i B)ξ)} ?I((C←i B)θ)=?I(Cθ←i Bθ)=?I(Cθ).←i?I(Bθ)

now,applying the adjoint property,we have?.

&i?I(Bθ) I(Cθ)=I(A)and

taking suprema on the corresponding set of substitutions we get

sup{?

.

&i?I(Bθ)| C←i B,? ∈P and A=Cθ} I(A)

If there is no rule with head A,then

T P(I)(A)=sup?=⊥ I(A)

We have also to prove the converse:if T P(I)(A) I(A)for all ground atom A, then I is a model for P.This is immediate,as for any weighted rule C←i B,?

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in P and ground instantiation ξfor C ←i B ;by de?nition of T P ,we have that ?&i ?I

(B ξ) T P (I )(Cξ).Now,by hypothesis,?&i ?I

(B ξ) I (Cξ)for all ground instantiation ξand,again by the adjoint property,? I (Cξ)←i ?I

(B ξ)=?I ((C ←i B )ξ)and ?nally,taking the in?mum:

? inf ξ

{?I ((C ←i B )ξ)}The monotonicity of the operator T P is given below:

Theorem 10The operator T P is monotonic.

PROOF.Consider I and J two interpretations such that I J ,and let us prove that T P (I ) T P (J ).

Firstly,let us prove that ?I

(F ) ?J (F )for all ground body formula F .We will use structural induction:

If F is a ground atomic formula,then it is obvious,ie

?I

(F )=I (F ) J (F )=?J (F )For the inductive case,consider F to be a body formula @(F 1,...,F n )and assume that ?I

(F i ) ?J (F i )for all i =1,...,n .By de?nition,@behaves as an aggregator,and therefore,using the induction hypothesis

?I

(F )=.@(?I (F 1),...,?I (F n )) .@(?J (F 1),...,?J (F n ))=?J (F )With the previous result,let us ?nish the proof:

Consider a rule C ←i B ,? in P and a ground θwith A =Cθ;by the induction hypotheses,as B θis a ground formula,we have the inequality ?I (B θ) ?J (B θ),then we also have ?.&i ?I

(B θ) ?.&i ?J (B θ)for all i ,since operators .&i are increasing.Now,by taking suprema T P (I )(A ) T P (J )(A )for all A .

Due to the monotonicity of the immediate consequences operator,the se-mantics of P is given by its least model which,by Knaster-Tarski’s theorem together with Theorem 9,is exactly the least ?x-point of T P ,which can be obtained by trans?nitely iterating T P from the least interpretation .

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A ?rst result in this approach is that whenever every operator turns out to be continuous in the lattice,then T P is also continuous and,consequently,its least ?x-point can be obtained by a countably in?nite iteration from the least interpretation.

Let us state the de?nition of continuous function which will be used.

De?nition 11Let L be a complete lattice and let f :L →L be a mapping.We say that f is continuous if it preserves suprema of directed sets,that is,given a directed set X one has

f (sup X )=sup {f (x )|x ∈X }

A mapping g :L n →L is said to be continuous provided that it is continuous in each argument separately.

Let F be a language interpreted on a multi-adjoint algebra L ,and let ωbe any operator symbol in the language.We say that ωis continuous if its interpret-ation under L ,that is .ω,is continuous in L .

Now we state and prove a technical lemma which will allow us to prove the continuity of the immediate consequences operator.

Lemma 12Let P be a multi-adjoint program,and let B be any body formula in P .Assume that all the operators in B are continuous,let X be a directed set of interpretations,and write S =sup X ;then

?S

(B )=sup {?J (B )|J ∈X }PROOF.Firstly,let us prove by structural induction the equality for all ground formula B ;in the base case of being an atomic ground formula the result is obvious,since

?S

(B )=S (B )=sup {J (B )|J ∈X }=sup {?J (B )|J ∈X }For the inductive case,consider B =@(B 1,...,B n ),then by de?nition we have ?S (B )=.@(?S (B 1),...,?S (B n ))since B is a ground formula.Now,by the induction hypothesis we have ?S

(B i )=sup {?J (B i )|J ∈X }for i =1,...,n ;thus

?S

(B )=.@ sup {?J (B 1)|J ∈X },...,sup {?J (B n )|J ∈X } Note that,as X is a directed set so is each {?J

(B i )|J ∈X }and,by the continuity of the aggregation operators,we have that

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?S (B )=sup {.@(?J (B 1),...,?J (B n ))|J ∈X }=sup {?J

(.@(B 1,...,B n ))|J ∈X }=sup {?J

(B )|J ∈X }Now we can consider the general case of a non-ground B .In this case we have that,for all ground instantiation B ξwe have

?S

(B ξ)=sup {?J (B ξ)|J ∈X }and calculating in?ma we obtain the desired equality.

