Paper Info
Title
Collaborative Ontology Evolution and Data Quality - An Empirical Analysis.
Abstract
Since more than a decade, theoretical research on ontology evolution has been published in literature and several frameworks for managing ontology changes have been proposed. However, there are less studies that analyze widely used ontologies that were developed in a collaborative manner to understand community-driven ontology evolution in practice. In this paper, we perform an empirical analysis on how four well-known ontologies (DBpedia, Schema. org, PROV-O, and FOAF) have evolved through their lifetime and an analysis of the data quality issues caused by some of the ontology changes. To that end, the paper discusses the composition of the communities that developed the aforementioned ontologies and the ontology development process followed. Further, the paper analyses the changes in those ontologies in the 53 versions of them examined in this study. Depending of the use case, the community involved, and other factors different approaches for the ontology development and evolution process are used (e.g., bottom-up approach with high automation or top-down approach with a lot of manual curation). This paper concludes that one model for managing changes does not fit all. Furthermore, it is also clear that none of the selected ontologies follow the theoretical frameworks found in literature. Nevertheless, in communities where industrial participants are dominant more rigorous editorial processes are followed, largely influenced by software development tools and processes. Based on the analysis, the most common quality problems caused by ontology changes include the use of abandoned classes and properties in data and introduction of duplicate classes and properties.
Year
DOI
Venue
2016
10.1007/978-3-319-54627-8_8
Lecture Notes in Computer Science
Keywords
Field
DocType
Ontology,Change,Evolution,Quality,DBpedia,Schema.org,PROV-O,FOAF
Data science,Ontology (information science),Ontology,Ontology-based data integration,Data mining,FOAF,Data quality,Process ontology,Computer science,Knowledge management,Schema.org,Software development
Conference
Volume
ISSN
Citations 
10161
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Nandana Mihindukulasooriya17517.75
María Poveda-Villalón216814.38
Raul Garcia-Castro352756.11
Asunción Gómez-Pérez42038201.05