Papers Knowledge Graph Completion
“Knowledge Graph Completion” 태그가 달린 논문 541편 · 필터 해제
Data Poisoning Attack against Knowledge Graph Embedding
Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph.Due to its benefit to a variety of downstream tasks such as knowledge graph completion, …
Data PoisoningGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+1Quaternion Knowledge Graph Embeddings
In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge graph embeddings. More specifically, qua…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Graph Pattern Entity Ranking Model for Knowledge Graph Completion
Knowledge graphs have evolved rapidly in recent years and their usefulness has been demonstrated in many artificial intelligence tasks. However, knowledge graphs often have lots of missing facts. To solve this problem, m…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
Incorporating knowledge graph (KG) into recommender system is promising in improving the recommendation accuracy and explainability. However, existing methods largely assume that a KG is complete and simply transfer the …
Graph LearningKnowledge Graph CompletionRecommendation SystemsRelationBinarized Knowledge Graph Embeddings
Tensor factorization has become an increasingly popular approach to knowledge graph completion(KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model lik…
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsQuantization+1TuckER: Tensor Factorization for Knowledge Graph Completion
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones.…
Knowledge Graph CompletionKnowledge GraphsLink PredictionPredictionKnowledge 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+7Improved Knowledge Graph Embedding using Background Taxonomic Information
Knowledge graphs are used to represent relational information in terms of triples. To enable learning about domains, embedding models, such as tensor factorization models, can be used to make predictions of new triples. …
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsLogic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding
Knowledge graph embedding aims at modeling entities and relations with low-dimensional vectors. Most previous methods require that all entities should be seen during training, which is unpractical for real-world knowledg…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+1DSKG: A Deep Sequential Model for Knowledge Graph Completion
Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of $(subject, relation, object)$. Current KG completion models compel two-thirds of a triple pro…
Knowledge Graph CompletionRelationKnowledge Graph Completion to Predict Polypharmacy Side Effects
The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manif…
Knowledge Graph CompletionPredictionLearning Sequence Encoders for Temporal Knowledge Graph Completion
Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or…
Knowledge Graph CompletionKnowledge GraphsLink PredictionRelation+1One-Shot Relational Learning for Knowledge Graphs
Knowledge graphs (KGs) are the key components of various natural language processing applications. To further expand KGs' coverage, previous studies on knowledge graph completion usually require a large number of trainin…
Knowledge Graph CompletionKnowledge GraphsRelational ReasoningA Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization
In this paper, we introduce an embedding model, named CapsE, exploring a capsule network to model relationship triples (subject, relation, object). Our CapsE represents each triple as a 3-column matrix where each column …
Knowledge Graph CompletionLink PredictionType-Sensitive Knowledge Base Inference Without Explicit Type Supervision
State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings. We observe that when they fail, they often make enti…
Entity TypingKnowledge Base CompletionKnowledge Base PopulationKnowledge Graph Completion+4A Sequence Learning Method for Domain-Specific Entity Linking
Recent collective Entity Linking studies usually promote global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. Although graph-based approaches are shown…
Entity DisambiguationEntity LinkingKnowledge Graph CompletionLink Prediction+2Neural Tensor Networks with Diagonal Slice Matrices
Although neural tensor networks (NTNs) have been successful in many NLP tasks, they require a large number of parameters to be estimated, which often leads to overfitting and a long training time. We address these issues…
Knowledge Graph CompletionLogical ReasoningSentiment AnalysisTensor NetworksA Multimodal Translation-Based Approach for Knowledge Graph Representation Learning
Current methods for knowledge graph (KG) representation learning focus solely on the structure of the KG and do not exploit any kind of external information, such as visual and linguistic information corresponding to the…
Graph Representation LearningInformation RetrievalKnowledge Graph CompletionKnowledge Graphs+4Expeditious Generation of Knowledge Graph Embeddings
Knowledge Graph Embedding methods aim at representing entities and relations in a knowledge base as points or vectors in a continuous vector space. Several approaches using embeddings have shown promising results on task…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+3TorusE: Knowledge Graph Embedding on a Lie Group
Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. Knowledge graph e…
Entity EmbeddingsGraph EmbeddingKnowledge Graph CompletionKnowledge Graph Embedding+2