Ontology Engineering

Semantic Web 0 (0) 1 1 IOS Press 1 1 2 2 3 3 4 Ontology Engineering: Current State, 4 5 5 6 Challenges, and Future Directions 6 7 7 8 Tania Tudorache 8 9 Stanford Center for Biomedical Informatics Research, Stanford University, CA, USA 9 10 E-mail: [email protected] 10 11 11 12 12 Editors: Pascal Hitzler, Kansas State University, Manhattan, KS, USA; Krzysztof Janowicz, University of California, Santa Barbara, USA 13 Solicited reviews: Aldo Gangemi, LIPN Université Paris13-CNRS-Sorbonne Cité, France and ISTC-CNR Roma, Italy; Oscar Corcho, 13 14 Universidad Politécnica de Madrid, Spain 14 15 15 16 16 17 17 18 18 19 Abstract. 19 20 In the last decade, ontologies have become widely adopted in a variety of fields ranging from biomedicine, to finance, engi- 20 21 neering, law, and cultural heritage. The ontology engineering field has been strengthened by the adoption of several standards 21 22 pertaining to ontologies, by the development or extension of ontology building tools, and by a wider recognition of the impor- 22 23 tance of standardized vocabularies and formalized semantics. Research into ontology engineering has also produced methods 23 24 and tools that are used more and more in production settings. Despite all these advancements, ontology engineering is still a 24 difficult process, and many challenges still remain to be solved. This paper gives an overview of how the ontology engineering 25 25 field has evolved in the last decade and discusses some of the unsolved issues and opportunities for future research. 26 26 27 Keywords: Ontologies, Ontology Engineering, Methods, Standards, Tooling, Patterns, Challenges, Future Research 27 28 28 29 29 30 30 31 1. Ontologies Make an Impact much more widely adopted in academia, industry and 31 32 government environments, and are finally making an 32 33 The research on ontologies in computer science impact in many domains. 33 34 started in the early 1990s. Ontologies were proposed Biomedicine has widely adopted ontologies since 34 35 as a way to enable people and software agents to seam- their beginnings. The Gene Ontology (GO) [3]—a 35 36 lessly share information about a domain of interest. comprehensive ontology describing the function of 36 37 An ontology was defined as a conceptual representa- genes—is the poster child for a successful ontology 37 38 tion of the entities, their properties and relationships development project that has produced a big impact in 38 39 in a domain [1]. The ultimate goal of using ontolo- biomedical research. Indeed, GO is routinely used in 39 40 gies was to make the knowledge in a domain compu- the computational analysis of large-scale molecular bi- 40 41 tationally useful [2]. The initial research period was ology and genetics experiments [4]. Researchers have 41 42 followed by a time of great excitement about using also used ontologies in biomedicine to standardize 42 43 ontologies to solve a wide range of problems. How- terminology in particular domains, to annotate large 43 44 ever, the enthusiasm dwindled in the early 2000s, as biomedical datasets, to integrate data, and to aid struc- 44 45 the methods and infrastructures for building and us- tured data mining and machine learning [5, 6]. 45 46 ing ontologies were not mature enough at that time. One notable example of the impact ontologies are 46 47 Nonetheless, significant changes have taken place in making in healthcare is the development of the 11th 47 48 the last decade: The research and development on on- revision of the International Classification of Diseases 48 49 tologies had a big boost, more standardization efforts (ICD-11). ICD—developed by the World Health Or- 49 50 were on the way, and industry started to buy into se- ganization (WHO)—is the international standard for 50 51 mantic technologies. As a result, ontologies are now reporting diseases and health conditions, and is used 51 1570-0844/0-1900/$35.00 © 0 – IOS Press and the authors. All rights reserved 2 T. Tudorache / Ontology Engineering 1 to identify health trends and statistics on a global allowing the exchange of cultural heritage data be- 1 2 scale [7]. ICD-11 is now using OWL to encode the for- tween institutions. 