基于本体的语义相似度计算研究
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摘要
语义相似度计算在自然语言理解与处理、信息检索、知识获取、机器翻译等领域具有重要作用.近年来,随着知识本体和知识图谱研究的深入,面向复杂关系处理的结构化知识表达手段更为丰富和强大,从而推动了基于本体的语义相似度计算方法的快速发展.基于本体的语义相似度计算大致分为基于距离的方法、基于信息量的方法、基于属性的方法和混合式方法 4种代表性方法.回顾了上述方法的发展脉络,分析了各自的基本思想和主要实现方法,并对其优缺点进行了系统比较.最后总结了语义相似度的评测方法,并在此基础上,展望了基于本体的语义相似度计算方法的发展方向.
Semantic similarity measurement played a crucial role in the fields of natural language understanding and processing,information retrieval,knowledge acquisition,and machine translation. In recent years,with the rapid development of the knowledge graph and the ontology-related research,structured semantic knowledge representation became more effective than ever before,thus promoting the development of ontology-based similarity measurement. Ontology-based semantic similarity measures could be roughly classified into the following categories: distance-based measurement,content-based measurement,feature-based measurement,and hybrid measurement. The development of several typical methods was reviewed by describing their respective characteristics and comparing their advantages and disadvantages. In the end,the evaluation method of semantic similarity was discussed. And new possible trends and directions in ontology-based semantic similarity measurement were explored
Semantic similarity measurement played a crucial role in the fields of natural language understanding and processing,information retrieval,knowledge acquisition,and machine translation. In recent years,with the rapid development of the knowledge graph and the ontology-related research,structured semantic knowledge representation became more effective than ever before,thus promoting the development of ontology-based similarity measurement. Ontology-based semantic similarity measures could be roughly classified into the following categories: distance-based measurement,content-based measurement,feature-based measurement,and hybrid measurement. The development of several typical methods was reviewed by describing their respective characteristics and comparing their advantages and disadvantages. In the end,the evaluation method of semantic similarity was discussed. And new possible trends and directions in ontology-based semantic similarity measurement were explored
引文
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[2]梅家驹.同义词词林[M].上海:上海辞书出版社,1983.
[3]董振东.语义关系的表达和知识系统的建造[J].语言文字应用,1998(3):76-82.
[4]黄曾阳. HNC理论概要[J].中文信息学报,1997,11(4):11-20.
[5] ZHANG K L,FEI Q L. Co-construction of ontology-based knowledge base through the web:theory and practice[C]∥The 6thIEEE International Conference on Natural Language Processing and Knowledge Engineering.Beijing,2010:1-6.
[6]张克亮,李伟刚,王慧兰.基于本体的航空领域问答系统[J].中文信息学报,2015,29(4):192-198.
[7]费勤龙,张克亮,朱沛胜.基于WEB的机载雷达领域本体知识库的共建研究[J].微计算机技术,2010,31(9):21-28.
[8]朱礼军,陶兰,刘慧.领域本体中的概念相似度计算[J].华南理工大学学报(自然科学版),2004,32(s1):147-150.
[9]黄果,周竹荣.基于领域本体的概念语义相似度计算研究[J].计算机工程与设计,2007,28(10):2460-2463.
[10]田久乐,赵蔚.基于同义词词林的词语相似度计算方法[J].吉林大学学报(信息科学版),2010,28(6):602-608.
[11]夏天.汉语词语语义相似度计算研究[J].计算机工程,2007,33(6):191-194.
[12]孙海霞,钱庆,成颖.基于本体的语义相似度计算方法研究综述[J].现代图书情报技术,2010,26(1):51-56.
[13]刘宏哲,须德.基于本体的语义相似度和相关度计算研究综述[J].计算机科学,2012,39(2):8-13.
[14]黄河燕,张克亮,张孝飞.基于本体的专业机器翻译术语词典研究[J].中文信息学报,2007,21(1):17-22.
[15]黄河燕,陈肇雄,张孝飞,等.大规模句子相似度计算方法[J].中文信息学报,2006,20(s1):47-52.
[16]祁琪.越南军情本体知识库的构建及问句分析处理[D].洛阳:战略支援部队信息工程大学洛阳校区,2014.
