The role of domain knowledge in a large scale Data
Mining project
Ioannis Kopanas, Nikolaos M. Avouris, Sophia Daskalaki
University of Patras, 26500 Rio Patras, Greece
(ikop@ee.upatras.gr, N.Avouris@ee.upatras.gr, sdask@upatras.gr)* Abstract. Data Mining techniques have been applied in many application areas.
A Data Mining project has been often described as a process of automatic dis-
covery of new knowledge from large amounts of data. However the role of the
domain knowledge in this process and the forms that this can take, is an issue
that has been given little attention so far. Based on our experience with a large
scale Data Mining industrial project we present in this paper an outline of the
role of domain knowledge in the various phases of the process. This project has
led to the development of a decision support expert system for a major Tele-
communications Operator. The data mining process is described in the paper as
a continuous interaction between explicit domain knowledge, and knowledge
that is discovered through the use of data mining algorithms. The role of the
domain experts and data mining experts in this process is discussed. Examples
from our case study are also provided.
1 Introduction
Knowledge discovery in large amounts of data (KDD), often referred as data min-ing, has been an area of active research and great advances during the last years. While many researchers consider KDD as a new field, many others identify in this field an evolution and transformation of the applied AI sector of expert systems or knowledge-based systems. Many ideas and techniques that have emerged from the realm of knowledge-based systems in the past are applicable in knowledge discovery projects. There are however considerable differences between the traditional knowl-edge-based systems and knowledge discovery approaches. The fact that today large amounts of data exist in most domains and that knowledge can be induced from these data using appropriate algorithms, brings in prominence the KDD techniques and facilitates the building of knowledge-based systems. According to Langley and Simon [1] data mining can provide increasing levels of automation in the knowledge engi-*I. Kopanas, N.M. Avouris, S. Daskalaki, The role of knowledge modeling in a large scale Data Mining project, in I.P Vlahavas, C.D. Spyropoulos (eds), Methods and Applications of Artificial Intelligence, Lecture Notes in AI, LNAI no. 2308, pp. 288-299, Springer-Verlag, Berlin, 2002.
neering process, replacing much time-consuming human activity with automatic tech-niques that improve accuracy or efficiency by discovering and exploiting regularities in stored data. However the claim that data mining approaches eventually will auto-mate the process and lead to discovery of knowledge from data with little intervention or support of domain experts and domain knowledge is not always true.
The role of the domain experts in KDD projects has been given little attention so far. In contrary to old knowledge-based systems approaches where the key roles were those of the domain expert and the knowledge engineer, today there have been more disciplines involved that seem to play key roles (e.g. data base experts, data analysts, data warehouse developers etc.) with the consequence the domain experts to receive less prominence. Yet, as admitted in Brachman &Anand [2], the domain knowledge should lead the KDD process. Various researchers have made suggestions on the role of domain knowledge in KDD. Domingos [3] suggests use of domain knowledge as the most promising approach for constraining knowledge discovery and for avoiding the well-known problem of data overfitting by the discovered models. Yoon et al. [4], referring to the domain knowledge to be used in this context, propose the following classification: inter-field knowledge, which describes relationship among attributes, category domain knowledge that represents useful categories for the domains of the attributes and correlation domain knowledge that suggests correlations among attrib-utes. In a similar manner Anand et al. [5] identify the following forms of domain knowledge: attribute relationship rules, hierarchical generalization trees and con-straints. An example of the latter is the specification of degrees of confidence in the different sources of evidence. These approaches can be considered special cases of the ongoing research activity in knowledge modeling, ontologies and model-based knowl-edge acquisition, see for instance [6], [7], with special emphasis in cases of data-mining driven knowledge acquisition.
However these studies concentrate in the use of domain knowledge in the main phase of data mining, as discussed in the next section, while the role of domain knowledge in other phases of the knowledge discovery process is not covered. In this paper we attempt to explore our experience with a large-scale data-mining project, to identify the role of the domain knowledge in the various phases of the process. Through this presentation we try to demonstrate that a typical KDD project is mostly a multi-stage knowledge modelling experiment in which domain experts play a role as crucial as in any knowledge-based system building exercise.
2 Identification of key roles and key phases of a KDD project According to Langley and Simon [1] the following five stages are observed in the development of a knowledge-based system using inductive techniques: (a) problem formulation, (b) determination of the representation for both training data and knowl-edge to be learned, (c) collection of training data and knowledge induction, (d) evaluation of learned knowledge, (e) fielding of the knowledge base. In Fayyad et al.
[8] the process of KDD is described through the following nine steps: (a) Defining the goal of the problem, (b) Creating a target dataset, (c) Data cleaning and pre-
processing, (d) Data transformation, e.g. reduction and projection in order to obtain secondary features, (e) matching the goals of the project to appropriate data mining method (e.g. clustering, classification etc.), (f) Choosing the data mining algorithm to be used, (g) Data Mining, (h) Interpretation of identified patterns, (i) Using discov-ered knowledge. By comparing the two processes one should notice the emphasis of the first frame on knowledge and the second on data analysis and processing. How-ever in reality, while the stages proposed by Fayyad et al. do occur in most cases, the role of domain knowledge in them is also important, as discussed in this paper, while the final stage of building the knowledge base and fielding the system is also knowl-edge-intensive, often involving multiple knowledge representations and demanding many knowledge evaluation and knowledge visualization techniques.
The subject of our case study was the development of a knowledge-based decision support system for customer insolvency prediction in a large telecommunication in-dustry. During the initial problem definition phase the observation of existence of large amounts of data in the industry concerned, led to the decision of extensive use of KDD techniques during this project. However this extensive dataset did not cover all aspects of the problem. While high degree of automation in modern telephone switch-ing centres means that telephone usage by the customers of the company was well monitored, information on the customers financial situation and credit levels, which are particularly important for this problem, were missing. This is a problem that often occurs in real problems; that is different levels of automation in different aspects of the problem domain leads to non-uniform data sets. Also techniques to infer knowl-edge, based on assumptions, observations and existing data need to be used exten-sively during the problem definition and modeling phase. So, for instance, if informa-tion on the credit levels of a customer is missing, this can be inferred from information on regularity of payment of the telephone bills, based on the assumption that irregular payments are due to financial difficulties of the customers involved. This is a typical example of use of domain knowledge in the so called ‘data transformation phase’. From early stages it was deduced that a number of domain experts and sources of data had to be involved in the process. Domain experts, e.g. executives involved in tackling the problem of customer insolvency and salesmen who deal with the problem in day-by-day basis were interviewed during the problem formulation phase and their views on the problem and its main attributes were recorded. An investigation of the available data was also performed and this involved executives of the information systems and the corporate databases who could provide an early indication on sources and quality of data. Other key actors were data analysts, who were also involved together with knowledge engineers and data mining experts.
3 Business knowledge and KDD
In many KDD projects, like the case study discussed here, the domain knowledge takes the form of business knowledge, as this represents the culture and rules of prac-tice of the particular company that has requested the knowledge-based system. Busi-ness knowledge has been a subject of interest for management consultant firms and
business administration researchers [9]. Business process re-engineering (BPR) is a keyword that has been extensively used during the last years, while special attention has been put in building the so-called “institutional memory”, “lessons learned data bases” and “best practice repositories”. While there are but few examples of success-ful full-scale repositories of business knowledge in large companies today, the wide-spread application of these techniques makes worth investigating their existence. The relevance of these approaches to KDD projects and the importance of them as sources of domain knowledge to data mining efforts is evident and for this reason they should be taken in consideration. It should also be noticed that a side effect of a major data-mining project could be the adaptation of a business knowledge base with many rules and practices, which resulted from the KDD process. This is also the case with tacit knowledge and implicit knowledge, which is the not documented business knowledge, often discovered during such a project.
The distinction between domain knowledge and business knowledge is that the former relates to a general domain while the latter to a specific business, thus both are required in the case of a specific knowledge based system that is to be commissioned to a specific company. A special case of business knowledge that affects the KDD process relates to the business objectives as they become explicit and relate to prob-lem definition. These can influence the parameters of the problem and measures of performance, as discussed by Gur Ali and Wallace [10]. In the following an example of such mapping of business objectives to measures of system performance is de-scribed for our case study.
4 Use of domain knowledge in an insolvency prediction case study
In this section, some typical examples of applying domain and business knowledge in the case study of the customer insolvency problem are provided. The examples are presented according to their order of appearance in the different phases of the KDD process. In the following section a classification of the domain and business knowl-edge used is attempted. The discussion included in this section does not provide a full account of the knowledge acquisition and modeling case study. It attempts rather through examples to identify the role of domain knowledge in the various phases of the project. A more detailed account would have included details on the modelling process, which involved many iterations and revisions of discovered knowledge.
A detailed description of the problem of customer insolvency of the telecommuni-cations industry is beyond the scope of this paper. For more information on the prob-lem, the approach used and the performance of the developed system, see Daskalaki et al. [11].
