International Journal of Intelligence Science, 2011, 1, 35-45 doi:10.4236/ijis.2011.12005 Published Online October 2011 (http://www.SciRP.org/journal/ijis) Copyright © 2011 SciRes. IJIS A New Algorithm for the Acquisition of Knowledge from Scientific Literature in Specific Fields Based on Natural Language Comprehension Hui Wei, Zhilong Dai Department of Computer Science and Engineering, Fudan University, Shanghai, Chin a E-mail: weihui@fudan.edu.cn Received September 11, 2011; revised October 1, 2011; accepted October 8, 2011 Abstract The acquisition of knowledge and the representation of that acquisition have always been viewed as the bot- tleneck in the construction of knowledge-based systems. The traditional methods of acquiring knowledge are based on knowledge engineering and communication with field experts. However, these methods cannot produce systematic knowledge effectively, automatically construct knowledge-based systems, or benefit knowledge reasoning. It has been noted that, in specific professional fields, experts often use fixed patterns to describe their expertise in the scientific articles that they publish. Abstracts and conclusions, for example, are key components of the scientific article, containing abundant field knowledge. This paper suggests a method of acquiring production rules from the abstracts and conclusions of scientific articles in specific fields based on natural language comprehension. First, the causal statements in article abstracts and conclu- sions are extracted using existing techniques, such as text mining. Next, antecedence and consequence frag- ments are extracted using causal template matching algorithms. As the final step, part-of-speech-tagging production rules are automatically generated according to a syntax parsing tree from the speech pair se- quence. Experiments show that this system not only improves the efficiency of knowledge acquisition but also simultaneously generates systematic knowledge and guarantees the accuracy of acquired knowledge. Keywords: Knowledge Acquisition, Knowledge Representation, Text Mining, Production Rules 1. Introduction Knowledge acquisition (KA) has long been perceived as the most difficult bottleneck in the construction of know- ledge-based systems (KBS). Over the past decade, know- ledge engineers have argued over the best means of con- structing an effective and reliable KBS. Many research- ers view knowledge acquisition as critical [1-5]. For example, Edward Albert Feigenbaum once said, “There are many important problems to be solved in the use, representation, and acquisition of the knowledge. Of them all, the knowledge acquisition is the most important and critical bottleneck” [6]. It is imperative to build automated knowledge acquisition systems. In specific professional fields, conclusive knowledge and experience are customarily represented concisely in normalized scientific language when stored in text form. Consequently, massive amounts of specialized field knowledge can be obtained from scientific articles. This can then be used to build knowledge-based systems. From the perspective of knowledge engineering, state- ments in scientific articles are usually verified, explicit, and expressed in a conclusive and conceptive style. All of these traits closely resemble the requirements of KBS. Additionally, in scientific articles in specific professional fields, these statements are often written in a relatively fixed style that makes them easier to extract. The abstract and conclusion sections of most scientific articles contain useful field knowledge. Labeling the summary statements in abstracts and conclusions, then, may greatly improve the effectiveness and accuracy of knowledge acquisition. These statements tend to have significant causal relationships, which makes them easier to represent using automatic production rules and easier to translate into field knowledge that can be used by KBS. The implementation of this idea is based on the following proven scientific practices: 1) There already exist many NLU and text mining
H. WEI ET AL. 36 techniques that can be used to extract knowledge en masse from scientific articles. The issue for knowledge engineers is how to formalize the explicit knowledge in these articles and the difficulty is how to achieve auto- matic conversion. Existing data mining and analysis techniques, such as text retrieval, text association analy- sis, and so on, provide effective algorithms to implement the transformation [7]. 2) The material processed in this paper focuses on a specific scientific field. In addition, the statements in abstracts and conclusions are often short, conclusive, and designed to focus on a single topic. Therefore, priori knowledge can be used as a guide during the text mining process to improve effectiveness of KA. In the experi- ments described in this paper, the range of both field knowledge and topic has been limited in order to im- prove accuracy. 