Theorem 13

(1)If all the operators occurring in the bodies of the rules of a program P are

continuous,and the adjoint conjunctions are continuous in their second argument,then T P is continuous.

(2)Conversely,if T P is continuous for all program P ,then all the operat-

ors occurring in the bodies of the rules are continuous,and the adjoint conjunctions are continuous in their second argument

PROOF.(1).We have to check that for each directed subset of interpreta-tions X and each ground atomic formula A

T P (sup X )(A )=sup {T P (J )(A )|J ∈X }

Let us write S =sup X ,and consider the following chain of equalities:

T P (sup X )(A )=sup {?.&i ?S (B θ)| C ←i B ,? ∈P ,A =Cθ}(?)=sup {?.&i sup {?J (B θ)|J ∈X }| C ←i B ,? ∈P ,A =Cθ}( )=sup {?.&i ?J (B θ)|J ∈X,and C ←i B ,? ∈P ,A =Cθ}=sup {sup {?.&i ?J

(B θ)| C ←i B ,? ∈P ,A =Cθ}|J ∈X }=sup {T P (J )(A )|J ∈X }

where equality (?)follows from Lemma 12and equality ( )follows from the continuity of the operators .&i .

The proof of item (2)is similar.

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4Procedural semantics

Once we know that the least model can be reached in at most countably many iterations,that is as T Pω( ),under pretty general assumptions,it is worth to de?ne a procedural semantics which allows us to actually construct the answer to a query for a given program.

In this section we provide a procedural semantics to the paradigm of multi-adjoint logic programming,and a quasi-completeness theorem will be given.

In the following,we will be working in a hybrid language F e consisting of body formulas built up from elements of the lattice and propositional symbols;this way we can develop a symbolic computational model with partially evaluated formulas.The formal de?nition of the extended language is given below:

De?nition14Let P be a multi-adjoint logic program on a multi-adjoint al-gebra L with carrier L and let V be the set of truth values of the rules in P. The extended language F e is the corresponding algebra of body formulas freely generated from the disjoint union of V and the set of propositional symbolsΠ. Formulas in the language F e are called extended formulas,or simply e-formulas. An operator symbolωinterpreted under F e will be denoted asˉω.

Our computational model will take a query(an atom),providing a lower bound of the value of A under any model of the program.Intuitively,the computation proceeds by,somehow,substituting atoms by lower bounds of their truth-value until,eventually,an extended formula with no atom is obtained,which will be interpreted in the multi-adjoint lattice to get the computed answer. Given a program P,we de?ne the following admissible rules for transforming any pair formed by an e-formula and a substitution.

De?nition15Admissible rules for a pair(F,θ)where F is an e-formula andθis a substitution,and A is an atom occurring in F(denoted F[A]),are the following:

R1Substitute F[A]by(F[A/?ˉ&i B])θ ,andθbyθ ?θwhenever

(a)θ is the mgu of C and A,

(b)there exists a rule C←i B,? in P,

R2Substitute A by⊥(just to cope with unsuccessful branches),and do not modifyθ.

R3Substitute F[A]by(F[A/?])θ andθbyθ ?θwhenever

(a)θ is the mgu of C and A

(b)there exists a fact C←i ,? in P.

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Note that if an e-formula turns out to have no atoms,then it can be directly interpreted in the multi-adjoint lattice L .This justi?es the following de?nition of computed answer .

De?nition 16Let P be a program in a multi-adjoint language interpreted on a multi-adjoint lattice L and let ?A be a goal.An element (.@[r 1,...,r m ],θ),with r j ∈L ,for all j =1,...,m is said to be a computed answer if there is a sequence G 0,...,G n +1such that

(1)G 0=(A,id )and G n +1=(ˉ@[

r 1,...,r m ],θ )where θ=θ restricted to the variables of A and r j ∈L for all j =1,...m .(2)Every G i ,for i =1,...,n ,is a pair of an e-formula and a substitution.

(3)Every G i +1is inferred from G i by one of the admissible rules.

The length of this computed answer is de?ned to be n .

Note that the properties of multi-adjoint lattice guarantee that every com-puted answer is correct,therefore the correctness theorem for this procedural semantics is immediate.

