›› 2013, Vol. 28 ›› Issue (4): 689-719.doi: 10.1007/s11390-013-1369-6

Special Issue: Surveys; Artificial Intelligence and Pattern Recognition

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

A Survey of Commonsense Knowledge Acquisition

Liang-Jun Zang1,2 (臧良俊), Cong Cao1,2 (曹聪), Ya-Nan Cao3 (曹亚男), Yu-Ming Wu1 (吴昱明) and Cun-Gen CAO1 (曹存根), Member, CCF   

  1. 1. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences Beijing 100190, China;
    2. University of the Chinese Academy of Sciences, Beijing 100049, China;
    3. The Second Lab, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
  • Received:2012-06-15 Revised:2013-04-28 Online:2013-07-05 Published:2013-07-05
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant Nos. 91224006, 61173063, 61035004, 61203284, and 309737163, and the National Social Science Foundation of China under Grant No. 10AYY003.

Collecting massive commonsense knowledge (CSK) for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects: CSK acquisition task (what CSK is acquired from where), technique used (how can CSK be acquired), and CSK evaluation methods (how to evaluate the acquired CSK). In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.

[1] Minsky M. The Emotion Machine. Simon & Schuster NewYork, 2006.

[2] McCarthy J. Some expert systems need common sense. An-nals of the New York Academy of Sciences, 1984, 426:129-137.

[3] Lenat D, Prakash M, Shepherd M. CYC: Using common senseknowledge to overcome brittleness and knowledge acquisitionbottlenecks. AI Magazine, 1986, 6(4):65-85.

[4] Lieberman H, Liu H, Singh P, Barry B. Beating common senseinto interactive applications. AI Magazine, 2004, 25(4):63-76.

[5] Curtis J, Cabral J, Baxter D. On the application of the Cycontology to word sense disambiguation. In Proc. the 19th Int.Florida AI Research Society Conf., May 2006, pp.652-657.

[6] Dahlgren K, McDowell J. Using commonsense knowledge todisambiguate prepositional phrase modiffiers. In Proc. the 5thNational Conf. Artifficial Intelligence, Aug. 1986, pp.589-593.

[7] Havasi C, Speer R, Pustejovsky J. Coarse word-sense disam-biguation using common sense. In Proc. the AAAI Fall Sym-posium Series, Nov. 2010.

[8] Cambria E, Hussain A, Havasi C, Eckl C. Affectivespace:Blending common sense and affective knowledge to performemotive reasoning. In Proc. the 1st WOMSA at CAEPIA2009, Nov. 2009, pp.32-41.

[9] Cambria E, Hussain A, Havasi C, Eckl C. Sentic comput-ing: Exploitation of common sense for the development ofemotion-sensitive systems. In Lecture Notes in Computer Sci-ence 5967, Hutchison D, Kanade T, Kittler J et al. (eds.),2010, pp.148-156.

[10] Curtis J, Matthews G, Baxter D. On the effective use of Cycin a question answering system. In Proc. IJCAI Work-shop. Knowledge and Reasoning for Answering Questions,Aug. 2005.

[11] Liu H, Singh P. Makebelieve: Using commonsense knowledgeto generate stories. In Proc. the 18th National Conf. Artiffi-cial Intelligence, Jul.28-Aug.1, 2002, pp.957-958.

[12] Ong E. A commonsense knowledge base for generating chil-dren's stories. In Proc. the AAAI Fall Symposium Series onCommon Sense Knowledge, Nov. 2010, pp.82-87.

[13] Liu H, Lieberman H, Selker T. Goose: A goal-oriented searchengine with commonsense. In Proc. the 2nd Int. Conf.Adaptive Hypermedia and Adaptive Web-Based Systems, Jun.2006, pp.253-263.

[14] Hsu M, Chen H. Information retrieval with commonsenseknowledge. In Proc. the 29th SIGIR 2006, Aug. 2006,pp.651-652.

[15] Nilsson N. Artifficial Intelligence: A New Synthesis. MorganKaufmann, 1998.