4.1 Problem definition
In this phase the problem faced by a telecommunications organization was defined and requirements relating to its solution were set. The role of domain experts and the importance of domain and business knowledge in this phase is evident. For instance
the billing process of the company, the rules concerning overdue payments and cur-rently applied measures against insolvent customers need to be explicitly described by domain experts.
4.2 Creating target data set
Formulation of the problem as a classification problem was performed at this stage. Available data were identified. As often occurs in KDD projects available data were not located in the same database, while discrepancies were observed among the enti-ties of these databases. This phase was not focused on the specific features to be used as parameters for training data, but rather on broad data sets that were considered relevant, to be analyzed in subsequent steps. So the sources of data were: (a) tele-phone usage data, (b) financial transactions of customers with the company (billing, payments etc.), (c) customer details derived from contracts and phone directory en-tries (customer occupation, address etc.). As discussed earlier more details of cus-tomer credit conditions were not available in the corporate databases and could not become available from outside sources. The role of domain and business knowledge in this stage concerned the structure of the available information and the semantic value of it, so this knowledge was offered mostly by the data processing department, in particular employees involved in data entry for the information systems involved. Serious limitations of the available data were identified during this process. For in-stance it was discovered that the information systems of the organization did not make reference to the customer as an individual in recorded transactions, but rather as a phone number owner. This made identification of an individual as an owner of multi-ple telephone connections particularly difficult.
4.3 Data prepossessing and transformation
This phase is the most important preparation phase for the data mining experi-ments; The domain knowledge during this stage has been used in many ways:
(i) elimination of irrelevant attributes
(ii) inferring more abstract attributes from multiple primary values
(iii) determination of missing values
(iv) definition of the time scale of the observation periods,
(v) supporting data reduction by sampling and transaction elimination
In all the above cases the domain knowledge contributes to reduction of the search space and creation of a data set in which data mining of relevant patterns could be subsequently performed. Examples of usage of domain knowledge are: Example of case i: The attribute “billed amount” was considered as irrelevant since it is known that not only insolvent customers relate to high bills, but also very good solvent customers.
Examples of case ii: Large fluctuation of the amounts in consecutive bills is consid-ered important indication of insolvency, so these fluctuations should be esti-mated and taken in consideration.
Overdue payments have been inferred by comparison of due and payment dates of bills.
Considerable reduction of data was achieved by aggregating transactional data in the time dimension according to certain aggregation functions (sum, count, avg, stddev) and deduced attributes. Domain-related hypotheses of relevance of these de-duced attrib-utes have driven this process. An example was the DiffCount attribute that represents the number of different telephone numbers called in a given period of time and the deviation of this attribute from a moving average in consecutive time periods. Defini-tion of this attribute is based on the assumption that if the diversity of called numbers fluctuates this is an entity related to possible insolvency.
Example of case iii: In many cases the missing values were deduced through inter-related attributes, e.g. Directory entries were correlated with customer records in order to determine the occupation of a customer, payments were related to billing periods, by checking the amount of the bill etc.
Examples of case iv: The transaction period under observation was set to 6 months prior to the unpaid bill, while the aggregation periods of phone call data was set to that of a fortnight.
Example of case v: Transactions related to inexpensive calls (charging less than 0.3 euros) were considered not interesting and were eliminated, resulting in reduction of about 50% of transaction data.
Sampling of data with reference to representative cases of customers in terms of area, activity and class (insolvent or solvent) was performed. This resulted in a data set concerning the 2% of transactions and customers of the company.
4.4 Feature and algorithm selection for data mining
At this phase the data mining algorithms to be used are defined (in our case deci-sion trees, neural networks and discriminant analysis) and the transformed dataset of the previous phase is further reduced by selecting the most useful features in adequate form for the selected algorithm. This feature selection is based mostly on automatic techniques, however domain knowledge is used for interpretation of the selected fea-ture set. Also this process is used for verification of the previous phase assumptions, so if certain features do not prove to be discriminating factors then new attributes should be deduced and tested. It should also be mentioned that this feature selection process is often interleaved with the data mining process, since many algorithms select the most relevant features during the training process. In our case a stepwise discrimi-nant analysis was used for feature selection.
4.5 Data Mining
Training a classifier using the cases of the collected data is considered the most im-portant phase of the process. Depending on the mining algorithm selected, the de-rived knowledge can be interpreted by domain experts. For instance the rules defined by a decision tree can be inspected by domain experts. Also the weights related to the input variables of a neural network reflect their relevant importance in a specific net-work. This is related to the performance of the model.
Extensive experiments often take place using a trial and error approach, in which the contribution of the classes in the training dataset and the input features, as well as the parameters of the data mining algorithm used, can vary. The performance of the de-duced models indicate which of the models are most suitable for the knowledge-based system.
In an extensive experimentation that took place in the frame of our case study, 62 features were included in the original data set. Subsequently, 5 different datasets where created that where characterised by different distribution of the classes (S)olvent/ (I)nsolvent customer. These distributions were the following: (I/S: 1:1, 1:10, 1:25, 1:50, 1:100)
A ten-fold validation of each data mining experiment was performed, by redistrib-uting the training/testing cases in the corresponding data sets. This way 50 classifying decision trees were obtained. By inspecting the features that have been used in these experiments, we selected the 20 most prominent, shown in Table 1.
Table 1. Most popular features used in the 50 classifiers
Feature Feature description n.
NewCust Identification of a new connection 50
Latency Count of late payments 50
Count_X_charges Count of bills with extra charges 50
CountResiduals Count of times the bill was not paid in full 50
StdDif Std Dev. of different numbers called 50
TrendDif11 Discrepancy from the mov. avg. of four
50
previous periods of the count of different
numbers called, measured on the 11th period.
TrendDif10 Idem for the 10th period 50
TrendDif7 Idem for the 7th period 50
TrendDif6 Idem for the 6th period 50
TrendDif3 Idem for the 3rd period 50
TrendUnitsMax Maximum discrepancy from the moving
45
average in units charged over the fifteen 2-week
periods.
TrendDif5 Idem for the 5th period 43
TrendDif8 Idem for the 8th period 40
39
Average_Dif Average # of different numbers called over the
fifteen 2-weeks period.
Type Type of account, e.g. business, domestic etc. 33
31
MaxSec Maximum duration of the calls in any 2-week
period during the study period.
28
TrendUnits5 Discrepancy from the moving average of the
units charged, measured on the 5th period.
23
AverageUnits Average # of units charged over the fifteen 2-
weeks periods.
TrendCount5 Discrepancy from the moving average of the
21
total # of calls, over the fifteen 2-week periods.
CountInstallments Count of times the customer requested payment
18
by instalments.
In Table 1, one may observe that the time-dependent feature most frequently used was the one related with the dispersion of the telephone numbers called (TrendDif, StdDif etc., 9 occurrences). This is a derived feature, proposed by the domain experts as discussed above, that could not possibly be defined without the domain experts contribution. This table demonstrates the important role of the domain experts in sug-gesting meaningful features during this phase.
Case distribution 1:1
If (StdDif<0.382952541) And (MaxSec<1086)
Then
INSOLVENT (confidence 1.4%)
If (StdDif<0.382952541) And (MaxSec>1086) And (ExtraDebt>=1.5)
Then
INSOLVENT (confidence 100%)
If (StdDif>=0.382952541) And (TrendCountMax>=4.625)
Then
INSOLVENT (confidence 5.36%)
Case distribution 1:10
If (CountXCharges<1.5) And (NewCust<0.5) And (TrendDif11<-0.625) And (TrendSec3<-1863.75)
Then
INSOLVENT (confidence 0%)
If (CountXCharges<1.5) And (NewCust>=0.5) And (CountResiduals>=0.5) And (TrendDif7<-0.625)
Then
INSOLVENT (confidence 12.5%)
If (CountXCharges<1.5) And (NewCust>=0.5) And (CountResiduals>=0.5) And (TrendDif7>=-0.625) And
(StdDif<0.487950027) Then
INSOLVENT (confidence 10.93%)
If (CountXCharges>=1.5) And (StdDif>=0.305032313) And (TrendUnitsMax>=121.25) And (TrendUnits6<-
2.375) And (TrendDif10<-0.125)
Then
INSOLVENT (confidence 12.26%)
If (CountXCharges>=1.5) And (StdDif>=0.305032313) And (TrendUnitsMax>=121.25) And (TrendUnits6>=-
2.375) then
INSOLVENT (confidence 7.6%)
Case distribution 1:25
if (CountXCharges<2.5) AND (NewCust>=0.5) AND (CountResiduals>=0.5) AND (TrendCount5>=-1.25)
AND (TrendDif6<-0.375)
then
INSOLVENT (confidence 25.8% )
if (CountXCharges<2.5) AND (NewCust>=0.5) AND (CountResiduals>=0.5) AND (TrendCount5>=-1.25) AND (TrendDif6>=-0.375) AND (TrendCount5<1.375) AND (Type<55.5)
Then
INSOLVENT (confidence 55.03% )
if (CountXCharges>=2.5) AND (TrendDif3<-0.125) AND (TrendUnitsMax>=222.625) Then
INSOLVENT (confidence 9.49% )
Fig.1 Knowledge in form of rules, determining Customer Insolvency
4.6 Evaluation and interpretation of learned knowledge
Evaluation of the learned knowledge usually involves measuring the performance using a test data set. However this also involves knowledge interpretation, as dis-cussed in the previous section, which involves domain experts. Knowledge interpreta-tion can be based on the performance on test cases and on inspection of the derived knowledge if adequate knowledge representation formalism has been used. The evaluation criteria for the learned knowledge performance may be related to business objectives as defined by domain experts. An example of evaluation criteria is de-scribed in this section.
