基于多维信息融合的知识库问答实体链接

doi:10.16451/j.cnki.issn1003-6059.201907008

Abstract
Figure/Table
References (0)
Related Citation (4)

Download: PDF (983 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

The entity linking task of knowledge base question answering(KBQA) is to accurately link the content of questions to the entities in the knowledge base. Recall rate and accuracy of linked entities cannot be balanced by most of the current methods, and only the text information is applied to distinguish and filter the entities. Therefore, a multi-dimensional information integration based entity linking for KBQA(MDIIEL) combining multi-dimensional features based on merging substeps is proposed in this paper. By representing learning methods, information such as text symbols, entities and question types, and semantic structure expressions of entities in the knowledge base are integrated and introduced into the entity linking task. The differentiation of similar entities is strengthened, and candidate sets are reduced while the accuracy is improved. The experiment proves that the MDIIEL model makes a holistic improvement on the entity linking task compared with the current methods, and it achieves the best current linking results on most indicators.

Key words： Entity Linking Entity Disambiguation Representation Learning Semantic Structure Feature of Knowledge Base

Received: 28 January 2019

ZTFLH:

TP 391.1

Fund:

Supported by National Key R&D Program of China(No.2017YFB1002302), National Natural Science Foundation of China(No.61772501,61572473,61572469,91646120)

About author:: ZENG Yutao, master student. His research interests include knowledge graph and natural language processing.LIN Xiexiong, master. His research inte-rests include knowledge graph and natural language processing.JIN Xiaolong(Corresponding author), Ph.D., professor. His research interests include know-ledge graph and knowledge engineering.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZENG Yutao
	LIN Xiexiong
	JIN Xiaolong
	XI Pengbi
	WANG Yuanzhuo

Cite this article:

ZENG Yutao,LIN Xiexiong,JIN Xiaolong等. Multi-dimensional Information Integration Based Entity Linking for Knowledge Base Question Answering[J]. , 2019, 32(7): 642-651.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201907008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I7/642

[1] 陆伟,武川.实体链接研究综述.情报学报, 2015, 34(1): 105-112.
(LU W, WU C. Literature Review on Entity Linking. Journal of the China Society for Scientific and Technical Information, 2015, 34(1): 105-112.)
[2] SHEN W, WANG J Y, HAN J W. Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(2): 443-460.
[3] STERN R, SAGOT B, B CHET F. A Joint Named Entity Recognition and Entity Linking System // Proc of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data. Stroudsburg, USA: ACL, 2012: 52-60.
[4] SIL A, YATES A. Re-ranking for Joint Named-Entity Recognition and Linking // Proc of the 22nd ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2013: 2369-2374.
[5] BORDES A, USUNIER N, CHOPRA S, et al. Large-Scale Simple Question Answering with Memory Networks[C/OL]. [2019-01-01]. https://arxiv.org/pdf/1506.02075.pdf.
[6] GOLUB D, HE X D. Character-Level Question Answering with Attention[C/OL]. [2019-01-01]. https://arxiv.org/pdf/1604.00727.pdf.
[7] YIN W P, YU M, XIANG B, et al. Simple Question Answering by Attentive Convolutional Neural Network[C/OL]. [2019-01-01]. https://arxiv.org/pdf/1606.03391.pdf.
[8] LUKOVNIKOV D, FISCHER A, LEHMANN J, et al. Neural Network-Based Question Answering over Knowledge Graphs on Word and Character Level // Proc of the 26th International Conference on World Wide Web. Berlin, Germany: Springer, 2017: 1211-1220.
[9] YU M, YIN W P, HASAN K, et al. Improved Neural Relation Detection for Knowledge Base Question Answering // Proc of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2017, I: 571-581.
[10] PROKHORENKOVA L, GUSEV G, VOROBEV A, et al. Ca-tBoost: Unbiased Boosting with Categorical Features[C/OL]. [2019-01-01]. https://arxiv.org/pdf/1706.09516.pdf.
[11] 詹晨迪,凌震华,戴礼荣.面向知识库问答中复述问句评分的词向量构建方法.模式识别与人工智能, 2016, 29(9): 825-831.
(ZHAN C D, LING Z H, DAI L R. Learning Word Embeddings for Paraphrase Scoring in Knowledge Base Based Question Answering. Pattern Recognition and Artificial Intelligence, 2016, 29(9):825-831.)
[12] PENNINGTON J, SOCHER R, MANNING C. Glove: Global Ve-ctors for Word Representation // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2014: 1532-1543.
[13] 吴运兵,朱丹红,廖祥文,等.路径张量分解的知识图谱推理算法.模式识别与人工智能, 2017, 30(5): 473-480.
(WU Y B, ZHU D H, LIAO X W, et al. Knowledge Graph Reasoning Based on Paths of Tensor Factorization. Pattern Recognition and Artificial Intelligence, 2017, 30(5): 473-480.
[14] BORDES A, USUNIER N, GARCIA-DURAN A, et al. Translating Embeddings for Modeling Multi-relational Data[C/OL]. [2019-01-01]. http://www.thespermwhale.com/jaseweston/papers/CR_paper_nips13.pdf.
[15] WANG Z, ZHANG J W, FENG J L, et al. Knowledge Graph Embedding by Translating on Hyperplanes // Proc of the 28th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2014: 1112-1119.
[16] LIN Y K, LIU Z Y, SUN M S, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion // Proc of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2015: 2181-2187.
[17] JI G L,HE S Z, XU L H, et al. Knowledge Graph Embedding via Dynamic Mapping Matrix // Proc of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg, USA: ACL, 2015, I: 687-696.
[18] CAI Q Q, YATES A. Large-Scale Semantic Parsing via Schema Matching and Lexicon Extension // Proc of the 51st Annual Mee-ting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2013, 1: 423-433.
[19] BERANT J, CHOU A, FROSTIG R, et al. Semantic Parsing on Freebase from Question-Answer Pairs // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2013: 1533-1544.
[20] QIU Y Q, LI M L, WANG Y Z, et al. Hierarchical Type Constrained Topic Entity Detection for Knowledge Base Question Answering // Proc of the Companion of the Web Conference. Berlin, Germany: Springer, 2018: 35-36.
[21] BAST H, HAUSSMANN E. More Accurate Question Answering on Freebase // Proc of the 24th ACM International Conference on Information and Knowledge Management. New York, USA: ACM, 2015: 1431-1440.
[22] CHEN B, AN B, SUN L, et al. Semi-Supervised Lexicon Learning for Wide-Coverage Semantic Parsing // Proc of the 27th International Conference on Computational Linguistics. Stroudsburg, USA: ACL, 2018: 892-904.
[23] XU K, REDDY S, FENG Y S, et al. Question Answering on Freebase via Relation Extraction and Textual Evidence // Proc of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA: ACL, 2016, I: 2326-2336.
[24] JAIN S. Question Answering over Knowledge Base Using Factual Memory Networks // Proc of the NAACL Student Research Workshop. Stroudsburg, USA: ACL, 2016: 109-115.
[25] WANG Y, ZHANG R C, XU C, et al. The APVA-TURBO Approach to Question Answering in Knowledge Base // Proc of the 27th International Conference on Computational Linguistics. Stroudsburg, USA: ACL, 2018: 1998-2009.