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Found 14 papers, 11 papers with code
What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?
Knowledge Graphs (KG) are the backbone of many data-intensive applications since they can represent data coupled with its meaning and context.
Understanding Adverse Biological Effect Predictions Using Knowledge Graphs
An effect prediction model, with and without background knowledge, was used to predict mean adverse biological effect concentration of chemicals as a prototypical type of stressors.
Contextual Semantic Embeddings for Ontology Subsumption Prediction
Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence.
Results of SemTab 2021
SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop.
Tough Tables: Carefully Evaluating Entity Linking for Tabular Data
Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables).
OWL2Vec*: 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.
Correcting Knowledge Base Assertions
The usefulness and usability of knowledge bases (KBs) is often limited by quality issues.
TERA: the Toxicological Effect and Risk Assessment Knowledge Graph
Ecological risk assessment requires large amounts of chemical effect data from laboratory experiments.
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity.
Canonicalizing Knowledge Base Literals
Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues.
Learning Semantic Annotations for Tabular Data
The usefulness of tabular data such as web tables critically depends on understanding their semantics.
Ranked #1 on
Column Type Annotation
on T2Dv2
Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]
Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide research attentions.
ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables.
Ranked #1 on
Column Type Annotation
on T2Dv2
(F1 (%) metric)
Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems.
