Entity disambiguation to Wikipedia using collective ranking |
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| Affiliation: | 1. Department of Electronic Engineering, Tsinghua University, Beijing, China;2. School of Computing and Information Sciences, Florida International University, Miami, FL, USA;1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran;2. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Iran;1. Universitat Politècnica de València, 46022 Valencia, Spain;2. Sciling, 46022 Valencia, Spain;3. Brown University, Providence, RI 02912, United States;4. École Polytechnique de Montréal, QC 06079, Canada;1. Electrical and Computer Engineering, Shahid Beheshti University, G.C., Tehran, Iran;2. Electrical and Computer Engineering, Tehran University, Tehran, Iran;3. Research School of Computer Science, Australian National University, Canberra, Australia;1. Sorbonne Universités, UPMC Univ Paris 06, CNRS UMR 7606, LIP6, F-75005, Paris, France;2. Toulouse University UPS IRIT 118 route de Narbonne, 31062 Toulouse Cedex 9, France;3. School of Communication & Information (SC&I) Rutgers University 4 Huntington St, New Brunswick, NJ 08901, USA |
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| Abstract: | Entity disambiguation is a fundamental task of semantic Web annotation. Entity Linking (EL) is an essential procedure in entity disambiguation, which aims to link a mention appearing in a plain text to a structured or semi-structured knowledge base, such as Wikipedia. Existing research on EL usually annotates the mentions in a text one by one and treats entities independent to each other. However this might not be true in many application scenarios. For example, if two mentions appear in one text, they are likely to have certain intrinsic relationships. In this paper, we first propose a novel query expansion method for candidate generation utilizing the information of co-occurrences of mentions. We further propose a re-ranking model which can be iteratively adjusted based on the prediction in the previous round. Experiments on real-world data demonstrate the effectiveness of our proposed methods for entity disambiguation. |
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