4.1Reductants

It might happen that for some lattices it is not possible to get the greatest correct answer,an example can be easily constructed for a non-linear lat-tice L, as follows:let a,b be two incomparable elements in L ,and consider the situation in which a given query can be only matched with two rules,the ?rst one leading to the answer a ,and the second one leading to the answer b .By correctness of the procedural semantics we have that both a and b are correct answer and,therefore,it is obvious that the supremum of a and b is also a correct answer but uncomputable.

The idea to cope with this problem is the generalization of the concept of

https://www.doczj.com/doc/774666722.html,ly,whenever we have k rules A ←i @i (D i 1,...,D i n i ),?i for i =1,...,k ,then there should exist a rule allowing us to get the greatest

possible value of A under the program,that is,we would like to have the possibility of reaching the supremum of all the contributions in a single step of the computational model.

The reductant property is de?ned in [13]so that a single rule in the program computes the supremum stated above (which is a generalization of the concept

of reductant [8]).Any rule A ←i @i (D i 1,...,D i n i

),?i contributes,by the adjoint property,with a value of the form ?i .&i b i for the calculation of the

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lower bound for the truth-value of A;this fact justi?es the de?nition below: De?nition17Let P be a program,and A a ground atom,let C i←j i B i,?i be the set of rules in P whose head matches with A(there areθi such that A=C iθi)and assume that this set contains at least a proper rule;a reductant for A is any rule A←@(B1,...,B n)θ, where4θ=θ1···θn,and←is any implication with an adjoint conjunctor,and the aggregator@is de?ned as

.

@(b1,...,b n)=sup{?1.

&1b1,...,?n

.

&n b n}

If the only rules matching with A turn out to be facts C i,then the reductant is de?ned to be a fact which aggregates all the knowledge about A,i.e., A← ,sup{?1,...,?n}

It is immediate to prove that the rule constructed in the de?nition above,in presence of proper rules,behaves as a classical reductant for A in P.Note that, as a consequence,the choice of the adjoint pair to represent the corresponding reductant is irrelevant for the computational model.Therefore,in the follow-ing,we will assume that our language has a distinguished adjoint pair to be selected in the construction of reductants,leading to the so-called canonical reductants.

Note that we have chosen to discard non-determinism by means of the use of reductants,following traditional techniques of logic programming in the rest of the construction.

In the following,we will assume that our program P has the reductant prop-erty,as a consequence we can also assume that the program contains all the reductants,since it can be easily proved that the set of models is not modi?ed. As a?rst consequence of the assumption of the reductant property,we present a result which states that any computed answer with length n can be improved by n iterations of the T P operator,which also constructs computed answers as we will show later.

Proposition18If(λ,θ1···θn)is a computed answer of length n for a ground goal?A,thenλ T n P( )(A).

PROOF.By induction on the length of the computed answer for?A.

4Note that the order is not important since the rules can be assumed to be stand-ardized apart.

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For n=1either R2or R3has been applied.The case of R2is trivial,since λ=⊥;the case of R3implies that there is a fact C← ,? and substitutionθsuch that A=Cθ,therefore the computed answer has the form(?,θ)and:

?=?.

&i sup{?

.

&i T0P( )(Bθ)| C←i B,? ∈P and A=Cθ} =T1P( )(A)

Assume that the result is true for length less than n,and that(λ,θ1···θn)is a computed answer for?A of length n,for n>0.Obviously,the?rst step has been to apply R1on a rule,say C←i B,? with matching substitutionθ1, therefore we can assume that G1=?&i Bθ1.

Note that the formula Bθ1is ground;and let us denote it by@[B1,...,B l], where B i are its ground atoms.Now,obviously,each atom B i has a computed answer of length at most n?1,which can be written as(λi,θ2···θn)for i=1,...,l.By induction hypothesis,and the monotonicity of the T P operator we have

λi T n?1

P

( )(B i)for all i=1,...,l

therefore

λ=?.

&i

.

@(λ1,...,λl) ?

.

&i

.

@(T n?1

P

( )(B1),...,T n?1

P

( )(B l))

=?

.

&i

T n?1

P

( )(@[B1,...,B l])

=?

.

&i

T n?1

P

( )(Bθ1)

sup{?

.

&i

T n P( )(Bθ)| C←i B,? ∈P,A=Cθ} =T n P( )(A)

4.2Completeness result

The proof of the completeness result follows from the following proposition showing the behavior of both computed answers,which says that the T P op-erator actually builds computed answers for ground goals.Speci?cally, Proposition19Let P be a program and A be a ground atom,then the pair (T n P( )(A),id)is a computed answer for?A for all n.

PROOF.By induction on n.

For n=0,since T0P( )(A)=⊥,the result follows as an application of R2.