[16] Gupta R, Kochenderfer M. Common sense data acquisitionfor indoor mobile robots. In Proc. the 19th National Conf.Artifficial Intelligence, Jul. 2004, pp.605-610.

[17] McCarthy J. Programs with common sense. In Proc. theTeddington Conf. the Mechanization of Thought Processes,Dec. 1958.

[18] Lenat D B. CYC: A large-scale investment in knowledge in-frastructure. Communications of the ACM, 1995, 38(11): 33-38.

[19] Singh P, Lin T, Mueller E, Lim G, Perkins T, Zhu W. Openmind common sense: Knowledge acquisition from the generalpublic. In Proc. Conf. Cooperative Information Systems,Oct.30-Nov.1 2002, pp.1223-1237.

[20] Dong Z, Dong Q. HowNet and the Computation of Meaning.Singapore: World Scientiffic Publishing Company, 2006.

[21] Liu H, Singh P. Conceptnet——A practical commonsense rea-soning tool-kit. BT Technology Journal, 2004, 22(4): 211-226.

[22] Speer R, Havasi C, Lieberman H. AnalogySpace: Reducingthe dimensionality of common sense knowledge. In Proc. the23rd AAAI, Jul. 2008, pp.548-553.

[23] Schubert L. Can we derive general world knowledge fromtexts In Proc. the 2nd Int. Conf. Human Language Tech-nology Research, Mar. 2002, pp.94-97.

[24] Torisawa K. An unsupervised learning method for common-sensical inference rules on events. In Proc. the 2nd CoLogNet-EIsNET Symposium, Dec. 2003.

[25] Torisawa K. Acquiring inference rules with temporal con-straints by using Japanese coordinated sentences and noun-verb co-occurrences. In Proc. the Human Language Technol-ogy Conf. the North American Chapter of the Associationof Computational Linguistics (HLT/NAACL), Jun. 2006,pp.57-64.

[26] Chklovski T. Learner: A system for acquiring commonsenseknowledge by analogy. In Proc. the 2nd Int. Conf. Knowl-edge Capture, Oct. 2003, pp.4-12.

[27] Witbrock M, Matuszek C, Brusseau A, Kahlert R, Fraser C,Lenat D. Knowledge begets knowledge: Steps towards as-sisted knowledge acquisition in Cyc. In Proc. the AAAISpring Symposium on Knowledge Collection from VolunteerContributors, Mar. 2005, pp.99-105.

[28] von Ahn L, Kedia M, Blum M. Verbosity: A game for collect-ing common-sense facts. In Proc. the ACM SIGCHI Conf.Human Factors in Computing Systems, Apr. 2006, pp.75-78.

[29] Lieberman H, Smith D, Teeters A. Common consensus: Aweb-based game for collecting commonsense goals. In Proc.Int. Conf. Intelligent User Interfaces, Jan. 2007.

[30] Speer R, Krishnamurthy J, Havasi C, Smith D, LiebermanH, Arnold K. An interface for targeted collection of commonsense knowledge using a mixture model. In Proc. the 14th Int.Conf. Intelligent User Interfaces, Feb. 2009, pp.137-146.

[31] Kuo Y, Lee J, Chiang K, Wang R, Shen E, Chan C, Hsu J.Community-based game design: Experiments on social gamesfor commonsense data collection. In Proc. the ACM SIGKDDWorkshop on Human Computation, Jun. 2009, pp.15-22.

[32] Kuo Y, Hsu J. Goal-oriented knowledge collection. In Proc.the AAAI Fall Symposium Series, Nov. 2010.

[33] Banko M, Cafarella M, Soderland S, Broadhead M, EtzioniO. Open information extraction from the web. In Proc. the20th IJCAI, Jan. 2007, pp. 2670-2676.

[34] Etzioni O, Fader A, Christensen J, Soderland S, Center M.Open information extraction: The second generation. InProc. the 22th Int. Joint Conf. Artifficial Intelligence, Jul.2011, pp.3-10.

