Papers Knowledge Graph Embeddings
“Knowledge Graph Embeddings” 태그가 달린 논문 269편 · 필터 해제
Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database
Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis. In this work, we present a new method for constructing a vulnerability knowledge graph from infor…
Knowledge Graph EmbeddingsKnowledge Graphsnamed-entity-recognitionNamed Entity Recognition+3Knowledge Graphs: Opportunities and Challenges
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate…
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsEnhancing Embedding Representations of Biomedical Data using Logic Knowledge
Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode statistical dependencies between entities …
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsRelational ReasoningCardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks
Cardinality Estimation over Knowledge Graphs (KG) is crucial for query optimization, yet remains a challenging task due to the semi-structured nature and complex correlations of typical Knowledge Graphs. In this work, we…
Knowledge Graph EmbeddingsKnowledge GraphsEffects of Locality and Rule Language on Explanations for Knowledge Graph Embeddings
Knowledge graphs (KGs) are key tools in many AI-related tasks such as reasoning or question answering. This has, in turn, propelled research in link prediction in KGs, the task of predicting missing relationships from th…
Knowledge Graph EmbeddingsKnowledge GraphsLink PredictionQuestion AnsweringLink Prediction with Attention Applied on Multiple Knowledge Graph Embedding Models
Predicting missing links between entities in a knowledge graph is a fundamental task to deal with the incompleteness of data on the Web. Knowledge graph embeddings map nodes into a vector space to predict new links, scor…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsLink PredictionEditing Language Model-based Knowledge Graph Embeddings
Recently decades have witnessed the empirical success of framing Knowledge Graph (KG) embeddings via language models. However, language model-based KG embeddings are usually deployed as static artifacts, making them diff…
EDIT Taskknowledge editingKnowledge Graph EmbeddingsLanguage Modeling+2Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
Knowledge graphs (KGs) play a crucial role in many applications, such as question answering, but incompleteness is an urgent issue for their broad application. Much research in knowledge graph completion (KGC) has been p…
Knowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge GraphsQuestion AnsweringPredicting missing annotations in Gene Ontology with Knowledge Graph Embeddings and True Path Rule
Gene Ontology (GO) and its Annotations (GOA) provide a controlled and evolving vocabulary for gene products and gene functions widely used in molecular biology. GO \& GOA are updated and maintained both automatically fro…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsCustomizing Knowledge Graph Embedding to Improve Clinical Study Recommendation
Inferring knowledge from clinical trials using knowledge graph embedding is an emerging area. However, customizing graph embeddings for different use cases remains a significant challenge. We propose custom2vec, an algor…
Graph EmbeddingInformation RetrievalKnowledge Graph EmbeddingKnowledge Graph Embeddings+2Biomedical Multi-hop Question Answering Using Knowledge Graph Embeddings and Language Models
Biomedical knowledge graphs (KG) are heterogenous networks consisting of biological entities as nodes and relations between them as edges. These entities and relations are extracted from millions of research papers and u…
Knowledge Graph EmbeddingsKnowledge GraphsMulti-hop Question AnsweringQuestion AnsweringCombining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language
Word embeddings play a significant role in today's Natural Language Processing tasks and applications. While pre-trained models may be directly employed and integrated into existing pipelines, they are often fine-tuned t…
Contrastive LearningGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform
The choice of geometric space for knowledge graph (KG) embeddings can have significant effects on the performance of KG completion tasks. The hyperbolic geometry has been shown to capture the hierarchical patterns due to…
Knowledge Graph EmbeddingsKGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles
We propose KGTN-ens, a framework extending the recent Knowledge Graph Transfer Network (KGTN) in order to incorporate multiple knowledge graph embeddings at a small cost. We evaluate it with different combinations of emb…
Few-Shot Image Classificationimage-classificationImage ClassificationKnowledge Graph EmbeddingsEnhancing Patent Retrieval using Text and Knowledge Graph Embeddings: A Technical Note
Patent retrieval influences several applications within engineering design research, education, and practice as well as applications that concern innovation, intellectual property, and knowledge management etc. In this a…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+3GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs
In graph learning, there have been two predominant inductive biases regarding graph-inspired architectures: On the one hand, higher-order interactions and message passing work well on homophilous graphs and are leveraged…
Graph LearningKnowledge Graph EmbeddingsLambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and text-rich entity/relation information. Text-based KG embeddings can represent entities by encoding descriptions with pre-trained la…
Graph Representation LearningKnowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge Graphs+5Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations
Knowledge graph embedding aims to represent entities and relations as low-dimensional vectors, which is an effective way for predicting missing links. It is crucial for knowledge graph embedding models to model and infer…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graph Embeddings+1Adversarial Robustness of Representation Learning for Knowledge Graphs
Knowledge graphs represent factual knowledge about the world as relationships between concepts and are critical for intelligent decision making in enterprise applications. New knowledge is inferred from the existing fact…
Adversarial RobustnessData PoisoningDecision MakingKnowledge Graph Embeddings+2A Survey on Knowledge Graph-based Methods for Automated Driving
Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD)in particular. However,…
Knowledge Graph EmbeddingsKnowledge GraphsMotion PlanningScene Understanding+1