14

As induction hypothesis,assume that(T n P( )(Q),id)is a computed answer for

all ground goal?Q,and let us prove that(T n+1

P ( )(A),id)is also a computed

answer for a given ground atom A.As we will use canonical reductants for A, we have to consider two possibilities attending to its two possible forms.

On the one hand,if the reductant is a fact,then all the rules matching with A are facts,and it should have the form A← ,? ,where?is the supremum of the con?dence values of the facts matching with A.

Now,by de?nition of T P we have:

T n+1 P ( )(A)=sup{?i

.

&j

i

T n P( )(Bθ)| C←j

i

B,?i ∈P,A=Cθ}

(?)

=sup{?i| C←j i ,?i ∈P,A=Cθ}=?

where the equality(?)holds because no proper rule(only facts)in P match with A.

By using the canonical reductant,the sequence

G0=(A,id)G1=(?,id)

proves that(?,id)is a computed answer for?A.

On the other hand,if A←@(B1,...,B l)θ, is a ground instance of the canonical reductant for A,the body of the reductant can be interpreted as a composition of a number of aggregators depending on a number of atoms B i, which will be denoted as@ [B1,...,B k].Thus we have:

T n+1 P ( )(A)=sup{?

.

&i

T n P( )(Bφ)| C←i B,? ∈P,A=Cφ}

(?)

=

.

@(

T n P( )(B1θ),...,

T n P( )(B lθ))

=

T n P( )(@(B1,...,B l)θ)

=

T n P( )(@ [B1,...,B k])

=

.

@ (T n P( )(B1),...,T n P( )(B k))

where(?)follows by the de?nition of reductant.

By induction hypothesis,(T n P( )(B i),id)is a computed answer for?B i for all ground atom B i,with computing sequence

H i

0=(B i,id),...,H i

j

=(T n P( )(B i),id)

15

Consider G0=(A,id),and generate G1as an application of the canonical reductant for A,that is G1=( ˉ&ˉ@ [B1,...,B k],id).Now,we can use the computing sequences for B i to obtaining5

G r=( ˉ&ˉ@ [T n P( )(B1),...,T n P( )(B k)],id)

The proof of the completeness theorem follows easily from the previous the-orem.

Theorem20(Approximative-completeness)Given a program P,for every correct answer(λ;θ)for a program P and a ground goal?A,there is a sequence of computed answers(λn,id)such thatλ sup{λn|n∈N}.

PROOF.By Proposition19we have that,for all n,(T n P( )(A),id)is a com-puted answer for?A.

Now,considerλn=T n P( )(A);as(λ;θ)is a correct answer,and TωP( )is,in particular,a model,we have that

λ inf

ξ

TωP( )(Aθξ)

=TωP( )(A)=sup{T n P( )(A)|n∈N}=sup{λn|n∈N}

This approximative completeness theorem cannot be extended to a full com-pleteness theorem.It is not di?cult to show the existence of programs for which the greatest correct answer for a query?A,that is T Pω( )(A),can be approximated up to any value,but not attainable in?nitely many steps. Regarding non-ground queries,all the results in this section can be obtained by means of suitable versions of lifting lemmas;just consider that we are using classical crisp uni?cation in our computational model.Moreover,in the next section we will show how a similarity-based uni?cation approach can be constructed on it.

5Similarity-based uni?cation

Our approach will consider similarities acting on elements of domains of at-tributes.This was already motivated in the introduction,when we have het-erogeneous data source,and aiming to solve problems for the semantic web 5This is due to the fact that the B i are ground.

16

we would have to link,e.g.the relation human (name,birth date,address )to the relation person (name,birth date,address ).The idea is based on the fact that part of our knowledge base,the multi-adjoint program P ,might consist of graded facts representing information about existent similarities on di?erent domains which depend on the predicate they are used in,e.g.

s (Salaˇs ,Sza l as),0.8 s (Salaˇs ,Sz′a l′a s),0.7 s (Sz′a l′a s ,Sza l as),0.9 The particular semantics of our logic,based on the multi-adjoint paradigm,enables one to easily implement a version of fuzzy uni?cation by extending suitably our given program.

Given a program P we construct an extension by adding a parameterized theory (which introduces a number of similarities depending on the predicate and function symbols in P ),such as those below

s (x,x ), s (x,y )←s (y,x ), s (x,z )←s (x,y )&s (y,z ), For all function symbol we also have

s (f (x 1,...,x n ),f (y 1,...,y n ))←s f 1(x 1,y 1)&···&s f n (x n ,y n ),

Finally,given a predicate symbol,then the following rules are added

P (y 1,...,y n )←P (x 1,...,x n )&s P 1(x 1,y 1)&···&s P n (x n ,y n ),

where &is some conjunction suitably describing the situation formalized by the program.Note that the implication in these axioms can be arbitrary due to both,the adjoint property and the fact that the truth-value of each rule is always .