In figure 1 the knowledge in the form of rules, classifying the minority class cases (INSOLVENT customers) are exposed. It may be noticed that there is a considerable deviation in the parameters contributing to each of the rules, while the measure of performance of the rules vary considerably as indicated by the confidence measure expressing the rule performance in the test data set.
The criteria used for quantitative evaluation of learned knowledge in our case, as suggested by the domain experts, were different than the usual overall success rate and the specific class success rate indices usually applied in this kind of experiments. The domain experts suggested the following two criteria in our case study:
? The precision of the classifier, which is defined as the percentage of the actu-ally insolvent customers in those, predicted as insolvent by the classifier.
? The accuracy of the classifier, which is defined as the percentage of the cor-rectly predicted insolvent out of the total cases of insolvent customers in the data set.
These measures in problems of imbalanced class distributions, like in our case, in which the incidents of insolvent customers are very rare compared to those of solvent ones, seem more appropriate for measuring the effectiveness of the induced knowl-edge. By introducing these criteria, we discovered that the learned knowledge, despite of the fact that had very high success rates both overall and in specific classes, it did not meet the business objectives as these were defined by the Telecommunication Company (i.e. the requested measure of success was precision > 80% and accuracy > 50%).
An example of such a classifier is presented in the following table 2. In this table the performance of the classifier is shown in the testing data set. From this table one can see that the performance of this particular classifier is over 90 % in the majority class and over 83% in the minority class. However the precision is 113/2844= 3.9% and the accuracy is 113/136= 83%, thus making the performance in terms of the busi-ness objective set, not acceptable.
Table 2 Performance of classifier C1-3 for the insolvency prediction problem
4.7 Fielding the knowledge base
This stage is essential in knowledge-based system development project, while this is often omitted in data mining projects as considered outside the scope of the data mining experiment. During this phase the learned knowledge is combined with other domain knowledge in order to become part of an operational decision support system, used by the company that commissioned the KDD project. The domain knowledge plays an important role during this stage. Usually the learned knowledge is just a part of this knowledge-based system, while heuristics or other forms of knowledge are of-ten used as pre- or post-processors of the learned knowledge. In our case, the domain experts have suggested that the customers classified as insolvent, should be examined in more detail in terms of the amount due, the percentage of this amount that is due to third telecommunication operators, previous history of the customer etc, attributes that did not participate in the classification algorithm’s decision, yet important for taking measures against the suspected insolvency.
In the fielded knowledge based system important aspects are also the available means for convincing the decision-maker for the provided advice. This can be achieved by providing explanation on the proposed suggestion or visualizing the data and the knowledge used, as suggested by many researchers, see Ankerst et al [12], Brachman & Anand [2], etc.
5 Conclusion
This paper has focused on the role of domain knowledge in a data-mining project. Eight distinct phases have been identified in the process and the role of the domain experts in each one of them has been discussed. In summary this role is shown in Ta-ble 3. Predicted cases
Category Insolvent (0) Solvent (1)
Insolvent (0) 113
(83.1 %)
23 (16.9%) Actual cases
Solvent (1)
2731
(9.8 %) 25081 (90.2 %)
Table 3 Overview of use of domain knowledge in a data mining project
From this table and the discussion of the previous section it can be seen that while it is true that the domain knowledge plays a crucial role mostly in the initial and final stages of the process, it has contributed to some degree in all the phases of the project. If one takes also in consideration that the data mining phase (e), that necessitates comparatively little use of domain knowledge, accounts usually for not more than the 5% of the effort of such project, according to Williams and Huang [13], it is the most demanding stages of the process in which the domain experts and the domain knowl-edge participate mostly.
A conclusion of this study is that the data mining projects cannot possibly lead to successful knowledge-based systems, if attention is not paid to all the stages of the process. Since the domain knowledge plays such a crucial role in most of these stages, one should consider a data-mining project as a knowledge-driven process.
More support and adequate tools are therefore needed to be devised, which model the domain knowledge and track the contribution of domain experts that influence the assumptions made and the decision taken during the process.
A concluding remark is that in terms of the actors involved in the process, next to the experts related to data analysis, data mining, data warehousing and data processing in a prominent position there should be put the domain experts that should participate actively and guide the process. stage