3) The production rule is a mature method of know- ledge representation with strong expression ability. It is easily combined with the Drools engine mechanism that is used as inference device in the KBS proposed in this paper. The method described in this paper takes texts that contain abundant field knowledge, such as professional papers, as input. After labeling, casual statements are then transformed into production rules that can be exe- cuted by machine. Transformation between text know- ledge and production rules can be achieved automatically through necessary and effective manual interventions, which improve the degree of typical KA automation and realize computer-aided KA. 2. Related Work As the data show, knowledge acquisition techniques are optimized toward three main goals: 1) To improve the efficiency of knowledge acquisition, which means acquiring knowledge from field experts effectively or adopting semi-automatic knowledge ac- quisition methods. 2) To extend the scope of knowledge that can be ac- cessed and improve the automation of knowledge acqui- sition. 3) To simplify the process of conversion from ac- quired knowledge to production rules that can be exe- cuted by machines. As far as improving KA efficiency is concerned, re- cent studies have mainly focused on two issues. First, there has been the development of methods and assistant tools that shorten the communication cycle with field experts and guarantee the accuracy of acquired know- ledge. These include the famous Repertory Grid method of delimiting and identifying field objects, the integrated model of KA, the MRM method, and the KADs model- based method, a comprehensive methodology for KA from multiple knowledge sources [8-12]. Second, there has been the development of techniques that improve KA automation and shorten the acquisition cycle [13-16]. These include pattern recognition, machine learning, and text mining techniques, such as the automatic KA me- thod based on inductive learning, the incremental ap- proach to discovering knowledge from text, and know- ledge discovery [17-19]. All of these methods have their own inevitable disadvantages. KA methods that use pat- tern reorganization or machine learning focus primarily on implicit rules contained in mass data and are suitable only for processing data text [20]. The rules obtained are still untested and have to be verified by field experts. KA methods based on text mining are often designed to process large amounts of text and the knowledge ob- tained is generally inadequate. Additionally, they are always designed to investigate objects and object hierar- chies [21,22]. There is great gap between the knowledge rules and production rules used in KBS. Compared to traditional KA methods, the method suggested in this paper processes causal statements fo- cused on one specific field. The algorithm proposed in this paper, natural language comprehension for rule ex- traction (NLCRE), is designed to obtain IF-THEN rules from scientific articles by labeling the causal statements in those, extracting antecedence and consequence using causal templates, and generating rules automatically us- ing a syntax parsing tree. 3. Architecture of Knowledge Based System Based on NLCRE Using the NLCRE algorithm, we developed a new know- ledge-based system with a knowledge base that can ex- pand incrementally. The overall architecture of the KBS is shown in Figure 1. The KBS architecture is comprised of four modules: 1) Causal-statement-finding module (white) The function of the causal-statement-finding module is to extract causal statements from the abstracts and con- clusions of scientific articles. 2) Production-rule-generation module (gray) This module is responsible for generating rules from causal statements. Pre-definition templates are con- structed based on the characters of causal statements. The antecedence and consequence portions of production rules can be obtained by using templates matching algo- rithms. Then production rules are then generated by the part-of-speech-tagging and syntax-parsing tree based on natural language comprehension. After being filtered and refined manually, these production rules are added to the Copyright © 2011 SciRes. IJIS
