TY - JOUR
T1 - SHINE+
T2 - A General Framework for Domain-Specific Entity Linking with Heterogeneous Information Networks
AU - Shen, Wei
AU - Han, Jiawei
AU - Wang, Jianyong
AU - Yuan, Xiaojie
AU - Yang, Zhenglu
N1 - Funding Information:
This work was supported in part by the National Basic Research Program of China (973 Program) under Grant No. 2014CB340505, the National Natural Science Foundation of China under Grant No. 61532010, 61502253, U1636116 and 11431006, the National 863 Program of China under Grant No. 2015AA015401, the Fundamental Research Funds for the Central Universities, Research Fund for International Young Scientists under Grant No. 61650110510, the U.S. Army Research Lab. under Cooperative Agreement No. W911NF-09-2-0053 (NSCTA), U.S. National Science Foundation IIS-1320617, IIS 16-18481, and NSF IIS 17-04532, and grant 1U54GM114838 awarded by NIGMS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - 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 I nformation 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.
AB - 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 I nformation 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.
KW - Entity linking
KW - heterogeneous information network
KW - knowledge population algorithm
KW - probabilistic linking model
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U2 - 10.1109/TKDE.2017.2730862
DO - 10.1109/TKDE.2017.2730862
M3 - Article
AN - SCOPUS:85028946936
SN - 1041-4347
VL - 30
SP - 353
EP - 366
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 2
M1 - 7990163
ER -