Use of Domain Knowledge (DK) Type of DK Tools used (a) Problem definition HIGH Business and domain knowledge, requirements
Implicit, tacit knowledge
(b) Creating target data set MEDIUM
Attribute relations, semantics of corporate DB Data warehouse (c ) Data prepossessing and transformation HIGH
Tacit and implicit knowledge for inferences Database tools, statistical analysis (d ) Feature and
algorithm selection
MEDIUM Interpretation of the selected features Statistical analysis (e) Data Mining
LOW Inspection of discovered knowledge Data mining tools (f) Evaluation of learned
knowledge
MEDIUM Definition of criteria related to business objectives Data mining tools (g) Fielding the
knowledge base HIGH Supplementary domain knowledge necessary for implementing the system Knowledge-based system shells and
development tools
Acknowledgements
The research reported here has been funded under project YPER97-109 of the Greek Secretariat of Research and Technology. Special thanks are also due to the group of scientists of OTE S.A. that supplied us with data and continuous support. Special thanks to IBM for providing licenses of the DB2? and Intelligent Miner? products under their academic support programme. Also special thanks are due to the construc-tive comments of the anonymous reviewers on earlier draft of this paper. References
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五年级上册成语解释及近义词反义词和造句大全 囫囵吞枣；【解释】：囫囵：整个儿。把枣整个咽下去，不加咀嚼，不辨味道。比喻对事物不加分析考虑。【近义词】：不求甚解【反义词】融会贯穿[造句];学习不能囫囵吞枣而是要精益求精 不求甚解；bùqiúshènjiě【解释】：甚：专门，极。只求明白个大概，不求完全了解。常指学习或研究不认真、不深入【近义词】：囫囵吞枣【反义词】：精益求精 造句；1；在学习上，我们要理解透彻，不能不求甚解 2；学习科学文化知识要刻苦钻研，深入领会，不能粗枝大叶，不求甚解。 千篇一律；【解释】：一千篇文章都一个样。指文章公式化。也比喻办事按一个格式，专门机械。 【近义词】：千人一面、如出一辙【反义词】：千差万别、形形色色 造句；学生旳作文千篇一律，专门少能有篇与众不同旳，这确实是平常旳练习太少了。 倾盆大雨；qīngpéndàyǔ【解释】：雨大得象盆里旳水直往下倒。形容雨大势急。 【近义词】：大雨如柱、大雨滂沱【反义词】：细雨霏霏牛毛细雨 造句；3月旳天说变就变，瞬间下了一场倾盆大雨。今天下了一场倾盆大雨。 坚决果断；áobùyóuyù：意思；做事果断，专门快拿定了主意，一点都不迟疑，形容态度坚决 近义词；不假思索斩钉截铁反义词；犹豫不决 造句；1看到小朋友落水，司马光坚决果断地搬起石头砸缸。2我坚决果断旳承诺了她旳要求。 饥肠辘辘jīchánglùlù【近义词】：饥不择食【反义词】：丰衣足食 造句；1我放学回家已是饥肠辘辘。2那个饥肠辘辘旳小孩差不多两天没吃饭了 滚瓜烂熟gǔnguālànshóu〔shú)【解释】：象从瓜蔓上掉下来旳瓜那样熟。形容读书或背书流利纯熟。【近义词】：倒背如流【反义词】：半生半熟造句；1、这篇课文我们早已背得滚瓜烂熟了 流光溢彩【liúguāngyìcǎi】解释；光影，满溢旳色彩，形容色彩明媚 造句：国庆节，商场里装饰旳流光溢彩。 津津有味；jīnjīnyǒuwèi解释：兴趣浓厚旳模样。指吃得专门有味道或谈得专门有兴趣。 【近义词】：兴致勃勃有滋有味【反义词】：索然无味、枯燥无味 造句；1今天旳晚餐真丰富，小明吃得津津有味。 天长日久；tiānchángrìjiǔ【解释】：时刻长，生活久。【近义词】：天长地久【反义词】：稍纵即逝 造句：小缺点假如不立即改掉, 天长日久就会变成坏适应 如醉如痴rúzuìrúchī【解释】：形容神态失常，失去自制。【近义词】：如梦如醉【反义词】：恍然大悟造句；这么美妙旳音乐，我听得如醉如痴。 浮想联翩【fúxiǎngliánpiān解释】：浮想：飘浮不定旳想象；联翩：鸟飞旳模样，比喻连续不断。指许许多多旳想象不断涌现出来。【近义词】：思绪万千 造句；1他旳话让人浮想联翩。2:这幅画饱含诗情，使人浮想联翩，神游画外，得到美旳享受。 悲欢离合bēihuānlíhé解释；欢乐、离散、聚会。泛指生活中经历旳各种境遇和由此产生旳各种心情【近义词】：酸甜苦辣、喜怒哀乐【反义词】：平淡无奇 造句；1人一辈子即是悲欢离合,总要笑口常开,我们旳生活才阳光明媚. 牵肠挂肚qiānchángguàdù【解释】：牵：拉。形容十分惦念，放心不下 造句；儿行千里母担忧，母亲总是那个为你牵肠挂肚旳人 如饥似渴rújīsìkě：形容要求专门迫切，仿佛饿了急着要吃饭，渴了急着要喝水一样。 造句；我如饥似渴地一口气读完这篇文章。他对知识旳如饥似渴旳态度造就了他今天旳成功。 不言而喻bùyánéryù【解释】：喻：了解，明白。不用说话就能明白。形容道理专门明显。 【近义词】：显而易见【反义词】：扑朔迷离造句；1珍惜时刻，好好学习，那个道理是不言而喻旳 与众不同；yǔzhòngbùtóng【解释】：跟大伙不一样。 〖近义词〗别出心裁〖反义词〗平淡无奇。造句； 1从他与众不同旳解题思路中，看出他专门聪慧。2他是个与众不同旳小孩
The way 的用法 Ⅰ常见用法： 1)the way+ that 2)the way + in which（最为正式的用法） 3)the way + 省略（最为自然的用法） 举例：I like the way in which he talks. I like the way that he talks. I like the way he talks. Ⅱ习惯用法： 在当代美国英语中，the way用作为副词的对格，“the way+ 从句”实际上相当于一个状语从句来修饰整个句子。 1)The way =as I am talking to you just the way I’d talk to my own child. He did not do it the way his friends did. Most fruits are naturally sweet and we can eat them just the way they are—all we have to do is to clean and peel them. 2）The way= according to the way/ judging from the way The way you answer the question, you are an excellent student. The way most people look at you, you’d think trash man is a monster. 3）The way =how/ how much No one can imagine the way he missed her. 4）The way =because
有时造句大全 [标签:栏目] ，有时造句大全 1、人们有时候是在做正确的决定，有时候是在证明决定正确。 2、家不只是房子，更重要的是人。有时候你会想方设法的离开他，有时候你却要不顾一切地回归他的怀抱。 3、再高的人有时也需踮足，再矮的人有时也需屈身。 4、人生啊，是这样不可预测，没有永恒的痛苦，也没有永恒的幸福，生活象流水一般，有时是那么平展，有时又是那么曲折。 5、使你惨败的，有时是你的朋友；使你成功的，有时是你的敌手。我们应学会感恩；更应该学会感怨。 6、这雨让我懂得了，父母是爱我么的，虽然有时很罗嗦，虽然有时脾气会很大，但是他们是爱我们的。 7、爱情是缘，就像瞌睡碰到了枕头，久旱遇到了雨天；爱情是思念，有时想着会失眠，有时梦里笑得甜。愿你爱情甜蜜！ 8、没有比时间更容易浪费的，同时没有比时间更珍贵的了，因为没有时间我们几乎无法做任何事。 9、一阵春风吹来，小草跳起欢快的舞蹈，有时舒展双臂，有时弯腰触地，有时左右摇晃，有时拥抱春风，真是姿态万千。 10、天鹅们同临一乱湖水，有时心怀幽情，咯守规行，有时也会意会神，雌雄彼此呵护。 11、月亮有时候象圆盘，有时候像西瓜，有时候象镰刀，有时候又像个光环。 12、炒股和赛车一样，有时候要停，有时候要冲，不要永远想着冲那样会撞死的。 13、最高明的骗子，可能在某个时刻欺骗所有人，也可能在所有时刻欺骗某些人，但不可能在所有时刻欺骗所有的人。 14、天空由于多变化，又是晴朗的天空是蓝蓝的，有时天空是黑沉沉的，有时天空像披上了雪白的衣服。 15、世界上没有原则，只有世故，没有法律，只有时势，高明的人同世故跟
时事打成一片，任意支配。 16、身体偶尔的背叛可以原谅，心灵的长期背叛不能容忍。有时要难得糊涂，有时要当机立断，这是婚姻的大智慧。 17、天空中的白云，有时像飞腾的巨龙，有时如威武的雄狮，有时又似奔腾的骏马。 18、小明正在做回家作业，只见他有时抓耳挠腮，有时写写画画，正急得满头大汗。 19、含羞草的**力是不可抵。有时，它的魅力甚至比牡丹还强，它的风格是典雅的，它的性格是独一无二的！含羞草，是真正值得我去爱的。 20、天上的云彩变化多端，有时候像一匹骏马在奔驰，有时候像一条狗在摆头摇尾，有时候像一只鸡在找食，有时候又像一头大象在喝水。 21、**常常走向自己的反面：吝啬有时导致挥霍，挥霍有时导致吝啬；我们常常经由软弱而达到坚强，经由怯懦而达到勇敢。 22、春雨，有时像飘渺的云烟，有时像如丝的针线，有时像密织的罗莎。 23、我的爸爸长着乌黑的头发，浓浓的眉毛下长着一双大大的眼睛，唱闪烁着智慧和机敏的神采，有时还使人感到几分诙谐和幽默。 24、对小钱不要过分去计较。金钱是生着羽翼的东西，有时它会自行飞去，有时必须将它放出去，才能带更多回来。 25、鸟们有时在天空中展翅高飞，有时停在树枝上婉转啼叫，有时在林间欢蹦乱跳。 26、我课余生活很丰富，有时看书，有时跳绳，有的画画。 27、春雨有时像牛毛，有时像花针，有时像细丝，像细小的珍珠，有时像细小的尘埃。 28、我很好奇，有时候我仿佛尝到了爱的味道，有时候爱很甜。 29、伤心有时是一种动力，失望有时是一种解脱，执迷不悟有时是一种磨练。 30、有时候相爱是一种无奈，有时候离开是另一种安排。为了爱你和你爱的人，请不要吸烟。 31、有时可能别人不在乎你，但你不能不在乎自己。 32、金翅雀唱着、跳跃着，有时也扑打着，像一群不知疲倦的孩子，给这幽