2 3 mal representation of diseases, their properties, and re- Other fields have also adopted ontologies more 3 4 lations, as well as mappings to other terminologies [8]. widely. Researchers have used ontologies in the legal 4 5 The financial industry has embraced the use of on- domain to formally represent laws and regulations, to 5 6 tologies. The most prominent example is the Finan- simulate legal actions, or for semantic searching and 6 7 cial Industry Business Ontology (FIBO)—the industry indexing [21]. In the geographical domain, the ISO 7 8 standard resource for the definitions of business con- 19150-1:2012 [22] defines a high-level model of the 8 9 cepts in the financial services industry [9]. FIBO is components required to handle semantics in the ISO 9 10 developed by the Enterprise Data Management Coun- geographic information standards with ontologies. The 10 11 cil (EDMC) and it is standardized through the Object second part of the standard, ISO 19150-2:2015 [23] 11 12 Management Group (OMG). FIBO is built as a series defines rules to convert the UML models used in the 12 13 of OWL ontologies and is developed using a rigorous ISO geographic information standards into OWL. 13 14 and well-defined process, known as the “Build-Test- The examples we mentioned above are not meant 14 15 Deploy-Maintain” methodology. to be comprehensive. They show how ontologies have 15 16 Engineering is another field that has adopted on- been embraced by a wide range of fields in the last 16 17 tologies from the early 1990s, long before the stan- decade and how they are making an impact. 17 18 dardization of the current Semantic Web languages, This paper is meant to give a retrospective overview 18 19 such as OWL and RDF [10, 11]. In the last decade, of how the ontology landscape and ontology engineer- 19 20 we have witnessed significant efforts around using on- ing have evolved in the last decade, current challenges, 20 21 tologies to cover different aspects of engineering rang- and prospects for future research. This paper can hope- 21 22 ing from defining requirements [12], to integrating dif- fully also serve as an introduction for newcomers in 22 23 ferent engineering models [13], to detecting incon- the field. We briefly discuss standards relevant to on- 23 24 sistencies in models in multidisciplinary engineering tology engineering that have been adopted in the last 24 25 projects [14]. Sabou et. al [15] provide a comprehen- decade (Section 2), highly visible and influential on- 25 26 sive overview into how ontologies and Semantic Web tologies and knowledge bases that are constructed by 26 27 27 technologies can assist in building intelligent engineer- large communities (Section 3), trends in ontology en- 28 gineering from the last ten years (Section 4), and cur- 28 ing applications. 29 rent challenges (Section 5) and opportunities for future 29 A lot of work has gone into developing methods 30 research (Section 6). 30 and tools for publishing linked datasets of the vast cul- 31 31 tural heritage field in the last decade [16]. One out- 32 32 standing example is the linked open dataset of the Eu- 33 2. New Standards 33 ropeana Collections1 [17] which provides access to 34 34 millions of artworks, artefacts, books, films and mu- 35 The significant standardization efforts on ontolo- 35 sic from European museums, galleries, libraries and 36 gies and Semantic Web languages in the last decade 36 archives. Another example comes from the scholarly 37 also prove the maturation of the field. Figure 1 shows 37 publishing domain, in which the SPAR (Semantic Pub- 38 some of ontology-related standards that the World 38 lishing and Referencing) Ontologies [18] are having 39 Wide Web Consortium (W3C) has adopted in the past 39 a high impact since they were released in 2015. The 40 decade. Several ontologies and vocabularies have be- 40 recently released Italian Cultural Heritage knowledge 41 come W3C recommendations: The Time Ontology 41 graph, ArCO2 [19], consists of a network of seven 42 (OWL-Time) [24]—describing the temporal properties 42 43 high-quality ontologies, modeling the cultural her- of resources; the Semantic Sensors Network Ontol- 43 44 itage domain, and contains over 169 million triples ogy (SSN) [25]—representing sensors and their obser- 44 45 about 820 thousand cultural entities. The testament vations; the Provenance Ontology (PROV-O) [26]— 45 46 to the importance of ontologies in the cultural her- describing provenance information from different sys- 46 47 itage field is shown also by the adoption of the ISO tems; or the RDF Data Cube [27]—enabling publish- 47 48 21127:2014 [20] standard that prescribes an ontology ing of multi-dimensional data on the Web. 48 49 Ontology and knowledge representation languages 49 50 1https://data.europeana.eu have also evolved as proved by the adoption of new 50 51 2http://dati.beniculturali.it/arco/index.php versions of the standards: RDF 1.1 was adopted in 51 T. Tudorache / Ontology Engineering 3 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 11 11 12 12 13 13 14 14 Fig. 1. The timeline of W3C recommendations related to ontologies and vocabularies in the last decade (2010-2019). The years that do not have 15 15 any recommendations are skipped.

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