[35] Fader A, Soderland S, Etzioni O. Identifying relations for openinformation extraction. In Proc. the Conf. Empirical Meth-ods in Natural Language Processing, Jul. 2011, pp.1535-1545.

[36] Mausam J C, Soderland S, Etzioni O. Learning arguments foropen information extraction. Technical Report, University ofWashington, 2011.

[37] Matuszek C, Witbrock M, Kahlert R, Cabral J, Schneider D,Shah P, Lenat D. Searching for common sense: PopulatingCycTM from the Web. In Proc. the National Conf. ArtifficialIntelligence, Jul. 2005, pp.1430-1435.

[38] Forbus K, Riesbeck C, Birnbaum L, Livingston K, Sharma A,Ureel L. Integrating natural language, knowledge represen-tation and reasoning, and analogical processing to learn byreading. In Proc. the 22nd National Conf. Artifficial Intelli-gence, Jul. 2007, pp.1542-1547.

[39] Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, IvesZ. DBpedia: A nucleus for a web of open data. In Proc. theISWC 2007/ASWC2007, Nov. 2007, pp.722-735.

[40] Bizer C, Lehmann J, Kobilarov G, Auer S, Becker C, Cyga-niak R, Hellmann S. DBpedia: A crystallization point for theweb of data. Web Semantics: Science, Services and Agentson the World Wide Web, 2009, 7(3): 154-165.

[41] Suchanek F M, Kasneci G, Weikum G. YAGO: A core of se-mantic knowledge. In Proc. the 16th Int. Conf. World WideWeb, May 2007, pp.697-706.

[42] Hoffart J, Suchanek F M, Berberich K, Weikum G. YAGO2:A spatially and temporally enhanced knowledge base fromWikipedia. Artifficial Intelligence, 2013, 194: 28-61.

[43] Niu X, Sun X, Wang H, Rong S, Qi G, Yu Y. Zhishi.me:Weaving Chinese linking open data. In Proc. the 10th Int.Conf. the Semantic Web, Oct. 2011, pp.205-220.

[44] Wang Z C, Wang Z G, Li J Z, Pan J Z. Knowledge extrac-tion from Chinese wiki encyclopedias. Journal of ZhejiangUniversity——Science C, 2012, 13(4): 268-280.

[45] Zeng Y. Extracting, linking and analyzing the Web of struc-tured Chinese data. Technical Report, Institute of Automa-tion, Chinese Academy of Sciences, 2012.

[46] Singh P. The public acquisition of commonsense knowledge.In Proc. the AAAI Spring Symposium on Acquiring (andUsing) Linguistic (and World) Knowledge for InformationAccess, Mar. 2002.

[47] Adomavicius G, Tuzhilin A. Toward the next generation ofrecommender systems: A survey of the state-of-the-art andpossible extensions. IEEE Transactions on Knowledge andData Engineering, 2005, 17(6): 734-749.

[48] Schubert L, Tong M. Extracting and evaluating general worldknowledge from the Brown corpus. In Proc. the HLT-NAACL2003 Workshop on Text Meaning, Vol.9, May 2003, pp.7-13.

[49] Zhu Y, Zang L, Wang D, Cao C. A manual experiment oncommonsense knowledge acquisition from web corpora. InProc. the Int. Conf. Machine Learning and Cybernetics,Vol.3, Jul. 2008, pp.1564-1569.

[50] Vanderwende L. Volunteers created the web. In Proc. AAAISpring Symposium on Knowledge Collection from VolunteerCotnributors, Mar. 2005.

[51] Eslick I. Searching for commonsense [Master Thesis]. Mas-sachusetts Institute of Technology, 2006.

[52] Etzioni O, Cafarella M, Downey D, Kok S, Popescu A, ShakedT, Soderland S, Weld D, Yates A. Web-scale information ex-traction in KnowItAll: (Preliminary results). In Proc. the13th Int. Conf. World Wide Web, May 2004, pp.100-110.

