Entity Profiling in Knowledge Graphs
Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8985548/ |
| _version_ | 1818922065685643264 |
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| author | Xiang Zhang Qingqing Yang Jinru Ding Ziyue Wang |
| author_facet | Xiang Zhang Qingqing Yang Jinru Ding Ziyue Wang |
| author_sort | Xiang Zhang |
| collection | DOAJ |
| description | Knowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs. In this work, we present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model, which is a scalable representation learning model to produce a multi-pattern entity embedding. We fully evaluate the quality of entity profiless generated from real KGs. The results show that our approach facilitates human understanding of entities in KGs. |
| first_indexed | 2024-12-20T01:47:37Z |
| format | Article |
| id | doaj.art-14f262b6270747d89ca8ced872f97776 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:47:37Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-14f262b6270747d89ca8ced872f977762022-12-21T19:57:42ZengIEEEIEEE Access2169-35362020-01-018272572726610.1109/ACCESS.2020.29715678985548Entity Profiling in Knowledge GraphsXiang Zhang0Qingqing Yang1Jinru Ding2Ziyue Wang3https://orcid.org/0000-0002-6080-1040School of Computer Science and Engineering, Southeast University, Nanjing, ChinaSoutheast University–Monash University Joint Graduate School, Suzhou, ChinaSchool of Software Engineering, Southeast University, Suzhou, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, ChinaKnowledge Graphs (KGs) are graph-structured knowledge bases storing factual information about real-world entities. Understanding the uniqueness of each entity is crucial to the analyzing, sharing, and reusing of KGs. Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs. In this work, we present a novel profiling approach to identify distinctive entity features. The distinctiveness of features is carefully measured by a HAS model, which is a scalable representation learning model to produce a multi-pattern entity embedding. We fully evaluate the quality of entity profiless generated from real KGs. The results show that our approach facilitates human understanding of entities in KGs.https://ieeexplore.ieee.org/document/8985548/Knowledge graphentity profilingrepresentation learning |
| spellingShingle | Xiang Zhang Qingqing Yang Jinru Ding Ziyue Wang Entity Profiling in Knowledge Graphs IEEE Access Knowledge graph entity profiling representation learning |
| title | Entity Profiling in Knowledge Graphs |
| title_full | Entity Profiling in Knowledge Graphs |
| title_fullStr | Entity Profiling in Knowledge Graphs |
| title_full_unstemmed | Entity Profiling in Knowledge Graphs |
| title_short | Entity Profiling in Knowledge Graphs |
| title_sort | entity profiling in knowledge graphs |
| topic | Knowledge graph entity profiling representation learning |
| url | https://ieeexplore.ieee.org/document/8985548/ |
| work_keys_str_mv | AT xiangzhang entityprofilinginknowledgegraphs AT qingqingyang entityprofilinginknowledgegraphs AT jinruding entityprofilinginknowledgegraphs AT ziyuewang entityprofilinginknowledgegraphs |