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Article: KGVQL: A knowledge graph visual query language with bidirectional transformations

TitleKGVQL: A knowledge graph visual query language with bidirectional transformations
Authors
KeywordsBidirectional transformation
Knowledge graphs
Query graph pattern
Visual query language
Issue Date2022
Citation
Knowledge-Based Systems, 2022, v. 250, article no. 108870 How to Cite?
AbstractWith the rapid development of artificial intelligence, knowledge graphs have been widely recognized as a critical component in many AI techniques and systems. A complex knowledge graph may contain hundreds of millions of nodes and edges, thus is challenging for end-users to understand and query. In this paper, we present a knowledge graph interactive visual query language, KGVQL, to improve the efficiency of end-users’ understanding and querying of knowledge graphs. Furthermore, KGVQL realizes the novel capability of flexible bidirectional transformations between query graphs and query results, therefore significantly assisting end-users in constructing queries over large and unfamiliar knowledge graphs in an incremental way. We present the visual syntax of KGVQL, discuss our design rationale behind this interactive visual query language, and illustrate a number of case studies. We empirically evaluate the effectiveness of a visual query system based on KGVQL against a number of textual and visual query environments over a large knowledge graph, DBpedia. Our evaluation demonstrates the superiority of KGVQL in effectiveness and accuracy.
Persistent Identifierhttp://hdl.handle.net/10722/330813
ISSN
2023 Impact Factor: 7.2
2023 SCImago Journal Rankings: 2.219
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, Pengkai-
dc.contributor.authorWang, Xin-
dc.contributor.authorFu, Qiang-
dc.contributor.authorYang, Yajun-
dc.contributor.authorLi, Yuan Fang-
dc.contributor.authorZhang, Qingpeng-
dc.date.accessioned2023-09-05T12:14:48Z-
dc.date.available2023-09-05T12:14:48Z-
dc.date.issued2022-
dc.identifier.citationKnowledge-Based Systems, 2022, v. 250, article no. 108870-
dc.identifier.issn0950-7051-
dc.identifier.urihttp://hdl.handle.net/10722/330813-
dc.description.abstractWith the rapid development of artificial intelligence, knowledge graphs have been widely recognized as a critical component in many AI techniques and systems. A complex knowledge graph may contain hundreds of millions of nodes and edges, thus is challenging for end-users to understand and query. In this paper, we present a knowledge graph interactive visual query language, KGVQL, to improve the efficiency of end-users’ understanding and querying of knowledge graphs. Furthermore, KGVQL realizes the novel capability of flexible bidirectional transformations between query graphs and query results, therefore significantly assisting end-users in constructing queries over large and unfamiliar knowledge graphs in an incremental way. We present the visual syntax of KGVQL, discuss our design rationale behind this interactive visual query language, and illustrate a number of case studies. We empirically evaluate the effectiveness of a visual query system based on KGVQL against a number of textual and visual query environments over a large knowledge graph, DBpedia. Our evaluation demonstrates the superiority of KGVQL in effectiveness and accuracy.-
dc.languageeng-
dc.relation.ispartofKnowledge-Based Systems-
dc.subjectBidirectional transformation-
dc.subjectKnowledge graphs-
dc.subjectQuery graph pattern-
dc.subjectVisual query language-
dc.titleKGVQL: A knowledge graph visual query language with bidirectional transformations-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.knosys.2022.108870-
dc.identifier.scopuseid_2-s2.0-85131456070-
dc.identifier.volume250-
dc.identifier.spagearticle no. 108870-
dc.identifier.epagearticle no. 108870-
dc.identifier.isiWOS:000811313600009-

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