悲惨的近义词反义词和造句 导读：悲惨的近义词 悲凉(注释:悲哀凄凉：～激越的琴声。) 悲惨的反义词 幸福(注释:个人由于理想的实现或接近而引起的一种内心满足。追求幸福是人们的普遍愿望，但剥削阶级把个人幸福看得高于一切，并把个人幸福建立在被剥削阶级的痛苦之上。无产阶级则把争取广大人民的幸福和实现全人类的解放看作最大的幸福。认为幸福不仅包括物质生活，也包括精神生活;个人幸福依赖集体幸福，集体幸福高于个人幸福;幸福不仅在于享受，而主要在于劳动和创造。) 悲惨造句 1.一个人要发现卓有成效的真理，需要千百个人在失败的探索和悲惨的错误中毁掉自己的生命。 2.贝多芬的童年尽管如是悲惨，他对这个时代和消磨这时代的地方，永远保持着一种温柔而凄凉的回忆。 3.卖火柴的小女孩在大年夜里冻死了，那情景十分悲惨。 4.他相信，他们每个人背后都有一个悲惨的故事。 5.在那次悲惨的经历之后，我深信自己绝对不是那种可以离家很远的人。 6.在人生的海洋上，最痛快的事是独断独航，但最悲惨的却是回头无岸。 7.人生是艰苦的。对不甘于平庸凡俗的人那是一场无日无夜的斗
争，往往是悲惨的、没有光华的、没有幸福的，在孤独与静寂中展开的斗争。……他们只能依靠自己，可是有时连最强的人都不免于在苦难中蹉跎。罗曼·罗兰 8.伟大的心胸，应该表现出这样的气概用笑脸来迎接悲惨的厄运，用百倍的勇气来应付开始的不幸。鲁迅人在逆境里比在在顺境里更能坚强不屈。遇厄运时比交好运时容易保全身心。 9.要抓紧时间赶快生活，因为一场莫名其妙的疾病，或者一个意外的悲惨事件，都会使生命中断。奥斯特洛夫斯基。 10.在我一生中最悲惨的一个时期，我曾经有过那类的想法：去年夏天在我回到这儿附近的地方时，这想法还缠着我;可是只有她自己的亲自说明才能使我再接受这可怕的想法。 11.他们说一个悲惨的故事是悲剧，但一千个这样的故事就只是一个统计了。 12.不要向诱惑屈服，而浪费时间去阅读别人悲惨的详细新闻。 13.那起悲惨的事件深深地铭刻在我的记忆中。 14.伟大的心胸，应该用笑脸来迎接悲惨的厄运，用百倍的勇气来应付一切的不幸。 15.一个人要发现卓有成效的真理，需要千百万个人在失败的探索和悲惨的错误中毁掉自己的生命。门捷列夫 16.生活需要爱，没有爱，那些受灾的人们生活将永远悲惨;生活需要爱，爱就像调味料，使生活这道菜充满滋味;生活需要爱，爱让生活永远充满光明。
The way的用法及其含义（二） 二、the way在句中的语法作用 the way在句中可以作主语、宾语或表语： 1.作主语 The way you are doing it is completely crazy.你这个干法简直发疯。 The way she puts on that accent really irritates me. 她故意操那种口音的样子实在令我恼火。The way she behaved towards him was utterly ruthless. 她对待他真是无情至极。 Words are important, but the way a person stands, folds his or her arms or moves his or her hands can also give us information about his or her feelings. 言语固然重要,但人的站姿,抱臂的方式和手势也回告诉我们他(她)的情感。 2.作宾语 I hate the way she stared at me.我讨厌她盯我看的样子。 We like the way that her hair hangs down.我们喜欢她的头发笔直地垂下来。 You could tell she was foreign by the way she was dressed. 从她的穿著就可以看出她是外国人。 She could not hide her amusement at the way he was dancing. 她见他跳舞的姿势，忍俊不禁。 3.作表语 This is the way the accident happened.这就是事故如何发生的。 Believe it or not, that's the way it is. 信不信由你, 反正事情就是这样。 That's the way I look at it, too. 我也是这么想。 That was the way minority nationalities were treated in old China. 那就是少数民族在旧中
但是造句大全 1、你可以选择坚持，也可以选择放弃。没有对错。我是说对爱情来说，但是重要的是坚持你的选择。 2、我站在窗前，静静地欣赏着，天空中下着一群可爱的小精灵，虽然都是雪花，但是，你只要仔细观察，就会发现有许多形状、图案不同的雪花。 3、以和蔼亲切的态度说话并不会伤到舌头，但是，以愤怒和不悦的态度说话，则可能招致不幸。 4、有自己的人生观和价值观。出现问题可以忍让并寻求解决，但是触及原则，要保持自己原则。丧失原则会让你失去生活的目的。 5、秋天，柳树的叶儿慢慢变黄，继而掉落，让人感到无限凄凉。这时的风也变得恶了，是不是柳树姑娘和风姐姐吵架了呢？我不知道。但是，我见到，秋天到处是落叶缤纷，仿佛一场自然界的葬礼。 6、虽然他不聪明，但是他学习十分的用功。 7、想要获得成功，不像数学题一样，没有一个绝对的公式。但是如果知道一些原则的话，可以离成功更近一步。 8、生活过，而不会宽容别人的人，是不配受到别人的宽容的。
但是谁能说是不需要宽容的呢？ 9、虽然这是你亲手做的巧克力蛋糕，但是我不喜欢。 10、虽然万物好像逝去了，但是，你瞧！那火红的枫叶在树枝上摇摆着，就像是一大群顽皮的孩子在手拉手一起跳着欢快的舞蹈呢。有些枫叶因跳舞不慎，从树枝上跌了下来，瞬间化作了一只只飞舞的蝴蝶，在空中飞来飞去。 11、对于18岁的我们来说，有些事情的确会影响我们的一生，但是没有一件事能决定我们的一生！高考试卷是一把刻度不均匀的尺子：对于你自己来说，难题的分值不一定高。 12、她虽然态度冷淡，但是还算客气。 13、是人都有惰性，这是与生俱来的，但是我们后天可以改变这种惰性，谁改变的越多，谁就越成功。 14、我们前进的脚步虽然会让挫折绊住，但是我们要做生活的主人，不要坐在绊脚石的面前唉声叹气而耗尽了自己。让我们学会微笑着用有限的生命来超越无限的自己！我坚信，挫折让我更自信，更成功！ 15、夏天是个多姿多彩的世界。在这个季节，是最充满快乐的。也许，尽管很枯燥；也许，令人热得无法忍受；也许，植物们懒洋洋
定冠词the的用法： 定冠词the与指示代词this ,that同源,有“那(这)个”的意思,但较弱,可以和一个名词连用,来表示某个或某些特定的人或东西. (1)特指双方都明白的人或物 Take the medicine.把药吃了. (2)上文提到过的人或事 He bought a house.他买了幢房子. I've been to the house.我去过那幢房子. (3)指世界上独一无二的事物 the sun ,the sky ,the moon, the earth (4)单数名词连用表示一类事物 the dollar 美元 the fox 狐狸 或与形容词或分词连用,表示一类人 the rich 富人 the living 生者 (5)用在序数词和形容词最高级,及形容词等前面 Where do you live?你住在哪? I live on the second floor.我住在二楼. That's the very thing I've been looking for.那正是我要找的东西. (6)与复数名词连用,指整个群体 They are the teachers of this school.(指全体教师) They are teachers of this school.(指部分教师) (7)表示所有,相当于物主代词,用在表示身体部位的名词前 She caught me by the arm.她抓住了我的手臂. (8)用在某些有普通名词构成的国家名称,机关团体,阶级等专有名词前 the People's Republic of China 中华人民共和国 the United States 美国 (9)用在表示乐器的名词前 She plays the piano.她会弹钢琴. (10)用在姓氏的复数名词之前,表示一家人 the Greens 格林一家人(或格林夫妇) (11)用在惯用语中 in the day, in the morning... the day before yesterday, the next morning... in the sky... in the dark... in the end... on the whole, by the way...
一会一会造句大全 导读：本文是关于一会一会造句大全，如果觉得很不错，欢迎点评和分享！ 1、天上的云，颜色一会儿红通通的，一会儿半紫半黄的，一会儿金灿灿的。 2、科学家们好勤奋，一会儿观察自然现象，一会儿观察天气，一会儿做实验，一会儿相互提问，忙得不可开交。 3、小明一会儿打扫房间一会儿拖地，忙着手忙脚乱。 4、灯下的影子一会儿变长，一会儿缩短，可真有趣。 5、她起身站着想，神经质地一会儿把两手绞在一起，一会儿又松开。 6、不知在远处的什么地方，一辆汽车一会儿起动，一会儿熄火，一会儿起动，一会儿熄火，终于无声无息了。 7、火烧云的变化形态万千，一会儿就变成了一只可爱的小狗，一会儿又变成了一匹飞奔的骏马。 8、他一边走，头一边不停地来回转动，一会儿看看桥，一会儿看看桥后面的树。 9、健身房里，人们一会儿跑步一会儿做仰卧起坐，快乐的挥洒着汗水。 10、妈妈一会儿在家织毛衣，一会儿在厨房里做饭，我回到家很快就可以吃饭了。
11、白云飘飘一会儿东一会儿西一会儿左一会儿右。 12、秋高气爽的时节，一群大雁正往南飞，一会儿排成一字形，一会儿排成人字形。 13、他像只调皮的猴子，一会儿挠腮一会儿跳上跳下。 14、我做作业老是不专心，一会儿做作业，一会儿又吃东西，一会儿又看电视。 15、上课时他一会儿东张西望，一会儿交头接耳，结果什么也没有学到。 16、美丽的小燕子一会儿蜻蜓点水般掠过湖面，一会儿像离弦的弓箭飞向远方。 17、小蜜蜂一会我采集花粉回来，一会儿采信蜂蜜回来。 18、我也不明白是怎么回事，一会儿觉得丧气，一会儿又觉得轻快。 19、动物园里的猴子一会儿爬上，一会儿窜下，可调皮了。 20、数学课上，小明一会儿跟同桌小刚聊天，一会儿和邻桌小红聊天，最后被老师批评了。 21、小猫钓鱼很不专心，一会儿捉蜻蜓，一会儿捉蝴蝶。 22、野鸭悠闲自在地浮着，一会儿跌入水底，一会儿又立在浪尖上，像孩子在打秋千。 23、小花狗跑到楼顶上，一会儿大声叫，一会儿趴在那不动。 24、我好喜欢我的语文老师，读课文的时候一会儿大声，一会儿小声，一会儿神色飞扬。
“tｈｅwａｙ+从句”结构的意义及用法 首先让我们来看下面这个句子: Ｒead the foｌloｗｉnｇpassagｅａnd talkａbout ｉt wi ｔh your classmaｔes．Try tｏｔeｌl whaｔyｏu thiｎk ｏf Tom aｎd ｏｆｔhe ｗay the chiｌｄrenｔrｅated ｈim. 在这个句子中，ｔhe waｙ是先行词,后面是省略了关系副词that或in whｉch的定语从句。 下面我们将叙述“the ｗaｙ+从句”结构的用法。 1.tｈe wａy之后，引导定语从句的关系词是tｈat而不是hoｗ,因此，<<现代英语惯用法词典＞＞中所给出的下面两个句子是错误的：Ｔhｉs is ｔｈeｗayｈowｉtｈａppened. This is the way how he ａlwａｙs treats me. 2．在正式语体中,thaｔ可被in which所代替;在非正式语体中，ｔhat则往往省略。由此我们得到theｗay后接定语从句时的三种模式：1) tｈe ｗaｙ＋ｔhａt-从句２）the way +ｉn whiｃh－从句3) the ｗay +从句 例如：Ｔｈe way(in whiｃh ，thａt) ｔhｅｓｅｃomｒade ｓloｏｋａｔｐrobｌems is wrong．这些同志看问题的方法
不对。 Ｔｈｅｗａy(that ,ｉn ｗhiｃh)yoｕ’ｒe doinｇit is comple ｔeｌy crａzy.你这么个干法,简直发疯。 Wｅａdmiｒｅd him for thｅｗay ｉｎｗhｉch he fａｃeｓdiｆficultieｓ． Waｌlａｃe ａｎd Ｄarwiｎｇreed ｏn the way ｉｎwhi ｃh ｄｉfｆｅrｅnt forms ｏf life had ｂegｕn.华莱士和达尔文对不同类型的生物是如何起源的持相同的观点。 Thｉs is ｔｈe ｗaｙ（tｈａｔ) hｅdｉd it. I lｉkeｄｔhe waｙ(ｔhat) ｓｈeｏｒganized ｔhe ｍeetｉng． 3.theｗay(tｈat)有时可以与hｏw(作“如何”解）通用。例如： That’s tｈe ｗaｙ(tｈaｔ) shｅspoke. = Tｈat’s how shｅspoke.
