Results 21 to 30 of about 8,119,426 (288)

Few-shot temporal knowledge graph completion based on meta-optimization

Complex & Intelligent Systems, 2023
Knowledge Graphs (KGs) have become an increasingly important part of artificial intelligence, and KGs have been widely used in artificial intelligence fields such as intelligent answering questions and personalized recommendation.
Lin Zhu, Luyi Bai, Shuo Han, Mingcheng Zhang   +3 more
doaj   +2 more sources

Knowledge Graph Completeness: A Systematic Literature Review [PDF]

IEEE Access, 2021
The quality of a Knowledge Graph (also known as Linked Data) is an important aspect to indicate its fitness for use in an application. Several quality dimensions are identified, such as accuracy, completeness, timeliness, provenance, and accessibility, which are used to assess the quality. While many prior studies offer a landscape view of data quality
Issa, Subhi, Adekunle, Onaopepo, Hamdi, Fayçal, Sisaid-Cherfi, Samira, Dumontier, Michel, Zaveri, Amrapali   +5 more
openaire   +5 more sources

Logic Programming for Knowledge Graph Completion. [PDF]

A knowledge graph (KG) represents a domain of interest with a graph where some of the involved entities are linked with an edge. Knowledge Graph Completion (KGC) is a well-known task for KGs which requires finding missing connections. KGC has been studied for many years with multiple solutions available based on both symbolic and sub-symbolic ...
Azzolini D., Bonato M., Gentili E., Riguzzi F.   +3 more
openaire   +2 more sources

Conflict-aware multilingual knowledge graph completion [PDF]

, 2023
Knowledge graph completion (KGC), a task that aims at predicting missing links with existing information inside a knowledge graph (KG), has emerged as a popular research area in recent years. While many existing works have demonstrated effectiveness on a
Zhang, Weihang, Sun, Jiahao, Ovidiu Şerban, Guo, Yike, Serban, Ovidiu, Yike Guo, Jiahao Sun, Weihang Zhang   +7 more
core   +1 more source

On the Aggregation of Rules for Knowledge Graph Completion

CoRR, 2023
Rule learning approaches for knowledge graph completion are efficient, interpretable and competitive to purely neural models. The rule aggregation problem is concerned with finding one plausibility score for a candidate fact which was simultaneously predicted by multiple rules. Although the problem is ubiquitous, as data-driven rule learning can result
Betz, Patrick, Lüdtke, Stefan, Meilicke, Christian, Stuckenschmidt, Heiner   +3 more
openaire   +4 more sources

Knowledge graph embeddings: open challenges and opportunities [PDF]

, 2023
While Knowledge Graphs (KGs) have long been used as valuable sources of structured knowledge, in recent years, KG embeddings have become a popular way of deriving numeric vector representations from them, for instance, to support knowledge graph ...
Russa Biswas, Lucie-Aim('e)e Kaffee, Matteo Lissandrini, Gerard (de Melo), Heiko Paulheim, Michael Cochez, Harald Sack, Stefania Dumbrava (Theis E) Jendal, Vanessa Lopez (Eneldo Loza) Menc('i)a, Vakaj (Edlira Kalemi)   +9 more
core   +3 more sources

Survey on Few-Shot Knowledge Graph Completion Technology [PDF]

Jisuanji kexue yu tansuo, 2023
Few-shot knowledge graph completion (FKGC) is a new research hotspot in the field of knowledge graph completion, which aims to complete knowledge graph with a few samples of data.
PENG Yanfei, ZHANG Ruisi, WANG Ruihua, GUO Jialong
doaj   +1 more source

Representing a Heterogeneous Pharmaceutical Knowledge-Graph with Textual Information

Frontiers in Research Metrics and Analytics, 2021
We deal with a heterogeneous pharmaceutical knowledge-graph containing textual information built from several databases. The knowledge graph is a heterogeneous graph that includes a wide variety of concepts and attributes, some of which are provided in ...
Masaki Asada, Nallappan Gunasekaran, Makoto Miwa, Yutaka Sasaki   +3 more
doaj   +1 more source

Enabling inductive knowledge graph completion via structure-aware attention network

, 2023
: Knowledge graph completion (KGC) aims at complementing missing entities and relations in a knowledge graph (KG). Popular KGC approaches based on KG embedding are typically limited to the transductive setting, i.e., all entities must be seen during ...
Jin, Qun, Qun Jin, Liu, Wei, Wang, Can, Weimin Li, Li, Weimin, Jingchao Wang, Wei Liu, Wang, Jingchao, Can Wang   +9 more
core   +1 more source

Knowledge Graph Completion Based on Half-Edge Principle [PDF]

Jisuanji gongcheng, 2020
Existing knowledge graph completion algorithms are time-consuming and inaccurate.To address these problems,this paper proposes a multi-layer convolution model based on half-edge.The model introduces the half-edge principle,and uses the descriptive ...
CHENG Tao, CHEN Heng, LI Guanyu
doaj   +1 more source