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KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion [PDF]
Conference on Empirical Methods in Natural Language ProcessingKnowledge Graph Completion (KGC) is crucial for addressing knowledge graph incompleteness and supporting downstream applications. Many models have been proposed for KGC.
Yanbin Wei +3 more
semanticscholar +1 more source
AYNEC: All you need for evaluating completion techniques in knowledge graphs [PDF]
, 2019 The popularity of knowledge graphs has led to the development of techniques to refine them and increase their quality. One of the main refinement tasks is completion (also known as link prediction for knowledge graphs), which seeks to add missing triples
Ayala Hernández, Daniel +5 more
core +1 more source
A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications
Mathematics, 2023 As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios.
Yong Chen +5 more
doaj +1 more source
Knowledge Graph Completion with Relation-Aware Anchor Enhancement [PDF]
AAAI Conference on Artificial IntelligenceText-based knowledge graph completion methods take advantage of pre-trained language models (PLM) to enhance intrinsic semantic connections of raw triplets with detailed text descriptions.
Duanyang Yuan +6 more
semanticscholar +1 more source
Findings, 2022 In recent years, pre-trained language models (PLMs) have been shown to capture factual knowledge from massive texts, which encourages the proposal of PLM-based knowledge graph completion (KGC) models. However, these models are still quite behind the SOTA
Xin Lv +7 more
semanticscholar +1 more source
Open-World Knowledge Graph Completion Based on Bayesian Network [PDF]
Jisuanji gongcheng, 2021 The relations among entities in Knowledge Graph(KG) are usually interdependent, and this interdependency can be leveraged to construct more triples based on new entities in open-world data to complete KG.Bayesian Network(BN) is an effective model for ...
LI Xinbai, WU Xinran, YUE Kun
doaj +1 more source
Learning Context-based Embeddings for Knowledge Graph Completion
Journal of Data and Information Science, 2022 Due to the incompleteness nature of knowledge graphs (KGs), the task of predicting missing links between entities becomes important. Many previous approaches are static, this posed a notable problem that all meanings of a polysemous entity share one ...
Pu Fei +3 more
doaj +1 more source
Improved Knowledge Graph Completion Method for Capsule Network [PDF]
Jisuanji gongcheng, 2020 In order to complete the missing relations between entities of knowledge graph,this paper proposes an improved knowledge graph completion method for capsule network.First,the triplets are presented as a 3-column matrix,which is convolved with multiple ...
WANG Weimei, SHI Yimin, LI Guanyu
doaj +1 more source
Completeness-Aware Rule Learning from Knowledge Graphs [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2017 Knowledge graphs (KGs) are huge collections of primarily encyclopedic facts that are widely used in entity recognition, structured search, question answering, and similar. Rule mining is commonly applied to discover patterns in KGs. However, unlike in traditional association rule mining, KGs provide a setting with a high degree of incompleteness, which
Pellissier Tanon, T. +4 more
openaire +4 more sources
Temporal Knowledge Graph Completion Using Box Embeddings
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp.
Messner, Johannes +2 more
openaire +2 more sources