Context-aware outstanding fact mining from knowledge graphs

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...

Full description

Saved in:
Bibliographic Details
Main Authors: YANG, Yueji, LI, Yuchen, KARRAS, Panagiotis, TUNG, Anthony
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/6133
https://ink.library.smu.edu.sg/context/sis_research/article/7136/viewcontent/3447548.3467272.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-7136
record_format dspace
spelling sg-smu-ink.sis_research-71362021-09-29T12:14:04Z Context-aware outstanding fact mining from knowledge graphs YANG, Yueji LI, Yuchen KARRAS, Panagiotis TUNG, Anthony 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. 2021-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6133 info:doi/10.1145/3447548.3467272 https://ink.library.smu.edu.sg/context/sis_research/article/7136/viewcontent/3447548.3467272.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Outstanding Fact Mining Knowledge Graph Databases and Information Systems Data Storage Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Outstanding Fact Mining
Knowledge Graph
Databases and Information Systems
Data Storage Systems
spellingShingle Outstanding Fact Mining
Knowledge Graph
Databases and Information Systems
Data Storage Systems
YANG, Yueji
LI, Yuchen
KARRAS, Panagiotis
TUNG, Anthony
Context-aware outstanding fact mining from knowledge graphs
description 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.
format text
author YANG, Yueji
LI, Yuchen
KARRAS, Panagiotis
TUNG, Anthony
author_facet YANG, Yueji
LI, Yuchen
KARRAS, Panagiotis
TUNG, Anthony
author_sort YANG, Yueji
title Context-aware outstanding fact mining from knowledge graphs
title_short Context-aware outstanding fact mining from knowledge graphs
title_full Context-aware outstanding fact mining from knowledge graphs
title_fullStr Context-aware outstanding fact mining from knowledge graphs
title_full_unstemmed Context-aware outstanding fact mining from knowledge graphs
title_sort context-aware outstanding fact mining from knowledge graphs
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/6133
https://ink.library.smu.edu.sg/context/sis_research/article/7136/viewcontent/3447548.3467272.pdf
_version_ 1770575834373947392