[53] Etzioni O, Cafarella M, Downey D, Popescu A, Shaked T,Soderland S, Weld D, Yates A. Unsupervised named-entityextraction from the Web: An experimental study. ArtifficialIntelligence, 2005, 165(1): 91-134.

[54] Sharma A, Forbus K. Graph-based reasoning and rein-forcement learning for improving Q/A performance in largeknowledge-based systems. In Proc. the AAAI Fall Sympo-sium Series, Nov. 2010.

[55] Liu H, Lieberman H, Selker T. A model of textual affect sens-ing using real-world knowledge. In Proc. the 8th Int. Conf.Intelligent User Interfaces, Jan. 2003, pp.125-132.

[56] Gordon A S, Kozareva Z, Roemmele M. SemEval-2012 Task7 :Choice of Plausible Alternatives : An Evaluation of Common-sense Causal Reasoning. In Proc. the 1st Joint Conferenceon Lexical and Computational Semantics, Jun. 2012.

[57] Gennari J H, Musen M A, Fergerson R W, Grosso W E,Crubezy M, Eriksson H, Noy N F, Tu S W. The evolutionof Protege: An environment for knowledge-based systems de-velopment. Int. Journal of Human-Computer Studies, 2003,58(1): 89-123.

[58] Panton K, Miraglia P, Salay N, Kahlert R C, Baxter D,Reagan R. Knowledge formation and dialogue using theKRAKEN toolset. In Proc. AAAI, Jul. 2002, pp.900-905.

[59] Witbrock M, Baxter D, Curtis J, Schneider D, Kahlert R, Mi-raglia P, Wagner P, Panton K, Matthews G. An interactivedialogue system for knowledge acquisition in Cyc. In Proc.Int. Joint Conf. Artifficial Intelligence, Aug. 2003.

[60] Quinlan J, Cameron-Jones R. Induction of logic programs:FOIL and related systems. New Generation Computing,1995, 13(3): 287-312.

[61] Masters J, Matuszek C, Witbrock M. Ontology-based integra-tion of knowledge from semi-structured web pages. TechnicalReport, Cycorp, 2006.

[62] Medelyan O, Legg C. Integrating Cyc and Wikipedia: Folk-sonomy meets rigorously deffined common-sense. In Proc.AAAI Workshop on Wikipedia and AI, Jul. 2008.

[63] Pierce C, Booth D, Ogbuji C, Deaton C, Blackstone E, LenatD. SemanticDB: A semanticWeb infrastructure for clinical re-search and quality reporting. Current Bioinformatics, 2012,7(3): 267-277.

[64] Witbrock M, Panton K, Reed S, Schneider D, Aldag B,Reimers M, Bertolo S. Automated OWL annotation assistedby a large knowledge base. In Proc. the ISWC Workshopon Knowledge Markup and Semantic Annotation, Nov. 2004,pp.71-80.

[65] Mueller E. Natural language processing with ThoughtTrea-sure. http://citeseerx.ist.psu.edu/viewdoc/downloaddoi=, 1998.

[66] Mueller E. A calendar with common sense. In Proc. the 5thInt. Conf. Intelligent User Interfaces, Jan. 2000, pp.198-201.

[67] Speer R, Havasi C, Surana H. Using verbosity: Common sensedata from games with a purpose. In Proc. the 23nd Int.Florida AI Research Society Conf., May 2010.

[68] Anacleto J, Lieberman H, Tsutsumi M, Neris V, CarvalhoA, Espinosa J, Godoi M, Zem-Mascarenhas S. Can commonsense uncover cultural differences in computer applicationsIn Proc. the 19th World Computer Congress, August 2006,pp.1-10.

[69] Eckhardt N. A kid's open mind common sense [Ph.D. Thesis].Tilburg University, 2008.

[70] Chung H. GlobalMind —— Bridging the gap between differentcultures and languages with commonsense computing [MasterThesis]. Massachusetts Institute of Technology, 2006.

