Papers Triple Classification
“Triple Classification” 태그가 달린 논문 51편 · 필터 해제
GreenKGC: A Lightweight Knowledge Graph Completion Method
Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs). Most prior KGC work focuses on learning embeddings for entities and relations through a simple scoring f…
Edge-computingKnowledge Graph CompletionKnowledge GraphsLink Prediction+2Language Models as Knowledge Embeddings
Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding entities and relations into continuous vector spaces. Existing methods are mainly structure-based or description-based. Structure-based methods lear…
Contrastive LearningLink PredictionTriple ClassificationOneRel:Joint Entity and Relation Extraction with One Module in One Step
Joint entity and relation extraction is an essential task in natural language processing and knowledge graph construction. Existing approaches usually decompose the joint extraction task into several basic modules or pro…
graph constructionJoint Entity and Relation ExtractionRelationRelation Extraction+1Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs. In this paper, we propose a novel pre-training-then-fine-tuning f…
Entity AlignmentGraph Representation LearningKnowledge GraphsRepresentation Learning+2Triple Classification for Scholarly Knowledge Graph Completion
Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora o…
ClassificationKnowledge Graph CompletionKnowledge GraphsLink Prediction+2Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation
Existing technologies expand BERT from different perspectives, e.g. designing different pre-training tasks, different semantic granularities, and different model architectures. Few models consider expanding BERT from dif…
Language ModelingLanguage ModellingTriple ClassificationDifferentially Private Federated Knowledge Graphs Embedding
Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make f…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink Prediction+3Membership Inference Attacks on Knowledge Graphs
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training. MIAs have provoked many discussions in the information security community since they give rise to severe data pr…
Graph EmbeddingInference AttackKnowledge Graph CompletionKnowledge Graph Embedding+4CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
We present CoDEx, a set of knowledge graph completion datasets extracted from Wikidata and Wikipedia that improve upon existing knowledge graph completion benchmarks in scope and level of difficulty. In terms of scope, C…
BenchmarkingKnowledge Graph CompletionKnowledge GraphsLink Prediction+1TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces
Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG into f(KG) $\in$ R…
Knowledge GraphsLink PredictionRelationTriple ClassificationLearning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework
Many large-scale knowledge graphs are now available and ready to provide semantically structured information that is regarded as an important resource for question answering and decision support tasks. However, they are …
General ClassificationKnowledge Graph CompletionKnowledge GraphsLink Prediction+3On the Role of Conceptualization in Commonsense Knowledge Graph Construction
Commonsense knowledge graphs (CKGs) like Atomic and ASER are substantially different from conventional KGs as they consist of much larger number of nodes formed by loosely-structured text, which, though, enables them to …
Diversitygraph constructionKnowledge GraphsTriple ClassificationRevisiting Evaluation of Knowledge Base Completion Models
Representing knowledge graphs (KGs) by learning embeddings for entities and relations has led to accurate models for existing KG completion benchmarks. However, due to the open-world assumption of existing KGs, evaluatio…
Knowledge Base CompletionKnowledge Graph CompletionKnowledge GraphsTriple ClassificationDebate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs
We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which…
Common Sense ReasoningFact CheckingFairnessKnowledge Graphs+2Reasoning on Knowledge Graphs with Debate Dynamics
We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which…
General ClassificationKnowledge GraphsLink PredictionReinforcement Learning+1KG-BERT: BERT for Knowledge Graph Completion
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models for knowledge graph completion. We treat t…
Knowledge Graph CompletionKnowledge GraphsLanguage ModelingLanguage Modelling+5A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To this end, we introduce a novel embedding …
DecoderGeneral ClassificationGraph EmbeddingKnowledge Graph Embedding+2Neural Markov Logic Networks
We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an exponential-family model for modelling dis…
Knowledge Base CompletionRelational ReasoningTriple ClassificationLogic Rules Powered Knowledge Graph Embedding
Large scale knowledge graph embedding has attracted much attention from both academia and industry in the field of Artificial Intelligence. However, most existing methods concentrate solely on fact triples contained in t…
Graph EmbeddingKnowledge Graph EmbeddingLink PredictionTriple ClassificationKnowledge Representation Learning: A Quantitative Review
Knowledge representation learning (KRL) aims to represent entities and relations in knowledge graph in low-dimensional semantic space, which have been widely used in massive knowledge-driven tasks. In this article, we in…
General ClassificationInformation RetrievalKnowledge Graph CompletionLanguage Modeling+7