计算机科学 ›› 2019, Vol. 46 ›› Issue (11): 209-215.doi: 10.11896/jsjkx.181001939
李智星1,2, 任诗雅1,2, 王化明1,2, 沈柯1
LI Zhi-xing1,2, REN Shi-ya1,2, WANG Hua-ming1,2, SHEN Ke1
摘要: 知识图谱用一种结构化的方式存储实体、实体的属性以及实体之间的关系。由于知识图谱中的知识易于被计算机处理,因此它在许多自然语言处理任务中都起着至关重要的作用。虽然从绝对数量来看,现有的知识图谱已经包含了海量的三元组事实,但是与真实世界中存在的知识相比它远远不够。因此,如何完善知识图谱成为目前的研究热点。现有的研究方向主要分为内部推理和外部抽取两类,然而这些方法仍有很大的提升空间:一方面,由于知识图谱内部知识存在错误或缺失,可能会在推理时产生错误的扩散;另一方面,现有的知识抽取方法主要集中于对实体类型、关系等知识的抽取,从而导致抽取的知识不够全面。鉴于此,提出了一种基于非结构化文本增强关联规则的知识推理方法。该方法从非结构化文本表述中抽象出文本表述模式,并以词语分布袋的形式对其进行表示,进而结合知识图谱已有的知识构建关联规则。与传统关联规则的区别在于,该方法得到的关联规则可以通过与非结构化文本匹配的方式来完成知识推理。实验结果表明,与传统方法相比,该方法可以高效地从非结构化文本中推理出数量更大且质量更高的三元组知识。
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[1]QI G L,GAO H,WU T X.The Research Advances of Knowledge Graph[J].Technology Intelligence Engineering,2017,3(1):4-25.(in Chinese) 漆桂林,高桓,吴天星.知识图谱研究进展[J].情报工程,2017,3(1):4-25. [2]YIH W T,CHANG M W,HE X,et al.Semantic Parsing via Staged Query Graph Generation:Question Answering with Knowledge Base[C]∥Meeting of the Association for Computational Linguistics and the,International Joint Conference on Natural Language Processing.2015:1321-1331. [3]LU W,WU C.Literature Review on Entity Linking[J].Technology Intelligence Engineering,2015,34(1):105-112.(in Chinese) 陆伟,武川.实体链接研究综述[J].情报学报,2015,34(1):105-112. [4]AUER S,BIZER C,KOBILAROV G,et al.Dbpedia:A nucleus for a web of open data[M]∥The semantic web.Springer,Berlin,Heidelberg,2007:722-735. [5]VRANDEČIćD,KRÖTZSCH M.Wikidata:a free collaborative knowledgebase[J].Communications of the ACM,2014,57(10):78-85. [6]SUCHANEK F M,KASNECI G,WEIKUM G.Yago:a core of semantic knowledge[C]∥Proceedings of the 16th international conference on World Wide Web.ACM,2007:697-706. [7]BOLLACKER K,EVANS C,PARITOSH P,et al.Freebase:a collaboratively created graph database for structuring human knowledge[C]∥Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data.ACM,2008:1247-1250. [8]LIU L,REN X,ZHU Q,et al.Heterogeneous Supervision forRelation Extraction:A Representation Learning Approach[C]∥Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing.2017:46-56. [9]YANG X,REN S,LI Y,et al.Relation Linking for WikidataUsing Bag of Distribution Representation[C]∥National CCF Conference on Natural Language Processing and Chinese Computing.Springer,Cham,2017:652-661. [10]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating embeddings for modeling multi-relational data[C]∥Advances in Neural Information Processing Systems.2013:2787-2795. [11]WANG Z,ZHANG J,FENG J,et al.Knowledge Graph Embedding by Translating on Hyperplanes[C]∥AAAI.2014,14:1112-1119. [12]GALÁRRAGA L A,TEFLIOUDI C,HOSE K,et al.AMIE:association rule mining under incomplete evidence in ontological knowledge bases[C]∥Proceedings of the 22nd International Conference on World Wide Web.ACM,2013:413-422. [13]GALÁRRAGA L,TEFLIOUDI C,HOSE K,et al.Fast rulemining in ontological knowledge bases with AMIE \$\$+ \$\$+[J].The International Journal on Very Large Data Bases,2015,24(6):707-730. [14]WANG Z,LI J.RDF2Rules:Learning Rules from RDF Knowledge Bases by Mining Frequent Predicate Cycles[DB/OL].(2015-12-24)[2018-08-20].https://arxiv.org/abs/1512.07734. [15]ZENG D,LIU K,CHEN Y,et al.Distant supervision for relation extraction via piecewise convolutional neural networks[C]∥Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.2015:1753-1762. [16]LIN Y,SHEN S,LIU Z,et al.Neural relation extraction withselective attention over instances[C]∥Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.2016:2124-2133. [17]LI Q,JI H.Incremental joint extraction of entity mentions and relations[C]∥Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics.2014,1:402-412. [18]MIWA M,SASAKI Y.Modeling joint entity and relation extraction with table representation[C]∥Proceedings of the 2014 Conference on Empirical Methods in Natural Language Proces-sing (EMNLP).2014:1858-1869. [19]REN X,WU Z,HE W,et al.Cotype:Joint extraction of typed entities and relations with knowledge bases[C]∥Proceedings of the 26th International Conference on World Wide Web.International World Wide Web Conferences Steering Committee.2017:1015-1024. [20]RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492-1496. [21]KIM Y.Convolutional neural networks for sentence classification[C]∥Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing.2014:1746-1751. [22]BERGER M J.Large scale multi-label text classification with semantic word vectors[R].Stanford University,2015. |
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