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. In the paper we present an approach that combines a lexical index, a neural embedding model and locality modules to effectively divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed methods are adequate in practice and can be integrated within the workflow of state-of-the-art systems.
Code (1)
Tasks
Ontology MatchingSimilar Papers 제목 키워드 기반
Using a Lexical Semantic Network for the Ontology Building
Building multilingual ontologies is a hard task as ontologies are often data-rich resources. We introduce an approach which allows exploiting structured lexical semantic knowledge for the ontology building. Given a multi…
Word Tagging with Foundational Ontology Classes: Extending the WordNet-DOLCE Mapping to Verbs
Semantic annotation is fundamental to deal with large-scale lexical information, mapping the information to an enumerable set of categories over which rules and algorithms can be applied, and foundational ontology classe…
Matching with Transformers in MELT
One of the strongest signals for automated matching of ontologies and knowledge graphs are the textual descriptions of the concepts. The methods that are typically applied (such as character- or token-based comparisons) …
Graph MatchingKnowledge GraphsOntology MatchingTowards the Linking of a Sign Language Ontology with Lexical Data
We describe our current work for linking a new ontology for representing constitutive elements of Sign Languages with lexical data encoded within the OntoLex-Lemon framework. We first present very briefly the current sta…
A Lime-Flavored REST API for Alignment Services
A practical alignment service should be flexible enough to handle the varied alignment scenarios that arise in the real world, while minimizing the need for manual configuration. MAPLE, an orchestration framework for ont…