[17]陈红红,李辉,李新春.基于领域本体的概念格语义匹配[J].郑州大学学报(理学版),2010,42(2):70-73.
[18]鲁德浩,郏东耀.一种改进的概念相似度计算方法[J].郑州大学学报(理学版),2010,42(1):1-4.
[19]郑志蕴,阮春阳,李伦等.本体语义相似度自适应综合加权算法研究[J].计算机科学,2016,43(10):242-247.
[20] RADA R,MILI H,BICKNELL E,et al. Development and application of a metric on semantic nets[J].IEEE transactions onsystems man and cybernetics,1989,19(1):17-30.
[21] RICHARDSON R,SMEATON A F. Using Word Net in a knowledge-based approach to information retrieval[C]∥Proceedings ofthe BCS-IRSG-Colloquium. Crewe,1995.
[22] WU Z,PALMER M. Verbs semantics and lexical selection[C]∥Proceedings of the 32nd Annual Meeting on Association forComputational Linguistics. Morristown,1994:133-138.
[23] LEACOCK C,CHDOROW M. Combining local context and wordnet similarity for word sense identification[M]∥Word Net:AnElectronic Lexical Database. Massachusetts:MIT Press,1998:265-283.
[24] HIRST G,ONGE D S. Lexical chains as representations of context for the detection and correction of malapropisms[M]∥Word-Net:An Electronic Lexical Database. Massachusetts:MIT Press,1998:305-332.
[25] YANG D,POWERS D M W. Measuring semantic similarity in the taxonomy of Word Net[C]∥Proceedings of the 28th Austral-asian Computer Science Conference. Newcastle,2005:315-322.
[26] LORD P W,STEVENS R D,BRASS A,et al. Investigating semantic similarity measures across the gene ontology:the relation-ship between sequence and annotation[J]. Bioinformatics,2003,19(10):1275-83.
[27] RESNIK P. Using information content to evaluate semantic similarity in a taxonomy[C]∥IJCAI'95 Proceedings of the 14th In-ternational Joint Conference on Artificial Intelligence.Montreal,1995:448-453.
[28] LIN D. An information-theoretic definition of similarity[C]∥Proceedings of the 15th International Conference(ICML’98).Madison,1998:296-304.
[29] JIANG J J,CONRATH D W. Semantic similarity based on corpus statistics and lexical taxonomy[C]∥Proceedings of the 10thInternational Conference of Research on Computational Linguistics. Taiwan,1997.
[30] SECO N,VEALE T,HAYES J. An intrinsic information content metric for semantic similarity in Word Net[C]∥European Con-ference on Artificial Intelligence.Valencia,2004:1089-1090.
[31] PIRRO G. A semantic similarity metric combining features and intrinsic information content[J]. Data and knowledge engineer-ing,2009,68(11):1289-1308.
[32] SANCHEZ D,BATET M,VALLS A,et al. Ontology-driven web-based semantic similarity[J].Journal of intelligent informationsystems,2010,35(3):383-413.
[33] ZHANG X,SUN S,ZHANG K. An information content-based approach for measuring concept semantic similarity in Word Net[J/OL].Wireless Personal Communications,2018:1-16[2018-03-15].https:∥doi.org/10. 1007/s11277-018-5429-7.
[34] TVERSKY A. Features of similarity[J]. Readings in cognitive science,1977,84(4):290-302.
[35] RODRIGUEZ M A,EGENHOFER M J. Determining semantic similarity among entity classes from different ontologies[J].Knowledge&data engineering IEEE transactions on,2003,15(2):442-456.
[36] PETRAKIS E G M,VARELAS G,HLIAOUTAKIS A,et al. X-similarity:computing semantic similarity between concepts fromdifferent ontologies[J]. Journal of digital information management,2006,4(4):233-237.
[37] LESK M. Automatic sense disambiguation using machine readable dictionaries:how to tell a pine cone from an ice cream cone[C]∥Acm Special Interest Group for Design of Communication. New York,1986:24-26.
[38] BANERJEE S,PEDERSEN T. Extended gloss overlaps as a measure of semantic relatedness[C]∥Proceedings of IJCAI.Mexico,2003:805-810.
