There exist several dozens of metadata standards for empirical research data, making it difficult for users to choose and apply such standards. Consequently, the integration of datasets from similar empirical studies for further knowledge acquisition is highly constrained. To resolve this problem, an ontology for social science research data integration (Empirion-core) has been developed. The ontology reuses existing data integration schemas: DDI-RDF Discovery Vocabulary, Generic Statistical Information Model, Core Ontology for Scientific Research Activities, Data Catalog Vocabulary, and DCMI Metadata Terms. It consists of five subontologies that provide concepts for empirical datasets description: Information resource ontology, Research activity ontology, Research coverage ontology, Measurement ontology, and Sampling ontology.
Original languageEnglish
Title of host publicationProceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K2020) - Volume 2: KEOD
Pages267-274
StatePublished - 2020
Event12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management -
Duration: 2 Nov 2020 → …
Conference number: 12
http://www.keod.ic3k.org/?y=2020

Conference

Conference12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
Abbreviated titleKEOD
Period2/11/20 → …
Internet address

    Research areas

  • Ontology Reuse, Empirical Data Integration, Empirical Research Datasets, Knowledge Engineering

ID: 75121324