Search Results for author:
Found 7 papers, 3 papers with code
Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a single sentence is input.
ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
ReOnto employs a graph neural network to acquire the sentence representation and leverages publicly accessible ontologies as prior knowledge to identify the sentential relation between two entities.
Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges
Ontologies are used in various domains, with RDF and OWL being prominent standards for ontology development.
A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency
Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse.
OntoSeer -- A Recommendation System to Improve the Quality of Ontologies
From among the thousands of publicly available ontologies and vocabularies in repositories such as Linked Open Vocabularies (LOV) and BioPortal, it is hard to know the terms (classes and properties) that can be reused in the development of an ontology.
SERC: Syntactic and Semantic Sequence based Event Relation Classification
We propose an LSTM based model for temporal and causal relation classification that captures the interrelations between the three encoded features.
Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships
However, most of the existing KG embeddings only consider the network structure of the graph and ignore the semantics and the characteristics of the underlying ontology that provides crucial information about relationships between entities in the KG.
