Contextualized ranking of entity types based on knowledge graphs

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• 作者：Alberto Tonon^a ; ^{alberto.tonon@unifr.ch" class="auth_mail" title="E-mail the corresponding author} ; Michele Catasta^b ; ^{michele.catasta@epfl.ch" class="auth_mail" title="E-mail the corresponding author} ; Roman Prokofyev^a ; ^{roman.prokofyev@unifr.ch" class="auth_mail" title="E-mail the corresponding author} ; Gianluca Demartini^c ; ^{g.demartini@sheffield.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; Karl Aberer^b ; ^{karl.aberer@epfl.ch" class="auth_mail" title="E-mail the corresponding author} ; Philippe Cudré ; -Mauroux^a ; ^{philippe.cudre-mauroux@unifr.ch" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Entity typing ; Ranking ; Context ; Crowdsourcing ; Knowledge graphs
• 刊名：Web Semantics: Science, Services and Agents on the World Wide Web
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：37-38
• 期：Complete
• 页码：170-183
• 全文大小：1843 K

文摘

A large fraction of online queries targets entities. For this reason, Search Engine Result Pages (SERPs) increasingly contain information about the searched entities such as pictures, short summaries, related entities, and factual information. A key facet that is often displayed on the SERPs and that is instrumental for many applications is the entity type. However, an entity is usually not associated to a single generic type in the background knowledge graph but rather to a set of more specific types, which may be relevant or not given the document context. For example, one can find on the Linked Open Data cloud the fact that Tom Hanks is a person, an actor, and a person from Concord, California. All these types are correct but some may be too general to be interesting (e.g., person), while other may be interesting but already known to the user (e.g., actor), or may be irrelevant given the current browsing context (e.g., person from Concord, California). In this paper, we define the new task of ranking entity types given an entity and its context. We propose and evaluate new methods to find the most relevant entity type based on collection statistics and on the knowledge graph structure interconnecting entities and types. An extensive experimental evaluation over several document collections at different levels of granularity (e.g., sentences, paragraphs) and different type hierarchies (including DBpedia, Freebase, and schema.org) shows that hierarchy-based approaches provide more accurate results when picking entity types to be displayed to the end-user.