This way we get a multi-adjoint logic program P E in which it is possible to get computed answers wrt P E with similarity match in uni?cation.This justi?es the following extension of De?nition 16:

De?nition 21Let P be a program,let P E be an extension of this program by appropriate rules describing axioms of similarity for the respective predicate and function symbols,and let ?A be a query.An element (.@[r 1,...,r m ],θ),with r j ∈L ,for all j =1,...,m is said to be a similarity-based computed answer for ?A wrt the program P if it is a computed answer for ?A wrt P E .Note that similarity-based computed answers are nothing but computed an-swers with crisp uni?cation on a program extended by axioms of equality.

17

Moreover,as this approach makes use of the computational model of multi-adjoint programs,our similarity-based uni?cation model,could also get bene?t from existing e?cient implementations of its operational semantics[11]. Comparison with other approaches

A lot of research has been done in the?eld of uni?cation based on similarities, for instance,[1]starts from uni?cation based on similarity to derive a logic programming system,thoroughly relying on similarity,one of its principal features being the allowance of?exible information retrieval in deductive data bases.On the other hand,the purpose of[16]is to investigate the use of similarities as the basis for uni?cation and resolution in logic programming.As a result,a well-founded semantical method to incorporate linguistic variables into logic programming is given.

In contrast,the approach taken in[6]is based on fuzzy set theory and com-puting something like the degree of being a singleton.They are restricted to max-min connectives and we argue that similarity should be coupled by product semantics,at least when working in the unit interval.The reason is that the truth of the query answer should be independent of the fact whether we made a misprint and/or a translation error or not(one of possible reasons for having no uni?cation but similarity one).

An specially interesting connection can be established between our approach and the weak uni?cation algorithm introduced in[15].With this aim,in the rest of the section we will work in a particular case of the multi-adjoint frame-work in which L=[0,1]and the only connectives will be G¨o del’s implication and G¨o del’s conjunction.

We recall in the following paragraphs,in order to make this paper as self-contained as possible,Sessa’s weak uni?cation algorithm,which aims at?nd-ing a weak mgu of two atoms A and B starting from a set of equations W associated to A and B.

A weak uni?er for A=P(t1,...,t n)and B=Q(t 1,...,t n)with uni?ca-tion degreeλup to a similarity relation R(or,simply,aλ-uni?er)is a substitutionθsuch that

λ=min

1≤i≤n

R(P,Q),R(t iθ,t iθ)

=max

?∈Ψ

min

1≤i≤n

{R(P,Q),R(t i?,t i?)}

whereΨdenotes the set of all the substitutions.

The procedure constructs a sequence of improved sets of equations until a solved set is reached.The algorithm is given below:

18

Given two atoms A=P(t1,...,t n)and B=Q(t 1,...,t n)of the same arity with no common variables to be uni?ed,construct its associated set of equations W.If R(P,Q)=0,halts with failure,otherwise,setλ=R(P,Q) and W=W {P=Q}.

Until the current set of equation W does not change,non-deterministically choose from W an equation of a form below and perform the associated action.

(1)f(t1,...,t n)=g(t 1,...,t n)where R(f,g)>0replace by the equations

t1=t 1,...,t n=t n and setλ=min{λ,R(f,g)}.

(2)f(t1,...,t n)=g(t 1,...,t n)where R(f,g)=0;halts with failure.

(3)x=x delete the equation.

(4)t=x where t is not a variable;replace by the equation x=t.

(5)x=t where x=t and x has another occurrence in the set of equations:

if x appears in t then halt with failure,otherwise perform the substitution

{x/t}in every other equation.

Similarities are considered,in[15],to act on constants,function symbols and predicate symbols,therefore,assume that we have a similarity relation R acting on F∪P∪C(function,predicate and constant symbols)with truth-values ranging in the unit real interval[0,1].In our approach,the similarity R is internalized,that is,we include a new predicate symbol in our language, denoted s R.

Now,for every f,g∈F with R(f,g)>0let us extend our logic program by the following schema of axioms

s R(f(x1,...,x n),g(y1,...,y n))←s R(x1,y1)&···&s R(x n,y n),R(f,g) where,in the case of constants(as0-ary functions)it is understood as s R(c,d),R(c,d)

In addition,for every P,Q∈P with R(P,Q)>0let us extend our logic program by a schema of axioms

P(y1,...,y n)←Q(x1,...,x n)&s R(x1,y1)&···&s R(x n,y n),R(P,Q) The extension of a program P with respect to a similarity relation R described above will be denoted P R.