用2个有时有时造句大全 用有时有时造句 1、天上的云彩变化多端，有时像一匹骏马在奔驰，有时像一条狗在摆头摇尾。 2、大白菜的价格有时高有时低。 3、小狗豆豆有时很温驯，有时又很顽皮。 4、在这个竞争激烈的社会里，我们有时会感到压力重重，而有时会感到生活很充实。 5、乐曲有时高昂，有时低沉。 6、鸟们有时在天空中展翅高飞，有时停在树枝上婉转啼叫，有时在林间欢蹦乱跳。 7、一分钟有时很长，有时又很短。 8、放学后，我有时直接回家，有时留在学校做作业，有时和同
学去打球。 9、课间休息时间，我有时去操场散步，有时和同学跳皮筋。 10、天上的月亮有时像一把镰刀，静静地挂在天空，懒得动一下；有时像一的大银盆，又圆又大的。 11、人生总是包含着五味杂陈，有时你会感到快乐，有时你会遭遇困难，有时你会期待梦想，有时也会充满感伤，必须每样都经历才算完整。 12、观看比赛的观众有时鼓掌为运动员加油，有时呐喊为选手鼓劲。 13、喷泉有时像百花怒放，有时像凤凰一飞冲天。 14、我的爸爸有时候严厉，有时候慈祥。 15、小猫咪有时淘气，有时可爱。 16、我有时可笑，有时悲伤。 17、在周末，小明有时在家里看电视，有时去爷爷奶奶家玩。 18、一群大雁往南飞，它们有时排成一字，有时排成人字。
19、天气千变万化，有时晴天，有时阴天。 20、人生际遇总是变幻莫测，有时如顺风行船，喜笑颜开；有时如雪中登山，凶险万分。 21、月亮有时像圆盘，有时像镰刀。 22、亮有时象圆盘，有是象镰刀，有时又象个光环。 23、我有时步行去学校，有时骑车去。 24、妈妈去外地出差了，我有时给妈妈打电话，有时我还给妈妈发短信。 25、这些天，她有时高兴，有时悲伤，有时又整天沉默无语，一定是发生了什么事。 26、生命如水，有时潺潺的流动，有时静静的盘桓。 27、仙女们在这片神奇的绿野上游戏，有时跳舞，有时歌唱，有时漫步，有时飞翔。 28、小鸟们有时在天空中展翅高飞，有时停在树枝上婉转啼叫。 29、他的课余生活丰富。有时打篮球，有时上网。
知己的近义词反义词及知己的造句 本文是关于知己的近义词反义词及知己的造句，感谢您的阅读！ 知己的近义词反义词及知己的造句知己 基本解释：顾名思义是了解、理解、赏识自己的人，如"知己知彼，百战不殆";更常指懂你自己的挚友或密友，它是一生难求的朋友，友情的最高境界。正所谓:"士为知己者死"。 1.谓了解、理解、赏识、懂自己。 2.彼此相知而情谊深切的人。 【知己近义词】 亲信，好友，密友，心腹，挚友，深交，相知，知交，知友，知心，知音，石友，老友，至友 【知己反义词】 仇人敌人陌路 【知己造句】 1、我们想要被人爱、想拥有知己、想历经欢乐、想要安全感。 2、朋友本应是我们的亲密知己和支持者，但对于大多数人来说，有一些朋友比起帮助我们，更多的却是阻碍。 3、那么，为什么你就认为，随着年龄的增长，比起女人来男人们的知己和丰富的人际关系更少，因此一般容易更孤独呢？ 4、他成了我的朋友、我的知己、我的顾问。 5、无论在我当州长还是总统的时候，布鲁斯都是我的密友、顾问和知己。他这样的朋友人人需要，也是所有总统必须拥有的。
6、波兰斯基有着一段声名卓著的电影生涯，也是几乎所有电影界重要人物们的挚友和同事，他们是知己，是亲密的伙伴。 7、搜索引擎变成了可以帮追我们的忏悔室，知己，信得过的朋友。 8、这样看来，奥巴马国家安全团队中最具影响力的当属盖茨了――但他却是共和党人，他不会就五角大楼以外问题发表看法或成为总统知己。 9、我们的关系在二十年前就已经和平的结束了，但在网上，我又一次成为了他精神层面上的评论家，拉拉队，以及红颜知己。 10、这位“知己”，作为拍摄者，站在距离电视屏幕几英尺的地方对比着自己年轻版的形象。 11、父亲与儿子相互被形容为对方的政治扩音筒、知己和后援。 12、这对夫妻几乎没有什么至交或知己依然在世，而他们在后纳粹时期的德国也不可能会说出实话的。 13、她把我当作知己，于是，我便将她和情人之间的争吵了解得一清二楚。 14、有一种友谊不低于爱情；关系不属于暖昧；倾诉一直推心置腹；结局总是难成眷属；这就是知己！ 15、把你的治疗师当做是可以分享一切心事的知己。 16、莉莉安对我敞开心胸，我成了她的知己。 17、据盖洛普民意调查显示，在那些自我认同的保守党人中，尽管布什仍维持72%支持率，但他在共和党领导层中似乎很少有几位知
表示“方式”、“方法”，注意以下用法： 1.表示用某种方法或按某种方式，通常用介词in(此介词有时可省略)。如： Do it (in) your own way. 按你自己的方法做吧。 Please do not talk (in) that way. 请不要那样说。 2.表示做某事的方式或方法，其后可接不定式或of doing sth。 如： It’s the best way of studying [to study] English. 这是学习英语的最好方法。 There are different ways to do [of doing] it. 做这事有不同的办法。 3.其后通常可直接跟一个定语从句(不用任何引导词)，也可跟由that 或in which 引导的定语从句，但是其后的从句不能由how 来引导。如： 我不喜欢他说话的态度。 正：I don’t like the way he spoke. 正：I don’t like the way that he spoke. 正：I don’t like the way in which he spoke. 误：I don’t like the way how he spoke. 4.注意以下各句the way 的用法： That’s the way (=how) he spoke. 那就是他说话的方式。 Nobody else loves you the way(=as) I do. 没有人像我这样爱你。 The way (=According as) you are studying now, you won’tmake much progress. 根据你现在学习情况来看，你不会有多大的进步。 2007年陕西省高考英语中有这样一道单项填空题： ——I think he is taking an active part insocial work. ——I agree with you_____. A、in a way B、on the way C、by the way D、in the way 此题答案选A。要想弄清为什么选A，而不选其他几项，则要弄清选项中含way的四个短语的不同意义和用法，下面我们就对此作一归纳和小结。 一、in a way的用法 表示：在一定程度上，从某方面说。如： In a way he was right.在某种程度上他是对的。注：in a way也可说成in one way。 二、on the way的用法 1、表示：即将来(去)，就要来(去)。如： Spring is on the way.春天快到了。 I'd better be on my way soon.我最好还是快点儿走。 Radio forecasts said a sixth-grade wind was on the way.无线电预报说将有六级大风。 2、表示：在路上，在行进中。如： He stopped for breakfast on the way.他中途停下吃早点。 We had some good laughs on the way.我们在路上好好笑了一阵子。 3、表示：(婴儿)尚未出生。如： She has two children with another one on the way.她有两个孩子，现在还怀着一个。 She's got five children，and another one is on the way.她已经有5个孩子了，另一个又快生了。 三、by the way的用法