[39] PATWARDHAN S,PEDERSEN T. Using Word Net-based context vectors to estimate the semantic relatedness of concepts[C]∥Proceedings of the EACL 2006 Workshop Making Sense of Sense-Bringing Computational Linguistics and Psycholinguistics To-gether.Trento,2006:1501.
[40] SANCHEZ D,BATET M,ISERN D,et al. Ontology-based semantic similarity:a new feature-based approach[J]. Expert sys-tems with applications,2012,39(9):7718-7728.
[41] DING L,FININ T,JOSHI A,et al. Swoogle:a search and metadata engine for the semantic web[C]∥Proceedings of the 13thACM International Conference on Information and Knowledge Management. New York,2004:652-659.
[42]董振东,董强.知网[Z/OL].(1999-09-23)[2017-03-06]. http:∥www.keenage.com.
[43] LI Y,BANDAR Z A,MCLEAN D. An approach for measuring semantic similarity between words using multiple informationsources[J]. IEEE transactions on knowledge&data engineering,2003,15(4):871-882.
[44] AL-MUBAID H,NGUYEN H A.A cluster-based approach for semantic similarity in the biomedical domain[C]∥2006 Interna-tional Conference of the IEEE Engineering in Medicine and Biology Society.New York,2006:2713-2717.
[45] SCHICKEL-ZUBER V,FALTINGS B. OSS:a semantic similarity function based on hierarchical ontologies[C]∥InternationalJoint Conference on Artifical Intelligence.Hyderabad,2007:551-556.
[46] ALVAREZ M A,LIM S J. A graph modeling of semantic similarity between words[C]∥Proceedings of the First InternationalConference on Semantic Computing(ICSC’07).Irvine,2007:355-362.
[47] SHI B,FANG L,YAN J,et al. Ontology-based measure of semantic similarity between concepts[C]∥2009 WRI World Con-gress on Software Engineering.Xiamen,2009:109-112.
[48]刘群,李素建.基于《知网》的词汇语义相似度计算[J].中文计算语言学,2002,7(2):59-76.
[49]李峰,李芳.中文词语语义相似度计算———基于《知网》2000[J].中文信息学报,2007,21(3):99-105.
[50]江敏,肖诗斌,王弘蔚,等.一种改进的基于《知网》的词语语义相似度计算[J].中文信息学报,2008,22(5):84-89.
[51] RUBENSTEIN H,GOODENOUGH J B. Contextual correlates of synonymy[J]. Communications of the ACM,1965,8(10):627-633.
[52] MILLER G A,CHARLES W G. Contextual correlates of semantic similarity[J]. Language cognition&neuroscience,1991,6(1):1-28.
[53] FINKELSTEIN R L. Placing search in context:the concept revisited[J].Acm transactions on information systems,2002,20(1):116-131.
[54] HUANG E H,SICHER R,MANNING C D,et al. Improving word representations via global context and multiple word proto-types[C]∥50th Annual Meeting of the ACL.Jeju Island,2012:873-882.
[55] HILL F,REICHART R,KORHONEN A. Simlex-999:evaluating semantic models with(genuine)similarity estimation[J].Computational linguistics,2015,41(4):665-695.
[56] AVRAHAM O,GOLDBERG Y. Improving reliability of word similarity evaluation by redesigning annotation task and perform-ance measure[C]∥The Workshop on Evaluating Vector-Space Representations for Nlp.Berlin,2016:106-110.
[57] LIU Q,LI S.Word similarity computing based on How-net[J].Computational linguistics and Chinese language processing,2002,7(2):59-76.
[58] JIN P,WU Y.Semeval-2012 task 4:evaluating Chinese word similarity[C]∥Proceedings of the First Joint Conference on Lexi-cal and Computational Semantics.Montréal,2012:374-377.
[59] GUO J,CHE W,WANG H,et al. Learning sense-specific word embeddings by exploiting bilingual resources[C]∥Proceed-ings of the 25th International Conference on Computational Linguistics.Dublin,2014:497-507.
[60] WU Y,LI W. Overview of the NLPCC-ICCPOL 2016 shared task:Chinese word similarity measurement[C]∥InternationalConference on Computer Processing of Oriental Languages.Kunming,2016:828-839.
[61] CILIBRASI R L,VITANYI P M B. Normalized web distance and word similarity[J]. Computer science,2009:293-314.
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