[71] Cambria E, Xia Y, Hussain A. Affective common sense knowl-edge acquisition for sentiment analysis. In Proc. the 8thInt. Conf. Language Resources and Evaluation, May 2012,pp.3580-3595.

[72] von Ahn L. Games with a purpose. IEEE Computer Maga-zine, 2006, 39(6): 92-94.

[73] Speer R. Open mind commons: An inquisitive approach tolearning common sense. In Proc. the Workshop on CommonSense and Intelligent User Interfaces, Jan. 2007.

[74] Minsky M. The Society Of Mind. Simon and Schuster, 1988.

[75] Fellbaum C. WordNet: An Electronic Lexical Database. TheMIT press, 1998.

[76] Havasi C, Speer R, Alonso J. ConceptNet 3: A flexible, mul-tilingual semantic network for common sense knowledge. InProc. the 22nd AAAI, Sept. 2007.

[77] Alonso J. CSAMOA: A common sense application model ofarchitecture. In Proc. the Workshop on Common Sense andIntelligent User Interfaces, Jan. 2007.

[78] Pustejovsky J, Havasi C, Sauri R et al. Towards a generativelexical resource: The brandeis semantic ontology. In Proc.the Language Resources and Evaluation Conf., May 2006.

[79] Speer R, Havasi C. Representing general relational knowledgein ConceptNet 5. In Proc. the 8th Int. Conf. Language Re-sources and Evaluation, May 2012, pp.3679-3686.

[80] Havasi C, Speer R, Pustejovsky J, Lieberman H. Digital in-tuition: Applying common sense using dimensionality reduc-tion. IEEE Intelligent Systems, 2009, 24(4): 24-35.

[81] von Assem M, von Ossenbruggen J. Wordnet 3.0 in RDF.http://semanticweb. cs. vu. nl/lod/wn30/, Sep. 2011, pp.10-24.

[82] von Ahn L, Dabbish L. Designing games with a purpose.Communications of the ACM, 2008, 51(8): 58-67.

[83] Androutsopoulos I, Malakasiotis P. A survey of paraphrasingand textual entailment methods. Journal of Artifficial Intel-ligence Research, 2010, 38(1): 135-187.

[84] Hashimoto C, Torisawa K, De Saeger S, Oh J, Kazama J. Ex-citatory or inhibitory: A new semantic orientation extractscontradiction and causality from the Web. In Proc. EMNLP-CoNLL 2012, Jul. 2012.

[85] Tian W, Cao C, H W. Representation, acquisition and anal-ysis of psychological commonsense concepts. Computer Sci-ence, 2004, 31(6): 5-12. (in Chinese)

[86] Peng H, Cao C. Research on mining of associated events anddiscovering roles relaionship. Computer Science, 2010, 12. (inChinese)

[87] Cao Y, Cao C, Zang L, Zhu Y, Wang S, Wang D. Acquiringcommonsense knowledge about properties of concepts fromtext. In Proc. the 5th Int. Conf. Fuzzy Systems and Knowl-edge Discovery, Aug. 2008, Vol.4, pp.155-159.

[88] Cao Y, Cao C, Zang L, Wang S, Wang D. Extracting compar-ative commonsense from the Web. In Proc. the IFIP Inter-national Conference on Intelligent Information Processing,Oct. 2010, pp.154-162.

[89] Cao Y, Cao C, Zang L, Wang S. Web mining for causal rela-tions between events. Information: An International Inter-disci Journal, 2011, 15(1).

[90] Bollacker K, Evans C, Paritosh P, Sturge T, Taylor J. Free-base: A collaboratively created graph database for structur-ing human knowledge. In Proc. SIGMOD2008, 2008, pp.1247–1250.

[91] Lehmann J, Isele R, Jakob M, Jentzsch A, Kontokostas D,Mendes P N, Hellmann S, Morsey M, Kleef v P, Auer S, BizerC. Dbpedia —— A large-scale, multilingual knowledge base ex-tracted from Wikipedia. Semantic Web Journal, 2013, Underreview.