H. WEI ET AL. Copyright © 2011 SciRes. IJIS 37 Text analysis Fill algorithm Text labeling Pre-defined templates Production rule type Knowledge module, production rule system Text input device Atom operation (predication, function definition) Specialized format language table Inference device Pseudo production rule Manual filter and refine Sub-field set KBS management Production syntax rules Figure 1. Archit ecture of K nowledge-Based Sy stem Based on NL CRE. 4. NLCRE Production Rule Generation Algorithm KBS. 3) Field knowledge management module (purple) Production rules generated from the scientific articles or acquired from field experts are stored and managed in the field KBS. Knowledge management includes know- ledge update, knowledge addition, knowledge deletion, and other processes. In order to eliminate redundancy and conflict, these operations must be verified before they are performed. The presentation of field knowledge contained in the natural language of scientific papers is quite different from the requirements of knowledge representation in KBS. Translating natural language to formal language requires an essential change translates labeled text into production rules. This change benefits the process of forming clear knowledge hierarchies and structures in knowledge systems. Furthermore, it improves the exten- sibility of knowledge and provides an effective means of increasing the efficiency of reasoning. 4) Inference device (green) Knowledge reasoning must be considered when de- signing a KBS. In this paper, the Drools java rules en- gine is the main tool employed toward knowledge rea- soning. Drools is an open-source business rule engine and an enhanced java language implementation system based on the RETE algorithm [23]. The NLCRE algorithm can be divided into four stages, as shown in Figure 2. These stages will now be des- cribed in detail. In addition to traditional functions such as knowledge storage, management, and reasoning, the KBS presented in this paper can also make use of production rules di- rectly extracted from great numbers of scientific papers and alter reasoning results accordingly. Because the production rules can be obtained automatically, the de- gree of KBS automation is improved and knowledge acquisition time is reduced relative to traditional methods of knowledge acquisition. In this way, computer-assisted knowledge acquisition processes can be realized. 4.1. Stage 1: Labeling of Causal Statements The aim of this stage is to mine casual statements from great numbers of field-specific articles. This paper mainly processes the abstracts and conclusions of scien- tific papers because they are contain the most useful field knowledge. Text extraction of casual statements takes advantage of NLU and data mining technologies and uses existing
H. WEI ET AL. Copyright © 2011 SciRes. IJIS 38 Figure 2. Production rule conversion. field knowledge for guidance. This stage comprises the following steps: 1) Extract abstracts and conclusions from the scientific articles in question, then label and extract their conclu- sive sentences. 2) Label field glossaries such as “NEPE propellant,” “metal powder,” “oxidants,” “energy,” and so on. Cur- rent effective labeling techniques can be adopted in this step. These include Tregex, which is used to search and operate tree data structure. 3) Label field operation terms, such as “increase,” “de- crease,” and “add.” These operation terms will take the glossaries defined in the previous step as operation ob- jects. 4) Identify causal words and use predefined causal templates with the terms and glossaries to extract causal statements from the conclusive sentences. Causal statements can then be extracted from scientific articles and these sentences can be translated into pro- duction rules.
H. WEI ET AL.39 4.2. Stage 2: Elimination of Noise After stage 1, some noisy words that do not contain knowledge will inevitably be included in the causal statements. These noisy words affect the accuracy of the production rules and should be eliminated before con- verting the field causal statements to production rules. To this end, noise elimination templates must be constructed according to field and language characters. Because the labeled contents are often conclusive sentences that al- ways have fixed patterns and locations, it is practical to locate most of the similar noisy words with high occur- rences and separate them from the labeled causal state- ments using fuzzy matching algorithms. Table 1 shows key words often labeled as noisy content. 4.3. Stage 3: Extraction of Antecedence and Consequen c e Phras e s Once the noisy words have been eliminated, it is crucial to convert the remaining labeled content into rough pro- duction rules describable as “IF (antecedence) THEN (consequence)” according to causal templates. This en- sures the accuracy of the whole conversion, so it is im- portant to construct effective causal templates with high accuracy. In field-specific scientific papers, causal rela- tionships are usually presented in fixed patterns. Conver- sion templates meant to translate causal statements to production rules can be constructed by summarizing common presentation patterns. Detailed information is provided in Appendix. In the Chinese language, some words are used to indi- cate casual relations explicitly, such as “for,” “because,” “so,” and so on. For sentences that have obvious