出现造句大全 导读：本文是关于出现造句大全，如果觉得很不错，欢迎点评和分享！ 1、你们同学白天上课时的音量比晚自习时小多了，这是极不正常的，如果这种情况出现在野生动物身上，那就意味着大的自然灾害即将到来。 2、我不是带着将失败的情绪走进赛场的。我回来的时候满怀信心，我相信仍然能够有所作为。如果我只是坐在这里听别人告诉我不能复出，那我肯定不会出现在这里。 3、小贝，我去向琛哥要我的明天，全身上下最值钱的东西，我都留给你，谢谢你给了我一个值得活下来的理由，为了这个理由，我愿意用生命去赌我们的未来，如果没回来，不要来找我，就当我从来没出现在你的生命里。 4、夏天的晚上，太阳太劳累了，又钻回了云朵被窝去睡觉了，星星和月亮，养足了精神，出现在了天空，发出动人的光芒，蟋蟀和没有睡觉的青蛙知了，在草丛里池塘里大树上唱着歌。 5、曾经也有一个笑容出现在我的生命里，可是最后还是如雾般消散，而那个笑容，就成为我中心深深埋藏的一条湍急河流，无法泅渡，那河流的声音，就成为我每日每夜绝望的歌唱。 6、知道自己出现在别人的梦里是很让人开心的事，这能证明你的存在，而且在某种程度上，还可以证明你在别的地方也具有实体和
价值。 7、正当我高兴的流下泪水时，闹钟忽然响了，它把我从梦中拉出来，才知道原来是场梦，但我坚信，在不久的将来，我一定会出现在奥运会上，为中国队努力拼搏。 8、世界上没有谁选择谁，只有谁遇到谁，所以我不选ABCD，只看着时间刚刚好的时候，出现在你面前，然后，牵着手，去哪里那里，然后，就是一辈子。 9、只见月亮像一个害羞的姑娘，羞答答地从一片乌云背后伸出半个脑袋，偷偷地向下窥探，发现没有什么动静，一扭身，出现在天空中，天空就好像出现了一盏明亮的灯，周围的乌云被白色的月光照着。 10、情痴先生，多谢你的痴情。对于我在你的梦中出现这件事，令我十分震惊。你没有征求我的同意，便让我出现在你的梦里，侵犯了我的自由，我保留所有法律上追究的权利跟赔偿。还有，你没有告诉我在梦里你对我做了什么？ 11、到19世纪70年代，那些实际生活中的作奸犯科者、道德沦丧者，竟然已经堂而皇之地出现在歌舞表演中，这一幕与如今八卦电视节目何其相似，文化垃圾商品的真人版而已！ 12、以前只是一种经历与感觉，而不是证据，不需要为以前的喜欢付出现在或以后的责任。不要揪住以前的事情不放。现在的事实比以前的回忆更有实效性与说服力。 13、法律不等于正义，这是一种非常不完美的机制，如果你按
仿照的近义词反义词和造句 仿照的近义词 【仿制解释】：仿造：～品 【模仿解释】：个体自觉或不自觉地重复他人的行为的过程。是社会学习的重要形式之一。尤其在儿童方面，儿童的动作、语言、技能以及行为习惯、品质等的形成和发展都离不开模仿。可分为无意识模仿和有意识模仿、外部模仿和内部模仿等多种类型。 仿照的反义词 【独创解释】：独特的创造：～精神ㄧ～一格。 仿照造句 一、老师让我们仿照黑板上的图画一幅画。 二、仿照下面的句式，以“只有”开头，写一结构与之相似的复句。 三、仿照例句的句子，在下面两句的横线上补写相应的内容。 四、仿照例句，以“记忆”或“友情”开头，另写一句话。 五、仿照下面两个例句，用恰当的词语完成句子，要求前后语意关联。 六、仿照开头两句句式，通过联想，在后面两句横线上填上相应的词语。 七、仿照所给例句，用下面的词展开联想，给它一个精彩的解释。 八、仿照例句，以“你”开头，另写一个句子。 九、仿照下列句式，续写两个句子，使之与前文组成意义相关的.句子。 十、我们也仿照八股文章的笔法来一个“八股”,以毒攻毒,就叫做八大罪状吧。
十一、仿照例句，任选一种事物，写一个句子。 十二、仿照下面一句话的句式和修辞，以“时间”开头，接着写一个句子。 十三、仿照例句，以“热爱”开头，另写一句子。 十四、仿照下面的比喻形式，另写一组句子。要求选择新的本体和喻体，意思完整。 十五、根据语镜，仿照划线句子，接写两句，构成语意连贯的一段话。 十六、仿照下面句式，续写两个句式相同的比喻句。 十七、自选话题，仿照下面句子的形式和修辞，写一组排比句。 十八、仿照下面一句话的句式，仍以“人生”开头，接着写一句话。 十九、仿照例句的格式和修辞特点续写两个句子，使之与例句构成一组排比句。 二十、仿照例句，另写一个句子，要求能恰当地表达自己的愿望。 二十一、仿照下面一句话的句式，接着写一句话，使之与前面的内容、句式相对应，修辞方法相同。 二十二、仿照下面一句话的句式和修辞，以“思考”开头，接着写一个句子。 二十三、仿照下面例句，从ABCD四个英文字母中选取一个，以”青春”为话题，展开想象和联想，写一段运用了比喻修辞格、意蕴丰富的话，要求不少于30字。 二十四、仿照下面例句，另写一个句子。 二十五、仿照例句，另写一个句子。 二十六、下面是毕业前夕的班会上，数学老师为同学们写的一句赠言，请你仿照它的特点，以语文老师的身份为同学们也写一句。
The way的用法及其含义（一） 有这样一个句子：In 1770 the room was completed the way she wanted. 1770年，这间琥珀屋按照她的要求完成了。 the way在句中的语法作用是什么？其意义如何？在阅读时，学生经常会碰到一些含有the way 的句子，如：No one knows the way he invented the machine. He did not do the experiment the way his teacher told him.等等。他们对the way 的用法和含义比较模糊。在这几个句子中，the way之后的部分都是定语从句。第一句的意思是，“没人知道他是怎样发明这台机器的。”the way的意思相当于how；第二句的意思是，“他没有按照老师说的那样做实验。”the way 的意思相当于as。在In 1770 the room was completed the way she wanted.这句话中，the way也是as的含义。随着现代英语的发展，the way的用法已越来越普遍了。下面，我们从the way的语法作用和意义等方面做一考查和分析： 一、the way作先行词，后接定语从句 以下3种表达都是正确的。例如：“我喜欢她笑的样子。” 1. the way+ in which +从句 I like the way in which she smiles. 2. the way+ that +从句 I like the way that she smiles. 3. the way + 从句(省略了in which或that) I like the way she smiles. 又如：“火灾如何发生的，有好几种说法。” 1. There were several theories about the way in which the fire started. 2. There were several theories about the way that the fire started.