It is worth to mention that the equality axioms introduced in order to ob-tain P E are simply the similarity-based extension obtained when the‘external’similarity R is the usual equality relation.

19

In the following,in order to emulate the weak uni?cation algorithm,we will be working with the two-fold extension of the program P E R.Moreover,in this particular case,we will not need to have any other rules than those in the two-fold extension of an empty program?E R,denoted E∪R.

Theorem22Let P(t1,...,t n)and Q(t 1,...,t n)be two atoms,assume that some substitutionθis aλ-uni?er(forλ∈[0,1])obtained by the weak uni?c-ation algorithm,then(λ,θ)is a computed answer to the query?P(t1,...,t n) wrt the program E∪R∪{ Q(t 1,...,t n),1 }.

PROOF.As the weak uni?cation algorithm is non-deterministic,we will withdraw the use of reductants from our calculation,so that our computa-tional model turns back to be non-deterministic.

By induction along a branch of the computation of the weak uni?cation al-gorithm.We will show how the multi-adjoint computational model emulates each possible step in the weak uni?cation algorithm:

Preprocessing step.Setλ=R(P,Q).The emulation makes use of the rule P(y1,...,y n)←Q(x1,...,x n)&s R(x1,y1)&···&s R(x n,y n),R(P,Q) which builds the following e-formula

min

R(P,Q),Q(x1,...,x n)&s R(x1,t1)&···&s R(x n,t n)

now,by using the fact Q(t 1,...,t n),1 ,we obtain

min

R(P,Q),s R(t 1,t1)&···&s R(t n,t n)

which represents both the set of equations W of the weak uni?cation algorithm and the current value of the uni?cation degree.

We will show below our emulation of the applications of Steps1–5in the weak uni?cation algorithm.

Step1.Given t1=f(u1,...,u m)=g(v1,...,v m)=t 1,with R(f,g)>0,re-place by the equalities u1=v1,...,u m=v m and updateλ=min(λ,R(f,g)). The computation uses the axiom

s R(f(x1,...,x m),g(y1,...,y m))←

s R(x1,y1)&···&s R(x m,y m),R(f,g)

20

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tabularx宏包中改变弹性列的宽度

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4.对本研究课题有创造性见解,并取得显著的科研成果。 5.学位论文必须是作者本人独立完成,与他人合作的只能提出本人完成的部分。 6.论文字数不少于5万字,中、英摘要3000字;详细中文摘要(单行本)1万字左右。 (四)临床专业学位博士论文要求 1.要求论文课题紧密结合中医临床或中西结合临床实际,研究结果对临床工作具有一定的应用价值。 2.论文表明研究生具有运用所学知识解决临床实际问题和从事临床科学研究的能力。 3.论文字数一般不少于3万字,中、英文摘要2000字;详细中文摘要(单行本)5000字左右。 二、学位论文的格式要求 (一)学位论文的组成 博士、硕士学位论文一般应由以下几部分组成,依次为:1.论文封面;2. 原创性声明及关于学位论文使用授权的声明;3.中文摘要;4.英文摘要;5.目录; 6.引言; 7.论文正文; 8.结语; 9.参考文献;10.附录;11.致谢。 1.论文封面:采用研究生处统一设计的封面。论文题目应以恰当、简明、引人注目的词语概括论文中最主要的内容。避免使用不常见的缩略词、缩写字,题名一般不超过30个汉字。论文封面“指导教师”栏只写入学当年招生简章注明、经正式遴选的指导教师1人,协助导师名字不得出现在论文封面。 2.原创性声明及关于学位论文使用授权的声明(后附)。 3.中文摘要:要说明研究工作目的、方法、成果和结论。并写出论文关键词3~5个。 4.英文摘要:应有题目、专业名称、研究生姓名和指导教师姓名,内容与中文提要一致,语句要通顺,语法正确。并列出与中文对应的论文关键词3~5个。 5.目录:将论文各组成部分(1~3级)标题依次列出,标题应简明扼要,逐项标明页码,目录各级标题对齐排。 6.引言:在论文正文之前,简要说明研究工作的目的、范围、相关领域前人所做的工作和研究空白,本研究理论基础、研究方法、预期结果和意义。应言简

