Papers Knowledge Graph Embeddings
“Knowledge Graph Embeddings” 태그가 달린 논문 269편 · 필터 해제
BloomGML: Graph Machine Learning through the Lens of Bilevel Optimization
Bilevel optimization refers to scenarios whereby the optimal solution of a lower-level energy function serves as input features to an upper-level objective of interest. These optimal features typically depend on tunable …
Bilevel OptimizationGraph LearningGraph Neural NetworkKnowledge Graph EmbeddingsFederated Neural Graph Databases
The increasing demand for large-scale language models (LLMs) has highlighted the importance of efficient data retrieval mechanisms. Neural graph databases (NGDBs) have emerged as a promising approach to storing and query…
Complex Query AnsweringFederated LearningKnowledge Graph EmbeddingsKnowledge Graphs+2Improving Molecule Generation and Drug Discovery with a Knowledge-enhanced Generative Model
Recent advancements in generative models have established state-of-the-art benchmarks in the generation of molecules and novel drug candidates. Despite these successes, a significant gap persists between generative model…
Drug DiscoveryKnowledge Graph EmbeddingsKnowledge GraphsEmpowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts
Social networks are vital for information sharing, especially in the health sector for discussing diseases and treatments. These platforms, however, often feature posts as brief texts, posing challenges for Artificial In…
Knowledge Graph EmbeddingsKnowledge GraphsWord EmbeddingsEmbedding Knowledge Graphs in Degenerate Clifford Algebras
Clifford algebras are a natural generalization of the real numbers, the complex numbers, and the quaternions. So far, solely Clifford algebras of the form $Cl_{p,q}$ (i.e., algebras without nilpotent base vectors) have b…
Entity EmbeddingsKnowledge Graph EmbeddingsKnowledge GraphsCapturing Knowledge Graphs and Rules with Octagon Embeddings
Region based knowledge graph embeddings represent relations as geometric regions. This has the advantage that the rules which are captured by the model are made explicit, making it straightforward to incorporate prior kn…
Knowledge Graph EmbeddingsKnowledge GraphsPre-training and Diagnosing Knowledge Base Completion Models
In this work, we introduce and analyze an approach to knowledge transfer from one collection of facts to another without the need for entity or relation matching. The method works for both canonicalized knowledge bases a…
General KnowledgeKnowledge Base CompletionKnowledge Graph EmbeddingsTransfer Learning+1Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings
In Natural Language Processing (NLP), Machine Reading Comprehension (MRC) is the task of answering a question based on a given context. To handle questions in the medical domain, modern language models such as BioBERT, S…
Knowledge Graph EmbeddingsKnowledge GraphsMachine Reading ComprehensionMedical Question Answering+3Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts. While rotation-based methods like RotatE and QuatE perform well in K…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsRelation+1BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving
The rapid development of the autonomous driving industry has led to a significant accumulation of autonomous driving data. Consequently, there comes a growing demand for retrieving data to provide specialized optimizatio…
Autonomous DrivingCaption GenerationDescriptiveImage Retrieval+6RDF-star2Vec: RDF-star Graph Embeddings for Data Mining
Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, object>). Knowledge graph embedding (KGE) i…
BenchmarkingGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference
Prediction of vehicle lane change maneuvers has gained a lot of momentum in the last few years. Some recent works focus on predicting a vehicle's intention by predicting its trajectory first. This is not enough, as it ig…
Bayesian InferenceKnowledge Graph EmbeddingsKnowledge GraphsLinked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge graph that provides comprehensive, current information about almost 400,000 machine learning publications. This includes the tasks addressed, t…
Knowledge Graph EmbeddingsEXTRACT: Explainable Transparent Control of Bias in Embeddings
Knowledge Graphs are a widely used method to represent relations between entities in various AI applications, and Graph Embedding has rapidly become a standard technique to represent Knowledge Graphs in such a way as to …
Graph EmbeddingKnowledge Graph EmbeddingsKnowledge GraphsFaithful Path Language Modeling for Explainable Recommendation over Knowledge Graph
The integration of path reasoning with language modeling in recommender systems has shown promise for enhancing explainability but often struggles with the authenticity of the explanations provided. Traditional models mo…
Explainable RecommendationKnowledge Graph EmbeddingsKnowledge GraphsLanguage Modeling+3Universal Knowledge Graph Embeddings
A variety of knowledge graph embedding approaches have been developed. Most of them obtain embeddings by learning the structure of the knowledge graph within a link prediction setting. As a result, the embeddings reflect…
Entity DisambiguationGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+3A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things
Graph data structures are widely used to store relational information between several entities. With data being generated worldwide on a large scale, we see a significant growth in the generation of knowledge graphs. Thi…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+3Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings
Knowledge representation (KR) is vital in designing symbolic notations to represent real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) have emerged so far as a popular form of KR, off…
Decision MakingKnowledge Graph EmbeddingsKnowledge GraphsLink PredictionKGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation
Despite being the go-to choice for link prediction on knowledge graphs, research on interpretability of knowledge graph embeddings (KGE) has been relatively unexplored. We present KGEx, a novel post-hoc method that expla…
Knowledge DistillationKnowledge Graph EmbeddingsKnowledge GraphsLink PredictionClinical Trial Recommendations Using Semantics-Based Inductive Inference and Knowledge Graph Embeddings
Designing a new clinical trial entails many decisions, such as defining a cohort and setting the study objectives to name a few, and therefore can benefit from recommendations based on exhaustive mining of past clinical …
Knowledge Graph Embeddingstext similarity