Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Simple Rule Injection for ComplEx Embeddings
Recent works in neural knowledge graph inference attempt to combine logic rules with knowledge graph embeddings to benefit from prior knowledge. However, they usually cannot avoid rule grounding, and injecting a diverse …
Knowledge Graph CompletionKnowledge Graph EmbeddingsA Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects
Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from…
Knowledge Graph CompletionKnowledge GraphsMissing ElementsTemporal Knowledge Graph CompletionTowards Semantically Enriched Embeddings for Knowledge Graph Completion
Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the sema…
Inductive Link PredictionKnowledge Graph CompletionLink PredictionType predictionFast Knowledge Graph Completion using Graphics Processing Units
Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better kno…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1CausE: Towards Causal Knowledge Graph Embedding
Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge gr…
DisentanglementGraph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingSeparate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion
Knowledge graph completion (KGC) is the task of inferencing missing facts from any given knowledge graphs (KG). Previous KGC methods typically represent knowledge graph entities and relations as trainable continuous embe…
Inductive BiasKnowledge Graph CompletionKnowledge GraphsRelationDipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting
Knowledge Graph Completion (KGC) often requires both KG structural and textual information to be effective. Pre-trained Language Models (PLMs) have been used to learn the textual information, usually under the fine-tune …
Knowledge Graph CompletionExploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion
Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is als…
Knowledge Graph CompletionLogical ReasoningTransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation
This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+3DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion
To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples. Among those, several works have achieved …
Entity EmbeddingsKnowledge Graph CompletionKnowledge GraphsRelation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion
Few-shot knowledge graph completion (FKGC) task aims to predict unseen facts of a relation with few-shot reference entity pairs. Current approaches randomly select one negative sample for each reference entity pair to mi…
Knowledge Graph CompletionRelationContextual Dictionary Lookup for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to solve the incompleteness of knowledge graphs (KGs) by predicting missing links from known triples, numbers of knowledge graph embedding (KGE) models have been proposed to perform …
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1Revisiting Inferential Benchmarks for Knowledge Graph Completion
Knowledge Graph (KG) completion is the problem of extending an incomplete KG with missing facts. A key feature of Machine Learning approaches for KG completion is their ability to learn inference patterns, so that the pr…
Knowledge Graph CompletionA Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers
Relation extraction (RE) involves identifying the relations between entities from underlying content. RE serves as the foundation for many natural language processing (NLP) and information retrieval applications, such as…
Information RetrievalKnowledge Graph CompletionQuestion AnsweringRelation+4InGram: Inductive Knowledge Graph Embedding via Relation Graphs
Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive knowledge graph completion methods assu…
Entity EmbeddingsGraph EmbeddingInductive knowledge graph completionInductive Learning+5History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion
Temporal knowledge graph (TKG) completion models typically rely on having access to the entire graph during training. However, in real-world scenarios, TKG data is often received incrementally as events unfold, leading t…
ClusteringKnowledge Graph CompletionTemporal Knowledge Graph CompletionCommonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clustering
The nodes in the commonsense knowledge graph (CSKG) are normally represented by free-form short text (e.g., word or phrase). Different nodes may represent the same concept. This leads to the problems of edge sparsity and…
ClusteringContrastive LearningGraph Representation LearningKnowledge Graph Completion+2Collective Knowledge Graph Completion with Mutual Knowledge Distillation
Knowledge graph completion (KGC), the task of predicting missing information based on the existing relational data inside a knowledge graph (KG), has drawn significant attention in recent years. However, the predictive p…
Knowledge DistillationKnowledge Graph CompletionKnowledge GraphsTransfer LearningText-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarge the graph reasoning space or (2) manua…
Knowledge Graph CompletionLanguage ModelingLanguage ModellingTo Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion
Embedding models have shown great power in knowledge graph completion (KGC) task. By learning structural constraints for each training triple, these methods implicitly memorize intrinsic relation rules to infer missing l…
Knowledge Graph CompletionLink PredictionMemorizationRelation