casual relationships, it is quite easy to identify antecedents and consequents fill in “IF (antecedent) THEN (consequent)” rules. For example, the causal statement “B is the result of A” can be converted to “IF (A) THEN (B).” However, sometimes causal statements are more complicated. In- tricate “and” and “or” relationships may exist among many conditions and results. Conditions and results may even intersect. Conversion templates must be designed to deal with this complicated reality. They must also be Table 1. Key noise content words. Common noise words preliminary view, studies suggest that, studies show that, experimental results show that, results show that, studies discover that, preliminary discover, through this experiment, through above analysis that we can see , … shows, discover … by computing, discover … by experiments, discover … by studies, computing results show, experiments show, discover … by ana- lyzing …, as experimental results indicate, in sum- mary, etc. tested and refined according to experiment results. This improves the effectiveness of the conversion from causal statements to production rules. After this stage, causal statements described in natural language are formatted into “IF (antecedent) THEN (consequent)” structures. The antecedents and conse- quents are refined and formatted to predicate logic to generate production rules correctly. The semantics of the production rules must be consistent with the causal statements. Integrity must be preserved during conver- sion. 4.4. Stage 4: Generation of Production Rules The antecedent and consequent of each production rule must be separated from the causal statement. However, they are still represented in natural language and cannot be used to form production rules. This kind of know- ledge, then, must be converted into first-order predicate expressions that have clear semantics and are suitable for use in production rules. In this stage the parts of speech (POS) of the antecedent and consequent are tagged. Next, POS tagging results are analyzed according to a parsing tree (Figure 3) and a bottom-up merge is carried out. At the same time, the order of the words and phrases in each antecedent and consequent are changed to eventually form predicate verbs with structures such as “predicate (object, value).” The POS sequences of the antecedents and cones- quents are produced through this POS-tagging. Then verb-object structures contained in the antecedents and consequents can be found out. Finally, production rules are generated by translating the verb-object structures into first-order predicate expressions. Generally, predi- cate (object, value) structure is the basic element of pro- duction rules. The process of transforming antecedents and consequents into production rules can be focused on generating predicate (object, value) structures. In this paper, a parsing tree, which is summarized from scientific articles from one specific field, is used as guide to analyze POS sequences and transforming them into predicate structure elements. The conversion process is made up of the following steps: 1) Mark the POSes of the antecedents and consequents and represent these in speech pairs (word, speech tag). For example, an antecedent that literally says “increase content of metal fuel AL” and may be represented with a speech pair list including (increase, V), (metal fuel, DV), (AL, DC), (of, “of”), (content, DV). There are eight POS types in this system, as shown in Table 2 . 2) Merge the appropriate speech pairs into noun ph- rases (NPs) according to the parsing tree (Figure 3) and mark the speech tag as NP. For example, according to Copyright © 2011 SciRes. IJIS
H. WEI ET AL. Copyright © 2011 SciRes. IJIS 40 V(NP)Or!V(NP)V(NP) And VP&&VP VP&&(VP||VP)&& (N?Num) (N?Num)V(NP,adv) Verb Structure AnalysisFixed Item Analysis Summarize S V(NP)V(NP) Or VP VP||VP (DC|DV,adj) NP&&NP NP||NP Noun Structure AnalysisSummarize convert DC|DV adj adj Adj Structure Analysis DC DC DC|DV Pos NP advVNP NonVPrep NPVNPVNP Pos VNValCom NP And NP NP Or NP Val Num Unit Figure 3. Speech pair list parsin g tree. 4.5. Implementation of Production Rule Generation via Algorithm the “DC.DV->NP” branch of the parsing tree, “metal fuel” and “AL” should be combined and tagged “NP” to form a new speech pair: “(metal fuel, NP).” 3) Combine verbs with NPs and other words according to the parsing tree to form predicate elements with “predicate (object, value)” structure. For example, the speech pairs (increase, V), (metal fuel AL, NP), and (content, V) must be merged together to form a predicate element such as increase (AL, content). In this step, some fixed phrases must also be converted into formal formats according to the corresponding part of the pars- ing tree (Figure 3). In the process of obtaining rules from scientific literature, the first three steps can be implemented by using existing mature template matching algorithms. At the end of the third stage, the antecedence and consequence of each rule has been extracted from the causal statements and structured as “IF (antecedence) THEN (consequence).” However, the antecedence and consequence are still rep- resented in the form of natural language. This section will focus on describing the algorithm used to convert them into predicate expressions according to the parsing tree shown in Figure 3. 