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3.摘要:论文摘要的字数壹般为300字左右。摘要是对论文的内容不加注释和评论的简短陈述,是文章内容的高度概括。主要内容包括:该项研究工作的内容、目的及其重要性;所使用的实验方法;总结研究成果,突出作者的新见解;研究结论及其意义。摘要中不列举例证,不描述研究过程,不做自我评价。 论文摘要后另起壹行注明本文的关键词,关键词是供检索用的主题词条,应采用能够覆盖论文内容的通用专业术语,符合学科分类,壹般为3~5个,按照词条的外延层次从大到小排列。 4.目录(目录示例见附件3):独立成页,包括论文中的壹级、二级标题、后记、参考文献、和附录以及各项所于的页码。 5.正文:包括前言、论文主体和结论 前言:为正文第壹部分内容,简单介绍本项研究的背景和国内外研究成果、研究现状,明确研究目的、意义以及要解决的问题。 论文主体:是全文的核心部分,于正文中应将调查、研究中所得的材料和数据加工整理和分析研究,提出论点,突出创新。内容可根据学科特点和研究内容的性质而不同。壹般包括:理论分析、计算方法、实验装置和测试方法、对实验结果或调研结果的分析和讨论,本研究方法和已有研究方法的比较等方面。内容要求论点正确,推理严谨,数据可靠,文字精炼,条理分明,重点突出。 结论:为正文最后壹部分,是对主要成果的归纳和总结,要突出创新点,且以简练的文字对所做的主要工作进行评价。 6.后记:对整个毕业论文工作进行简单的回顾总结,对给予毕业论文工作提供帮助的组织或个人表示感谢。内容应尽量简单明了,壹般为200字左右。 7.参考文献:是论文不可或缺的组成部分。它既可反映毕业论文工作中取材广博程度,又可反映文稿的科学依据和作者尊重他人研究成果的严肃态度,仍能够向读者提供有关

配合前面的ntheorem宏包产生各种定理结构

%=== 配合前面的ntheorem宏包产生各种定理结构,重定义一些正文相关标题===% \theoremstyle{plain} \theoremheaderfont{\normalfont\rmfamily\CJKfamily{hei}} \theorembodyfont{\normalfont\rm\CJKfamily{song}} \theoremindent0em \theoremseparator{\hspace{1em}} \theoremnumbering{arabic} %\theoremsymbol{} %定理结束时自动添加的标志 \newtheorem{definition}{\hspace{2em}定义}[chapter] %\newtheorem{definition}{\hei 定义}[section] %!!!注意当section为中国数字时,[sction]不可用! \newtheorem{proposition}{\hspace{2em}命题}[chapter] \newtheorem{property}{\hspace{2em}性质}[chapter] \newtheorem{lemma}{\hspace{2em}引理}[chapter] %\newtheorem{lemma}[definition]{引理} \newtheorem{theorem}{\hspace{2em}定理}[chapter] \newtheorem{axiom}{\hspace{2em}公理}[chapter] \newtheorem{corollary}{\hspace{2em}推论}[chapter] \newtheorem{exercise}{\hspace{2em}习题}[chapter] \theoremsymbol{$\blacksquare$} \newtheorem{example}{\hspace{2em}例}[chapter] \theoremstyle{nonumberplain} \theoremheaderfont{\CJKfamily{hei}\rmfamily} \theorembodyfont{\normalfont \rm \CJKfamily{song}} \theoremindent0em \theoremseparator{\hspace{1em}} \theoremsymbol{$\blacksquare$} \newtheorem{proof}{\hspace{2em}证明} \usepackage{amsmath}%数学 \usepackage[amsmath,thmmarks,hyperref]{ntheorem} \theoremstyle{break} \newtheorem{example}{Example}[section]

论文写作格式规范与要求(完整资料).doc

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二、毕业论文资料的填写与装订 毕业论文须用计算机打印,一律使用A4打印纸,单面打印。 毕业论文任务书、毕业论文考核评议表、毕业论文正稿、答辩纪录纸须用计算机打印,一律使用A4打印纸。答辩提问记录一律用黑色或蓝黑色墨水手写,要求字体工整,卷面整洁;任务书由指导教师填写并签字,经主管院领导签字后发出。 毕业论文使用统一的封面,资料装订顺序为:毕业论文封面、论文任务书、考核评议表、答辩记录、中文摘要、英文摘要、目录、正文、参考文献、致谢、附录(如果有的话)。论文封面要求用A3纸包边。 三、毕业论文撰写的内容与要求 一份完整的毕业论文正稿应包括以下几个方面: (一)封面(见封面模版) (二)论文题目(填写在封面上,题目使用2号隶书,写作格式见封面模版) 题目应简短、明确,主标题不宜超过20字;可以设副标题。(三)论文摘要(写作格式要求见《摘要、绪论、结论、参考文献写作式样》P1~P2) 1、中文“摘要”字体居中,独占一页

