Papers Ontology Embedding
“Ontology Embedding” 태그가 달린 논문 18편 · 필터 해제
DELE: Deductive $\mathcal{EL}^{++} \thinspace $ Embeddings for Knowledge Base Completion
Ontology embeddings map classes, relations, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred. For ontologies in the Descripti…
Knowledge Base CompletionOntology EmbeddingTransBox: EL++-closed Ontology Embedding
OWL (Web Ontology Language) ontologies, which are able to represent both relational and type facts as standard knowledge graphs and complex domain knowledge in Description Logic (DL) axioms, are widely adopted in domains…
Knowledge Graph EmbeddingsKnowledge GraphsOntology EmbeddingOWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec4OA, an extension of the ontology embedd…
Knowledge Graph EmbeddingsOntology EmbeddingOntology Embedding: A Survey of Methods, Applications and Resources
Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains. However, logical reasoni…
Logical ReasoningOntology EmbeddingSurveyEnhancing Geometric Ontology Embeddings for $\mathcal{EL}^{++}$ with Negative Sampling and Deductive Closure Filtering
Ontology embeddings map classes, relations, and individuals in ontologies into $\mathbb{R}^n$, and within $\mathbb{R}^n$ similarity between entities can be computed or new axioms inferred. For ontologies in the Descripti…
Ontology EmbeddingStructural Positional Encoding for knowledge integration in transformer-based medical process monitoring
Predictive process monitoring is a process mining task aimed at forecasting information about a running process trace, such as the most correct next activity to be executed. In medical domains, predictive process monitor…
Knowledge Graph EmbeddingManagementOntology EmbeddingPredictive Process Monitoring+1A Self-matching Training Method with Annotation Embedding Models for Ontology Subsumption Prediction
Recently, ontology embeddings representing entities in a low-dimensional space have been proposed for ontology completion. However, the ontology embeddings for concept subsumption prediction do not address the difficulti…
Ontology EmbeddingEmbedding Ontologies via Incorporating Extensional and Intensional Knowledge
Ontologies contain rich knowledge within domain, which can be divided into two categories, namely extensional knowledge and intensional knowledge. Extensional knowledge provides information about the concrete instances t…
Language ModelingLanguage ModellingLink PredictionOntology Embedding+1Lattice-preserving $\mathcal{ALC}$ ontology embeddings with saturation
Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying sema…
DescriptiveKnowledge Base CompletionOntology EmbeddingFrom axioms over graphs to vectors, and back again: evaluating the properties of graph-based ontology embeddings
Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning. One approach of generating ontologies embeddings is by first embedding the on…
Graph EmbeddingOntology EmbeddingDual Box Embeddings for the Description Logic EL++
OWL ontologies, whose formal semantics are rooted in Description Logic (DL), have been widely used for knowledge representation. Similar to Knowledge Graphs (KGs), ontologies are often incomplete, and maintaining and con…
Knowledge GraphsLink PredictionOntology EmbeddingRepresentation LearningDisentangled Ontology Embedding for Zero-shot Learning
Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). However, existing ZSL methods that utilize…
image-classificationImage ClassificationOntology EmbeddingZero-Shot Image Classification+1OntoProtein: Protein Pretraining With Gene Ontology Embedding
Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of dive…
Contrastive LearningKnowledge GraphsOntology EmbeddingProtein Function PredictionMIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Healthcare representation learning on the Electronic Health Records is crucial for downstream medical prediction tasks in health informatics. Many NLP techniques, such as RNN and self-attention, have been adapted to lear…
Graph EmbeddingOntology EmbeddingRepresentation LearningOntoED: Low-resource Event Detection with Ontology Embedding
Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event…
Event DetectionOntology EmbeddingOWL2Vec*: Embedding of OWL Ontologies
Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attenti…
Knowledge GraphsLanguage ModellingOntology EmbeddingPredictionOntology-guided Semantic Composition for Zero-Shot Learning
Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In thi…
image-classificationImage ClassificationOntology EmbeddingQuestion Answering+4From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules
Recent years have witnessed the successful application of low-dimensional vector space representations of knowledge graphs to predict missing facts or find erroneous ones. However, it is not yet well-understood to what e…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsOntology Embedding