Abstract
A large number of applications which bridge web data with knowledge bases have led to an increase in the entity linking research. Candidate entity disambiguation plays an important role in the typical entity linking systems. Generally, graph-based candidate disambiguation approaches applied document-level topical coherence of candidate entities. Nevertheless, they do not make full use of abundant unambiguous entities to enrich semantic information during the disambiguation. To solve this problem, we propose a graph-based model combining semantic representation learning for entity linking. Specifically, we construct a referent graph based on semantic vectors trained from RDF data, in which we introduce the dynamic PageRank algorithm with unambiguous entities to enhance the performance of entity linking. Primary experiments show that this model outperforms state-of-the-art on benchmark datasets.
N. Ma and X. Liu—Contributed equally.
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Ma, N., Liu, X., Gao, Y. (2019). Entity Linking Based on Graph Model and Semantic Representation. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_50
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DOI: https://doi.org/10.1007/978-3-030-29551-6_50
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