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Knowledge and Information Systems

Erschienen in:

01.04.2019 | Regular Paper

HEEL: exploratory entity linking for heterogeneous information networks

verfasst von: Chengyu Wang, Xiaofeng He, Aoying Zhou

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2020

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Abstract

A heterogeneous information network (HIN) is a ubiquitous data model, consisting of multiple types of entities and relations. Names of entities in HINs are inherently ambiguous, making it difficult to fully disambiguate a HIN. In this paper, we introduce the task of exploratory entity linking for HINs. Given a partially disambiguated HIN, we aim at linking ambiguous names to disambiguated entities in the HIN if their referent entities are present. We also try to “explore” other alternatives by discovering new entities and adding them to the HIN. A partial classification EM-based approach is proposed to address this task. We present a constrained probability propagation model to link surface names to entities in the HIN. New entity detection process is modeled as a maximum edge weight clique problem. Experiments illustrate that our method outperforms state-of-the-art methods for entity linking with HINs and author name disambiguation.

Vorheriger Artikel Dataset-Transformation: improving clustering by enhancing the structure with DipScaling and DipTransformation

Nächster Artikel Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach

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In meta-path description, we use P, A, T, V and Y to represent any nodes (i.e., entities) in the FDS with the type of paper, author, term, venue and year, respectively.

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Titel: HEEL: exploratory entity linking for heterogeneous information networks
verfasst von: Chengyu Wang
Xiaofeng He
Aoying Zhou
Publikationsdatum: 01.04.2019
Verlag: Springer London
Erschienen in: Knowledge and Information Systems / Ausgabe 2/2020
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI: https://doi.org/10.1007/s10115-019-01354-1

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