Ontologies and Data Management: A Brief Survey
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Ontologies and Data Management: A Brief Survey Thomas Schneider1 · Mantas Šimkus2 Received: 25 June 2020 / Accepted: 22 July 2020 / Published online: 13 August 2020 © The Author(s) 2020
Abstract Information systems have to deal with an increasing amount of data that is heterogeneous, unstructured, or incomplete. In order to align and complete data, systems may rely on taxonomies and background knowledge that are provided in the form of an ontology. This survey gives an overview of research work on the use of ontologies for accessing incomplete and/or heterogeneous data.
1 Introduction In the digital age, we are dealing with a huge and growing amount of data in research, medicine, business and further areas. Information systems help us process and interpret that data. This is a challenging task because data used for a single purpose is often coming from heterogeneous sources, and is often unstructured and incomplete. In order to deal with these problems, ontologies provide taxonomies and background knowledge, which is useful (not only) for completion and alignment of data. In this survey we provide an overview of research on the use of ontologies for accessing incomplete and/or heterogeneous data. Since it is impossible to give a complete and detailed account of this broad and highly active research field, this overview treats most aspects briefly and omits others completely. In particular, we will focus on a family of ontology languages based on the so-called Description Logics, and only touch on other languages. This survey begins with a discussion of ontology languages and available reasoning systems (Sect. 2). The main part of the survey (Sect. 3) treats the variety of research topics revolving around ontologies and data, divided into core topics (Sect. 3.1), extensions (Sect. 3.2), design-phase considerations (Sect. 3.3), and further topics (Sect. 3.4). We also give a brief overview of available resources (Sect. 4) * Thomas Schneider thomas.schneider@uni‑bremen.de Mantas Šimkus [email protected] 1
University of Bremen, Bremen, Germany
TU Wien, Vienna, Austria
2
and finish with a conclusion and a list of research challenges (Sect. 5).
2 Ontologies Ontologies originate from the philosophical branch of metaphysics, which studies existence. Since the 1970s, they have been used in the Knowledge Representation (KR) subfield of Artificial Intelligence (AI) for modeling the knowledge about various domains of interest, including (bio-)medicine, software engineering, cultural heritage, business processes, multimedia annotation, and the semantic web [142, 363]. Using an ontology, information systems have access to the represented knowledge and, via automated reasoning, can compute inferences. Early KR systems date back to the 1980s and early 1990s [83, 286, 288, 298, 323]. Thomas Gruber defined the notion of an ontology in the context of computer science as an “explicit specification of a conceptualization”, which is not limited to a taxonomy or a set of (conservative) definitio
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