dc.contributor.author | Yang, E | |
dc.contributor.author | Hao, F | |
dc.contributor.author | Yang, Y | |
dc.contributor.author | De Maio, C | |
dc.contributor.author | Nasridinov, A | |
dc.contributor.author | Min, G | |
dc.contributor.author | Yang, LT | |
dc.date.accessioned | 2021-06-22T05:38:59Z | |
dc.date.issued | 2021-06-17 | |
dc.description.abstract | Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. With this backdrop, automatic entity summarization has received much attention in recent years, aiming to select the most concise and most typical facts that depict an entity in brief from lengthy RDF data. As new descriptions of entity are continually coming, creating a compact summary of entity quickly from a lengthy knowledge graph is challenging. To address this problem, this paper firstly formulates the problem and proposes a novel approach of Incremental Entity Summarization by leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove the rationality of our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate that the proposed method IES-FCA can save about 8.7% of time consumption for all entities than the non-incremental entity summarization approach KAFCA at best. As for the effectiveness, IES-FCA outperforms the state-of-the-art algorithms in terms of F1-measure, MAP, and NDCG. | en_GB |
dc.description.sponsorship | National Natural Science Foundation of China | en_GB |
dc.description.sponsorship | Fundamental Research Funds for the Central Universities | en_GB |
dc.identifier.citation | Published online 17 June 2021 | en_GB |
dc.identifier.doi | 10.1109/tsc.2021.3090276 | |
dc.identifier.grantnumber | 61702317 | en_GB |
dc.identifier.grantnumber | GK202103080 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/126127 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. | en_GB |
dc.subject | Resource description framework | en_GB |
dc.subject | Lattices | en_GB |
dc.subject | Computer science | en_GB |
dc.subject | Software algorithms | en_GB |
dc.subject | Context modeling | en_GB |
dc.subject | Search engines | en_GB |
dc.subject | Probability distribution | en_GB |
dc.title | Incremental Entity Summarization with Formal Concept Analysis | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2021-06-22T05:38:59Z | |
dc.identifier.issn | 1939-1374 | |
dc.description | This is the author's accepted manuscript; the final version is available from IEEE via the DOI in this record. | en_GB |
dc.identifier.journal | IEEE Transactions on Services Computing | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2021-06-14 | |
rioxxterms.funder | European Union Horizon 2020 | en_GB |
rioxxterms.identifier.project | Marie Sklodowska-Curie grant agreement No 840922 | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2021-06-14 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2021-06-21T07:19:13Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2021-06-22T05:39:16Z | |
refterms.panel | B | en_GB |
rioxxterms.funder.project | e4e40680-5a3e-4e7d-be59-2b310fb34b18 | en_GB |