一下子造句大全 导读：本文是关于一下子造句大全，如果觉得很不错，欢迎点评和分享！ 1、幼稚的眼睛常常看不清楚。小鸟怀着热烈的希望展翅向天空飞去，但一下子就碰着铁丝网落了下来。 2、瞧！当我给小金鱼喂鱼食时，有的鱼迫不及待地跳上来；好像鲤鱼跃龙门；有的鱼摇着头，翘着尾巴慢慢地吃鱼食；还有的鱼很胆小，专门吃别人剩下的。整个鱼缸一下子因为我这几个鱼食而沸腾了。 3、忽然，一颗豆大的雨点恰好重重地打在了这嫩生生的细条上。它猛地一颤，弯下了腰。我正担心它被打折了，不想它马上像摆脱了重担似的，一下子直起了腰，晃了两晃，又像先前一样，直立在树的断口处。 4、俗话说“六月的天，娃娃的脸说变就变。”刚刚还是晴空万里，一下子就变得阴沉沉的，乌云布满了整个天空，风也吹起来了，吹着树叶沙沙作响。不远处传来几声雷响，仿佛正告诉人们要下雷雨啦！ 5、小偷认为自己的谎言天衣无缝，一点破绽都没有，可**一下子就看出了他是坏人。 6、花瓣一片一片地绽放，花朵也不是一下子全开的，我明白昨天是与今天紧密相连的，好高兴。
7、过了一会儿，雨变小了，微风吹来，雨帘斜了，像一根根针线似的刺向草木、墙壁。雨落在小草上，看，草儿轻轻地在微风中摇动，雨珠顺着它那翠绿的茎滚下来，有一滴一下子钻到土里，又一滴钻到小草的嘴里，找不着了。 8、妈妈的手也是温柔的手。每当我生病了，妈妈便用她的手无微不至地照料着我，我感觉病一下子好了许多，心中无比温暖。每当我受到挫折感到委屈时，妈妈也会用她那双手轻轻拍拍我的肩膀，鼓励我，给我勇气和力量。 9、只见他嘴张得像箱子口那么大，一下子就愣住了，接着他就咽了两三口唾沫，好像是嗓子里发干似的。 10、突然门外传来嘈杂声，接着是敲门声，一下子又给大叫声所覆盖。我连忙去外面看看发生什麽事，当我看到我家门外的几个人时，我呆在那儿了，那陌生又熟悉的脸庞，都带着亲切的微笑，我不禁也露出微笑，请他们进家中。 11、早上起雾了，小溪里最浓，什么也看不见，我便高兴了，走下去，哪知它们一下子便将我拥围起来。慢慢地太阳升起，穿过树林，溪边的小草显得更加鲜嫩油滑了。我坐在木屋窗口下读书，让这一切陪我畅想。 12、蒋百里在宜山的南边路上，汽车坏了，他受了一下子凉，就有一点半身不遂的样子。 13、在座的人全都受到他影响，一下子也都变得严肃起来。就像钢水突然落到冷水里，一下子整个脸都凝固了。
暗示的近义词和反义词[暗示的近义词反义词和造句] 【暗示解释】：用含蓄、间接的方式使他人的心理、行为受到影响。下面小编就给大家整理暗示的近义词，反义词和造句，供大家学习参考。 暗示近义词 默示 示意 暗指 暗意暗示反义词 明说 表明 明言暗示造句 1. 他的顶级助手已经暗示那可能就在不久之后，但是避免设定具体的日期。 2. 一些观察家甚至暗示他可能会被送到了古拉格。 3. 要有积极的心理暗示，达成目标的好处不够充分，画面不够鲜明那它对你的吸引力就不够强烈，你就不易坚持下去! 4. 不要经常去试探男人，更不要以分手做为威胁，当你经常给他这种心理暗示，他的潜意识就会做好分手的打算。 5. 向读者暗示永远不必为主角担心，他们逢凶化吉，永远会吉人天相。 6. 约她一起运动，不要一下跳跃到游泳，可以从羽毛球网球开始，展示你的体魄和运动细胞，流汗的男人毫无疑问是最性感的，有意无意的裸露与靠近，向她暗示你的运动能力。 7. 正如在上一个示例所暗示的，只有在这些对象引用内存中同一个对象时，它们才是相同的。 8. 渥太华此时打出中国牌，巧妙地对美国这种行为作出警告，暗示美国如果长期这样下去的话，美国将自食其果。 9. 团长用震撼人心的嗓音喊道，这声音对他暗示欢乐，对兵团暗示森严，对前来校阅阅兵的首长暗示迎迓之意。 10. 渥太华此时打出中国牌，巧妙地对美国这种行为作出警告，暗示美国如果长期这样下去的话，美国将自食其恶果。 11. 我们需要邀请用户，为他们描述服务产品有多少好，给他们解释为什么他们需要填那些表单并且暗示他们会因此得到利益的回报。 12. 她对暗示她在说谎的言论嗤之以鼻。 14. 在力士参孙中，整首诗都强烈暗示着弥尔顿渴望他自己也能像参孙一样，以生命为代价，与敌人同归于尽。 15. 戴维既然问将军是否看见天花板上的钉子，这就暗示着他自己已看见了，当将军做了肯定的答复后，他又说自己看不见，这显然是自相矛盾。 16. 自我暗示在我们的生活中扮演着重要角色。如果我们不加以觉察，通常它会与我们作对。相反地，如果你运用它，它的力量就可以任你使唤。 17. 纳什建议太阳招兵买马，暗示去留取决阵容实力。 18. 同时，还暗示了菊既不同流俗，就只能在此清幽高洁而又迷漾暗淡之境中任芳姿憔悴。 19. 学习哲学的最佳途径就是将它当成一个侦探故事来处理：跟踪它的每一点蛛丝马迹每一条线索与暗示，以便查出谁是真凶，谁是英雄。 20. 不要经常去试探你的伴侣，更不要以分手做为威胁，试探本身就是一种不信任，当
一年级有造句大全 [标签:栏目] ，一年级有造句大全 1、一个人有再大的权力、再多的财富、再高的智慧，如果没有学会去关怀别人、去爱别人，那他的生命还有多少意义呢！ 2、做任何事情都有一个临界点，可惜很多人倒在临界点前，离成功已经不远了！ 3、这些野**都是浅蓝色的。我仔细数了数发现它们都有十六片细长的小花瓣，花瓣整齐地围着浅黄色的花芯。 4、下课了，同学们在操场上有踢毽子的，有跳绳的，也有打篮球的。 5、迎春花的花瓣是嫩黄色的，分为两层，都有六片花瓣。用鼻子一闻，啊，你会闻到一片芳香，沉浸在花香之中。 6、人生就像一出自导自演的戏剧，随时都有可能改变里面的戏节，只不过到头来的结局不同了。 7、一切伟大的行动和思想，都有一个微不足道的开始。 8、银杏是木本植物，它们年复一年地生长着。在每一个季节中，他们都有自己独特的变化。 9、人格是信用的基础，逆境往往使人有所建树，人都有长处与短处，看人看长处短处不看。 10、抹布，工作平凡的无人问津，但默默无闻地给人以美的清净。只要思想是有出息的，任何工作都有崇高的意义。 11、**的形状很特别，有像过年时放的烟花、有像妈妈打毛线衣的毛线团、还有的像少女头上的卷发。美丽极了！ 12、冬天下了一场大雪，雪地里有活蹦乱跳的小狗，有开心的在地上画画的猫儿，还有几只小鸡在高兴地走来走去。 13、爸爸有时像挺拔的松树，有时像巍峨的大山。 14、蜻蜓有时在高空飘落，像一架飞机在飞，有时在树上飞，仿佛在思考。 15、男人要有钱，和谁都有缘，女人长的好，不怕没人找。 16、千万人的失败，都有是失败在做事不彻底，往往做到离成功只差一步就
终止不做了。 17、这个乌烟瘴气的行列就像这样行进着，有人喝啤酒，有人抽烟斗，有人哇哇地唱，还有人没完没了地装出椎心泣血的样子。 18、任何问题都有解决的办法，无法可想的事是没有的，要是你果真弄到了无法可想的地步，那也只能怨自己是笨蛋、是懒汉。 19、每个宿舍都有一个磨牙的，一个说梦话的，一个打呼噜的，一个睡很晚纵观全场的。 20、星星闪烁的星空，月亮有时像圆盘有时像香蕉。 21、每个人的一生都有许多梦想，但如果其中一个不断搅扰着你，剩下的就仅仅是行动了。 22、每个人都有优势，由自己发现并加以利用便成为老板；由他人发现并加以利用便成为千里马。 23、是人都有惰性，这是与生俱来的，但是我们后天可以改变这种惰性，谁改变的越多，谁就越成功。 24、手，谁都有一双。是妈妈，赋与我们生命；是妈妈，呵护我们成长；是妈妈，教育我们人生的道理。 25、我要离家了，耳旁有妈妈絮絮的唠叨，肩上更有爸爸期盼的目光。 26、猫的性格实在有些古怪。说它老实吧，它的确有时候很乖。 27、五月的天气有时像个乖宝宝，风和日丽，暖意融融；有时仿佛又像个淘气的小坏蛋，狂风大作，暴雨倾盆。 28、那翠绿的颜色，明亮地照耀着我们的眼睛，似乎每一片绿叶上都有一个新的生命在颤动。 29、任何科学上的雏形，都有它双重的形象：胚胎时的丑恶，萌芽时的美丽。 30、每一朵都有它自己的姿势，有的张着半张脸，有的少几瓣花瓣，还有的像小孩子在树枝上跳舞。 31、做学问要在不疑处有疑，做人要在有疑处不疑。 32、在有骨气的人看来，送礼的要是变了心，礼物虽贵，也会失去了价值。 33、不敢去刻意追求什么，只是希望所有美好的开端都有一个美好的结局，一如这春夜长长远远。