Abstract
Knowledge graphs (KGs) are essential repositories of structured and semi-structured knowledge which benefit various NLP applications. To utilize the knowledge in KGs to help machines to better understand plain texts, one needs to bridge the gap between knowledge and texts. In this paper, a Relation Linking System for Wikidata (RLSW) is proposed to link the relations in KGs to plain texts. The proposed system uses the knowledge in Wikidata as seeds and clusters relation mentions in text with a novel phrase similarity algorithm. To enhance the system’s ability of handling unseen expressions and make use of the location information of words to reduce false positive rate, a bag of distribution pattern modeling method is proposed. Experimental results show that the proposed approach improves traditional methods, including word based pattern and syntax feature enriched system such as OLLIE.
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Acknowledgments
This paper is supported by the national key R&D of China program (No. 2016YFB1000900), NFSC program young scholar project (No. 61502066), scientific and technological research program of Chongqing municipal education commission (No. KJ1500438), basic and frontier research project of Chongqing, China (No. cstc2015jcyjA40018).
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Yang, X., Ren, S., Li, Y., Shen, K., Li, Z., Wang, G. (2018). Relation Linking for Wikidata Using Bag of Distribution Representation. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_55
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DOI: https://doi.org/10.1007/978-3-319-73618-1_55
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