[92] Ritter A, Mausam, Etzioni O. A latent dirichlet allocationmethod for selectional preferences. In Proc. the 48th An-nual Meeting of the Association for Computational Linguis-tics, Jul. 2010, pp.424-434.

[93] Lin T, Mausam, Etzioni O. Identifying functional relations inweb text. In Proc. the Conf. Empirical Methods in NaturalLanguage Processing, Oct. 2010, pp.1266-1276.

[94] Schoenmackers S, Etzioni O, Weld D S, Davis J. Learningffirst-order Horn clauses from Web text. In Proc. the Conf.on Empirical Methods in Natural Language Processing, Oct.2010, pp.1088-1098.

[95] Berant J, Dagan I, Goldberger J. Global learning of typedentailment rules. In Proc. the 49th HLT-ACL, Jun. 2011,vol.1, pp. 610-619.

[96] Soderland S, Roof B, Qin B, Xu S, Mausam, Etzioni O.Adapting open information extraction to domain-speciffic re-lations. AI Magazine, 2010, 31(3): 93-102.

[97] Klein D, Manning C. Accurate unlexicalized parsing. In Proc.the 41st Annual Meeting on Association for ComputationalLinguistics, Jul. 2003, Vol.1, pp.423-430.

[98] Downey D, Etzioni O, Soderland S. A probabilistic model ofredundancy in information extraction. In Proc. Int. JointConf. Artifficial Intelligence, Jul.30-Aug.5, 2005, Vol.19.

[99] Banko M, Etzioni O. The tradeoffs between open and tradi-tional relation extraction. In Proc. the 46th ACL, Jun. 2008,pp.28-36.

[100] Wu F, Weld D S. Open Information Extraction UsingWikipedia. In Proc. the 48th ACL, Jul. 2010, pp. 118-127.

[101] Wu W, Li H, Wang H, Zhu K Q. Probase: A probabilistictaxonomy for text understanding. In Proc. the ACM Int.Conf. Management of Data, Nov. 2012, pp. 481–492.

[102] Ponzetto S P, Strube M. Deriving a large scale taxonomy fromWikipedia. In Proc. AAAI2007, Jul. 2007, Volume 22.

[103] Wang Y, Li H, Wang H, Zhu K Q. Towards topic search onthe Web. Technical Report, Microsoft Research, 2010.

[104] Song Y, Wang H, Wang Z, Li H, Chen W. Short text con-ceptualization using a probabilistic knowledge base. In Proc.IJCAI2011, Jul. 2011, pp. 2330–2336.

[105] Carlson A, Betteridge J, Kisiel B, Settles B, Hruschka Jr E,Mitchell T. Toward an architecture for never-ending languagelearning. In Proc. the 24th AAAI, Jul. 2010, Vol.2.

[106] Carlson A, Betteridge J, Wang R et al. Coupled semi-supervised learning for information extraction. In Proc. the3rd ACM Int. Conf. Web Search and Data Mining, Feb.2010, pp.101-110.

[107] Fensel D, von Harmelen F, Andersson B et al. TowardsLarKC: A platform for Web scale reasoning. In Proc. Int.Conf. Semantic Computing, Aug. 2008, pp.524-529.

[108] Assel M, Cheptsov A, Gallizo G et al. Large knowledge col-lider: A service-oriented platform for large-scale semantic rea-soning. In Proc. the International Conference on Web Intel-ligence, Mining and Semantics, May 2011.

[109] Pasca M, Van Durme B. Weakly-supervised acquisition ofopen-domain classes and class attributes from web documentsand query logs. In Proc. the 46th Annual Meeting of the As-sociation for Computational Linguistics, Jun. 2008, pp.19-27.

[110] Turney P. Mining the Web for synonyms: PMI-IR versusLSA on TOEFL. In Proc. the 12th European Conf. MachineLearning, Sept. 2001, pp. 491-502.
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[1] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Zhang Bo; Zhang Ling;. Statistical Heuristic Search[J]. , 1987, 2(1): 1 -11 .
[10] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .

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