Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 30, Issue 2, Pages 353-366Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2017.2730862
Keywords
Entity linking; heterogeneous information network; probabilistic linking model; knowledge population algorithm
Categories
Funding
- National Basic Research Program of China (973 Program) [2014CB340505]
- National Natural Science Foundation of China [61532010, 61502253, U1636116, 11431006]
- National 863 Program of China [2015AA015401]
- Fundamental Research Funds for the Central Universities
- Research Fund for International Young Scientists [61650110510]
- U.S. Army Research Lab. [W911NF-09-2-0053]
- U.S. National Science Foundation [IIS-1320617, IIS 16-18481]
- NSF [17-04532]
- NIGMS [1U54GM114838]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1618481] Funding Source: National Science Foundation
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Heterogeneous information networks that consist of multi-type, interconnected objects are becoming increasingly popular, such as social media networks and bibliographic networks. The task of linking named entity mentions detected from unstructured Web text with their corresponding entities in a heterogeneous information network is of practical importance for the problem of information network population. This task is challenging due to name ambiguity and limited knowledge existing in the network. Most existing entity linking methods focus on linking entities with Wikipedia and cannot be applied to our task. In this paper, we present SHINE+, a general framework for linking named entitieS in Web free text with a Heterogeneous Information NEtwork. We propose a probabilistic linking model, which unifies an entity popularity model with an entity object model. As the entity knowledge contained in the information network is insufficient, we propose a knowledge population algorithm to iteratively enrich the network entity knowledge by leveraging the context information of mentions mapped by the linking model with high confidence, which subsequently boosts the linking performance. Experimental results over two real heterogeneous information networks (i.e., DBLP and IMDb) demonstrate the effectiveness and efficiency of our proposed framework in comparison with the baselines.
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