4) Merge the predicate elements bottom-up according to the parsing tree and change the relative positions of the predicate elements. When the merging process comes to the highest level, rules will be generated. 1) This step can be implemented by querying field variable table, field constant able, etc. (Table 2). After t
H. WEI ET AL.41 Table 2. POS in the KBS. POS Type Corresponding Causal Statement Element Field Variable: DV Variables, such as “metal power,” etc. Field Constant: DC Constant, such as AL, etc. Field Verb: V Verb, such as “increase,” “improve,” “raise,” etc. Value: Value Constant, such as “temperature is 20°C,” etc. Degree Word: Adj (adjective) Degree words, such as “mass of,” etc. Field Adverb: Ad (adverb) Properties, such as “significantly,” “effectively,” etc. And/or Relationship Words: And/or Logical words, such as “at the same time,” “and,” etc. Negative Words: Non Negative words, such as “not,” “cannot,” etc. Preposition: Prep Generally this is the Chinese word “liao,” following a verb. Comparing Words: Com Such as “more than,” “equal to,” “lower than,” “reach,” etc. Suffix: Pos Generally this is the Chinese word “de,” following a DC or DV. Number: Num Number, such as 18, 32, etc. Unit Noun: Unit Unit nouns, such as mm.s-1, %, etc. this step, speechPairs structured as (word, speech tag) are created. The speechPairs of each antecedent and conse- quent are stored in the speechPairList. 2) Extract predicate expressions by merging structured speechPairs according to the parsing tree: The merging process must take place in a bottom-up and circular style following the instructions in Stage 4. When deriving NP and predicate elements, conflict resolution may be nec- essary if multiple routes exist. The merging algorithm is described as follows: //Set merging completion mark: end = false; //Keep looping until POS merging is completed and production rule is generated //”.” indicates adjoining words while(!end){ //Combine Noun structure circularly while(multiAdjacent(<word,”DV”>Or< word,”DC”>)||< word A,”DC”>.”de”Or< word A,”DV”>.”de”||< word A,”DC”>.< word B,”adj”>||< word A,”NP”>AndOr< word B,”NP”>){ Routes route = searchAllMatch- TreeRoute(NonStructureLevel); if(!single(route)) route = resolveConfliction(route); //Summarize current word according to final route path word C = compile(words sequence, route); define(word C, “NP”); update(speechPairList); } //Change single speech pair with structure < word,”DC”> or < word,”DV”> into < word,”NP”>; if(single(< word A,”DC”>)|| single(< word A,”DV”>)){ define(word A, “NP”); update(speechPairList); } //Merge Verb structure words into predicate element cir- cularly while(<word A,”adv”>.< word B,”V'>||< word A,”NP”>.< word B,” non”>.< word C,” V”>||< word A,”NP”>.< word B,” de”>.< word C,” V”>){ Routes route = searchAllMatch- TreeRoute(VerbStructureLevel); if(!single(route)) route = resolveConfliction(route); // Summarize current word according to final route path word C = combine(words sequence, route); define(word C, “VP”); update(speechPairList); } //Convert fixed phrases into formal formats cir- cularly Copyright © 2011 SciRes. IJIS
H. WEI ET AL. 42 while(<word A,”Num”>.< word B,”Unit”>){ word C = combine(word A, word B); define(word C, “Val”); update(speechPairList); } if(antecedence or consequence has been converted to predicate elements or formal fixed phrases){ PredicateExpression pe = compose(speechPair, andOrRelation); end = true; } } //Generate production rule. convert(speechPairList, pe); Production rules obtained through the abovementioned process can be used in knowledge reasoning immediately after conflicts have been eliminated. It can then be stored in corresponding knowledge managing modules. 5. Experiments This section describes two instances of the algorithm used for deriving rules from scientific articles under simulated use. 5.1. Example 1 Stage 1: A causal statement is extracted from the paper “Effect of RDX Particle Size on Properties of CMDB Propellant” [24]. “As experiments have proved, adding moderate amounts of AP and decreasing the granularity and granularity gradation of high-energy composite pro- pellants can improve flammability.” Stage 2: After eliminating the noise in the statement, the result turns to be “through adding moderate AP, de- creasing its granularity and granularity gradation in high-energy composite propellant, the flammability will be improved.” Stage 3: The antecedent and consequent are derived by matching casual relation templates (Through A, B: IF (A) THEN (B)) and represented as IF (adding moderate AP, decreasing its granularity and granularity gradation in high-energy composite propellant) THEN (the flamma- bility will be improved). Stage 4: In this stage, the predicate expressions are de- rived from the antecedent and consequent according to the parsing tree. This can be implemented following the instructions in Section 4.4. 