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先决条件 本教程的目标读者是入门级到中级水平的UNIX?开发人员和管理员。您应 该对使用UNIX命令行Shell和文本编辑器有基本的了解。 系统要求 要运行本教程中的示例,您需要访问运行UNIX操作系统并安装了下面这些软件的计算机(请参见本教程的参考资料部分以获取相关链接): Groff。Groff分发版中包括groff前端工具、troff后端排版引擎和本教 程中使用的各种附属工具。 自由软件基金会将Groff作为其GNU Project中的一部分进行了发布,所 发布的源代码符合GNU通用公共许可证(GPL)并得到了广泛的移植,几乎对于所有的UNIX操作系统、以及非UNIX操作系统(如Microsoft?Windows?)都有相应 的可用版本。 在撰写本教程时,最新的Groff发布版是Version 1.19.2,对于学习本教 程而言,您至少需要Groff Version 1.17。 gxditview。从Version 1.19.2开始,Groff中包含了这个工具,而在以 前的版本中,对其进行了单独的发布。 PostScript Previewer,如ghostview、gv或showpage。 如果您是从源代码安装Groff,那么请参考Groff源代码分发版中的自述 文件,其中列举了所需的任何额外的软件,而在编译和安装Groff时可能需要 使用这些软件。 介绍Groff 用户通常在字处理软件、桌面发布套件和文本布局应用程序等应用程序环 境中创建文档,而在这些环境中,最终将对文档进行打印或导出为另一种格式。整个文档准备过程,从创建到最后的输出,都发生在单个应用程序中。文档通

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论文的写作格式及规范

论文的写作格式及规范

附件9: 科学技术论文的写作格式及规范 用非公知公用的缩写词、字符、代号,尽量不出现数学式和化学式。 2作者署名和工作单位标引和检索,根据国家有关标准、数据规范为了提高技师、高级技师论文的学术质量,实现论文写的科学化、程序化和规范化,以利于科技信息的传递和科技情报的作评定工作,特制定本技术论文的写作格式及规范。望各位学员在注重科学研究的同时,做好科技论文撰写规范化工作。 1 题名 题名应以简明、确切的词语反映文章中最重要的特定内容,要符合编制题录、索引和检索的有关原则,并有助于选定关键词。 中文题名一般不宜超过20 个字,必要时可加副题名。英文题名应与中文题名含义一致。 题名应避免使作者署名是文责自负和拥有著作权的标志。作者姓名署于题名下方,团体作者的执笔人也可标注于篇首页地脚或文末,简讯等短文的作者可标注于文末。 英文摘要中的中国人名和地名应采用《中国人名汉语拼音字母拼写法》的有关规定;人名姓前名后分写,姓、名的首字母大写,名字中间不加连字符;地名中的专名和通名分写,每分写部分的首字母大写。 作者应标明其工作单位全称、省及城市名、邮编( 如“齐齐哈尔电业局黑龙江省齐齐哈尔市(161000) ”),同时,在篇首页地脚标注第一作者的作者简介,内容包括姓名,姓别,出生年月,学位,职称,研究成果及方向。

3摘要 论文都应有摘要(3000 字以下的文章可以略去)。摘要的:写作应符合GB6447-86的规定。摘要的内容包括研究的目的、方法、结果和结论。一般应写成报道性文摘,也可以写成指示性或报道-指示性文摘。摘要应具有独立性和自明性,应是一篇完整的短文。一般不分段,不用图表和非公知公用的符号或术语,不得引用图、表、公式和参考文献的序号。中文摘要的篇幅:报道性的300字左右,指示性的100 字左右,报道指示性的200字左右。英文摘要一般与中文摘要内容相对应。 4关键词 关键词是为了便于作文献索引和检索而选取的能反映论文主题概念的词或词组,一般每篇文章标注3?8个。关键词应尽量从《汉语主题词表》等词表中选用规范词——叙词。未被词表收录的新学科、新技术中的重要术语和地区、人物、文献、产品及重要数据名称,也可作为关键词标出。中、英文关键词应一一对应。 5引言 引言的内容可包括研究的目的、意义、主要方法、范围和背景等。 应开门见山,言简意赅,不要与摘要雷同或成为摘要的注释,避免公式推导和一般性的方法介绍。引言的序号可以不写,也可以写为“ 0”,不写序号时“引言”二字可以省略。 6论文的正文部分 论文的正文部分系指引言之后,结论之前的部分,是论文的核心, 应按GB7713--87 的规定格式编写。 6.1层次标题

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