Results 31 to 40 of about 35,565 (288)
Assessing LLMs Suitability for Knowledge Graph Completion
Recent work has shown the capability of Large Language Models (LLMs) to solve tasks related to Knowledge Graphs, such as Knowledge Graph Completion, even in Zero- or Few-Shot paradigms. However, they are known to hallucinate answers, or output results in a non-deterministic manner, thus leading to wrongly reasoned responses, even if they satisfy the ...
Vasile Ionut-Remus Iga +1 more
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QubitE:Qubit Embedding for Knowledge Graph Completion [PDF]
Jisuanji kexue, 2023 The knowledge graph completion task completes the knowledge graph by predicting missing facts in the knowledge graph.The quantum-based knowledge graph embedding(KGE) model uses variational quantum circuits to score triples by mea-suring the probability ...
LIN Xueyuan, E Haihong , SONG Wenyu, LUO Haoran, SONG Meina
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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 +9 more
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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. +3 more
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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 +3 more
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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
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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 +3 more
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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 +9 more
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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
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Knowledge Graph Completion with Counterfactual Augmentation
Proceedings of the ACM Web Conference 2023, 2023 Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph Completion (KGC) by modeling how entities and relations interact in recent years. However, most of them are designed to learn from the observed graph structure, which appears to have imbalanced relation distribution during the training stage.
Heng Chang, Jie Cai, Jia Li 0009
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