1) By querying the field variable table (e.g., high-en- ergy composite propellant), field constant table (e.g., AP), etc. (Table 2), the structured speechPairs of the antece- dent and consequent are produced as follows: IF({in [.prep]}{ high-energy composite propellant [.DV]} {add [.V]} {moderate[.adj]} {AP[.DC]} {,[.And]} {decrease [.V]} its{granularity[.DV]} {and[.And]} {make[.V]} {granularity[.DV]} {gradation[.DV]}) THEN ({improve [.V]} {flammability[.DV]}). 2) The process of merging speechPairs based on the parsing tree is shown in Figure 4: High‐energy composite propellant && add(AP,moderate)&&decrease (granularity)&&make(granularity gradation) improve (flammability) V(NP)V(NP)V(NP) NP POS replacement NP&&V(NP)&&V(NP)&&V(NP)&&V(NP)POS replacement V(NP)V(NP) antecedence analysisconsequence analysis NP V Prep DV NP VNP VNP VNP VNP adj DV and DV and DV DVand DV DV Figure 4. Merging process of example 1. Copyright © 2011 SciRes. IJIS
H. WEI ET AL. Copyright © 2011 SciRes. IJIS 43 3) The production rule is finally derived from the causal statement as follows: IF (high-energy composite propellant &&add (AP, moderate) && decrease (granu- larity) &&make (granularity gradation)) THEN (improve (flammability)). 5.2. Example 2 Stage 1: Another example of a statement with a causal relationship was extracted from the paper “Influence of Ammonium Perchlorate and Aluminum Powder on the Combustion Characteristics of AP-CMDB Propellant” [25]. “If average size of RDX decreases from 92.02 μm to 17.35 μm, pressure index will increase 7.3%.” Stage 2: The above statement does not contain any noise, so it is not changed during this stage. Stage 3: By matching casual relation template (If A, B: IF (A) THEN (B)), the antecedent and consequent can be extracted from the statement and represented as follows: IF (average size of RDX decreases from 92.02 μm to 17.35 μm) THEN (pressure index will increase 7.3%). Stage 4: Based on the above result, the production rule can be derived as follows: 1) After querying field variable table (e.g., average size), field constant table (e.g., RDX), field verb table (e.g., decrease, increase) etc. (shown in Table 2), the structured speechPairs will be produced. The result is: IF({RDX[DC]} {average size[DV]} {92.02[Num]} {μm [Unit]} {decrease[V]} {17. 35 [Nu m]} {μm[Unit]}) THEN ({pressure index[DV]} {increase[V]} {7.3[Num]} {% [Unit]}). 2) According to the parsing tree, the merging process of the speechPairs takes place as shown in Figure 5. 3) Upon the completion of these processes, the pro- duction rule becomes “IF (decrease (RDX average size, 92.02 μm, 17.35 μm)) THEN (increase (pressure index, 7.3%)).” 6. Conclusions and Possibilities for Future Research This paper is based on a well-noted fact: in specific pro- fessional fields, experts often use relatively fixed pat- terns to describe their findings when they publish scien- tific articles. Moreover, the abstracts and conclusions, the essence of these articles, contain abundant field knowledge. Labeling the summarizing those statements in abstracts and conclusions that have apparent causal relationships, transforming these relationships into for- mats that can be used in KBSes, and then using an auto- matic process to represent this knowledge as production rules may greatly improve the effectiveness and accuracy Figure 5. Merging process of example 2.
H. WEI ET AL. Copyright © 2011 SciRes. IJIS 44 of knowledge acquisition. In this paper a pattern-text-mining-based method of acquiring production rules from field-specific papers is proposed. This paper takes texts that contain field know- ledge as input. First, the summarizing statements in the abstracts and conclusions of the articles are processed using mature technologies, such as text mining, and causal statements are extracted from them. Then produc- tion rules are derived by POS analysis and parsing tree processing. Two experiments showed that the proposed method can be used effectively to obtain production rules from summarizing statements. In future studies, algorithms used for POS analysis can be investigated to enhance the algorithm’s self-learning ability. At the same time, the process of converting causal statements to production rules can be refined to further improve the efficiency of field knowledge acquisition. 7. 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