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Context-aware Outstanding Fact Mining from Knowledge Graphs

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DOI

  • Yueji Yang, National University of Singapore
  • ,
  • Yuchen Li, National University of Singapore
  • ,
  • Panagiotis Karras
  • Anthony K.H. Tung, National University of Singapore

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 Context-aware Outstanding Facts (COFs) for a target entity under a given context specified by a context entity. We propose FMiner, a context-aware mining framework that leverages knowledge graphs (KGs) for COF mining. FMiner generates COFs in two steps. First, it discovers top-k 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-l COFs. As such, the mining process is modeled as a top-(k,l) 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.

OriginalsprogEngelsk
TitelKDD 2021 - Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Antal sider11
UdgivelsesstedNew York
ForlagAssociation for Computing Machinery
Udgivelsesåraug. 2021
Sider2006-2016
ISBN (Elektronisk)9781450383325
DOI
StatusUdgivet - aug. 2021
Begivenhed27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021 - Virtual, Online, Singapore
Varighed: 14 aug. 202118 aug. 2021

Konference

Konference27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2021
LandSingapore
ByVirtual, Online
Periode14/08/202118/08/2021
SponsorACM SIGKDD, ACM SIGMOD

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