收稿日期: 2016-07-17
修回日期: 2016-10-08
网络出版日期: 2016-10-20
基金资助
本文系国家社会科学基金重大招标项目“面向学科领域的网络信息资源深度聚合与服务研究”（项目编号：12&ZD221）和南京大学优秀博士研究生创新能力提升计划（项目编号：201601B033）研究成果之一。
Extracting Non-taxonomic Relationships for Ontology Learning of Scientific Resources
Received date: 2016-07-17
Revised date: 2016-10-08
Online published: 2016-10-20
[目的/意义] 概念非等级关系抽取是本体构建的必要步骤，学术文献作为一种重要的学术资源类型，本文主要利用其结构特点来进行本体概念非等级关系的抽取。[方法/过程] 首先，在本体概念抽取的基础上，对文献中概念的类型进行分类，以便于后期关系动词搭配的概念类型来排除不符合条件的三元组；其次，确定学术文献中的关系类型，并采用C-value方法抽取表示关系的动词，进行本体关系的表示；再次，评价概念对的关联性，利用互信息法对概念对进行排序并去除非相关概念对，实验表明该方法非常有效；最后评价概念对与关系动词的关联，分析影响三元组关联的因素，再采用实验确定模型挖掘三元组，实验比较现有的关联规则挖掘的方法。[结果/结论] 结果表明本文提出的三元组选择模型效果明显超过现有的关联规则挖掘方法，并且在语料集扩大的情况下这种优势更为明显。
蒋婷 , 孙建军 . 学术资源本体非等级关系抽取研究[J]. 图书情报工作, 2016 , 60(20) : 112 -122 . DOI: 10.13266/j.issn.0252-3116.2016.20.014
[Purpose/significance] Extracting non-taxonomic relationship is the most complex work of ontology learning. It is also an unsolved problem of ontology learning. Related research is mainly based on the domain vocabulary. This paper aims to study the scientific ontology construction, and the knowledge acquisition comes from scientific articles. This paper uses the structure of the scientific articles to extract non-taxonomic relationships. [Method/process] Firstly, based on the concept extraction of the previous work of our project, the paper classifies the concept into some categories in order to exclude some concept types with verb templates. Then, in order to define the type of the relationship, it uses C-value to extract verb represent the relationship. After that, it evaluates the relationship of the concepts, and uses MI to exclude some concepts. The experiments show that it is very effective. At last, it evaluates the relationship of concepts and verb to analyze the factors influencing the relationship, and selects a model. The experiment compares the associate rule methods. [Result/conclusion] It shows the model proposed by this paper is very effective and outperforms the associate rule methods, and it is especially significant with a spread of the corpus.
[1] SÁNCHEZ D, MORENO A. Learning non-taxonomic relationships from web documents for domain ontology construction[J]. Data & knowledge engineering, 2008, 64(3):600-623.
[2] BERLAND M, CHARNIAK E. Finding parts in very large corpora[C]//Proceedings of the 37th annual meeting of the association for computational linguistics on computational linguistics. Stroudsburg:Association for computational linguistics, 1999:57-64.
[3] GIRJU R, BADULESCU A, MOLDOVAN D. Learning semantic constraints for the automatic discovery of part-whole relations[C]//Proceedings of the 2003 conference of the North American chapter of the association for computational linguistics on human language technology-Volume 1. Stroudsburg:Association for Computational Linguistics, 2003:1-8.
[4] NENADIC' G, SPASIC' I, ANANIADOU S. Automatic discovery of term similarities using pattern mining[C]//COLING-02 on COMPUTERM 2002:second international workshop on computational terminology-Volume 14. Stroudsburg:Association for computational linguistics, 2002:1-7.
[5] PENG J, DU Y, CHEN Y, et al. Medical ontology learning based on Web resources[C]//201512th Web information system and application conference. IEEE, 2015:116-119.
[6] KAVALEC M, SVATÉK V. A study on automated relation labelling in ontology learning[J]. Ontology learning from text:methods, evaluation and applications, 2005, 123:44-59.
[7] XU Y, LI G, MOU L, et al. Learning non-taxonomic relations on demand for ontology extension[J]. International journal of software engineering and knowledge engineering, 2014, 24(8):1159-1175.
[8] PUNURU J, CHEN J. Learning non-taxonomical semantic relations from domain texts[J]. Journal of intelligent information systems, 2012, 38(1):191-207.
[9] LIU W, LIU F, WANG D, et al. Extraction of event elements based on event ontology reasoning[M]//Intelligent information and database systems. Springer, 2015:577-586.
[10] VILLAVERDE J, PERSSON A, GODOY D, et al. Supporting the discovery and labeling of non-taxonomic relationships in ontology learning[J]. Expert systems with applications, 2009, 36(7):10288-10294.
[11] GULLA J A, BRASETHVIK T, KVARV G S. Association rules and cosine similarities in ontology relationship learning[M]//Enterprise information systems. Berlin:Springer,2008:201-212.
[12] XIE C, WU J. Research of conceptual relation extraction based on improved hierarchical clustering method[J]. Open electrical & electronic engineering journal, 2014, 8:355-360.
[13] WONG M K, ABIDI S S R, JONSEN I D. A multi-phase correlation search framework for mining non-taxonomic relations from unstructured text[J]. Knowledge and information systems, 2014, 38(3):641-667.
[14] SERRA I, GIRARDI R, NOVAIS P. The problem of learning non-taxonomic relationships of ontologies from text[M]//Distributed computing and artificial intelligence. Berlin:Springer, 2012:485-492.
[15] SERRA I, GIRARDI R, NOVAIS P. Evaluating techniques for learning non-taxonomic relationships of ontologies from text[J]. Expert systems with applications, 2014, 41(11):5201-5211.
[16] PETROVA A, KISSA M. Non-taxonomic role extraction for biomedical concepts[C]//ESSLLI student session. Düsseldorf,2013:140.
[17] WEICHSELBRAUN A, WOHLGENANNT G, SCHARL A, et al. Discovery and evaluation of non-taxonomic relations in domain ontologies[J]. International journal of metadata, semantics and ontologies, 2009, 4(3):212-222.
[18] KAVALEC M, MAEDCHE A, SVÁTEK V. Discovery of lexical entries for non-taxonomic relations in ontology learning[C]//International conference on current trends in theory and practice of computer science. Berlin:Springer, 2004:249-256.
[19] MAEDCHE A, STAAB S. Mining non-taxonomic conceptual relations from text[C]//12th European knowledge acquisition workshop. London:Springer,2000:2-6.
[20] NABILA N F, BASIR N, SAUDI M M, et al. Domain specific inter textual non taxonomic extraction (DSINTE)[C]//Proceedings of the 201517th UKSIM-AMSS international conference on modelling and simulation. Cambridge:IEEE computer society, 2015:502-506.
[21] FRANTZI K, ANANIADOU S, MIMA H. Automatic recognition of multi-word terms:. the c-value/nc-value method[J]. International journal on digital libraries, 2000, 3(2):115-130.
[22] CHURCH K W, HANKS P. Word association norms, mutual information, and lexicography[J]. Computational linguistics, 1990, 16(1):22-29.
[23] TURNEY P D. Thumbs up or thumbs down?:semantic orientation applied to unsupervised classification of reviews[C]//Proceedings of the 40th annual meeting on association for computational linguistics. Stroudsburg:Association for computational linguistics, 2002:417-424.
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