Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2021

Abstract

An Outstanding Fact (OF) is an attribute that makes a target entity stand out from its peers. The mining of OFs has important applications, especially in Computational Journalism, such as news promotion, fact-checking, and news story finding. However, existing approaches to OF mining: (i) disregard the context in which the target entity appears, hence may report facts irrelevant to that context; and (ii) require relational data, which are often unavailable or incomplete in many application domains. In this paper, we introduce the novel problem of mining Contextaware Outstanding Facts (COFs) for a target entity under a given context specified by a context entity. We propose FMiner, a contextaware mining framework that leverages knowledge graphs (KGs) for COF mining. FMiner generates COFs in two steps. First, it discovers top-�� relevant relationships between the target and the context entity from a KG. We propose novel optimizations and pruning techniques to expedite this operation, as this process is very expensive on large KGs due to its exponential complexity. Second, for each derived relationship, we find the attributes of the target entity that distinguish it from peer entities that have the same relationship with the context entity, yielding the top- �� COFs. As such, the mining process is modeled as a top-(��,��) search problem. Context-awareness is ensured by relying on the relevant relationships with the context entity to derive peer entities for COF extraction. Consequently, FMiner can effectively navigate the search to obtain context-aware OFs by incorporating a context entity. We conduct extensive experiments, including a user study, to validate the efficiency and the effectiveness of FMiner.

Keywords

Outstanding Fact Mining, Knowledge Graph

Discipline

Databases and Information Systems | Data Storage Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Virtual Conference, 2021 August 14-18

First Page

2006

Last Page

2016

ISBN

9781450383325

Identifier

10.1145/3447548.3467272

Publisher

ACM

City or Country

Singapore

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