$$\uptau $$ τ OWL: A Systematic Approach to Temporal Versioning of Semantic Web Ontologies

Abstract

The W3C OWL 2 recommendation is an ontology language for the Semantic Web. It allows defining both schema (i.e., entities, axioms, and expressions) and instances (i.e., individuals) of ontologies. However, OWL 2 lacks explicit support for time-varying schema or for time-varying instances. Hence, knowledge engineers or maintainers of semantics-based Web resources have to use ad hoc techniques to specify OWL 2 time-varying ontologies. In this paper, for a disciplined and systematic approach to the temporal management of Semantic Web ontologies, we propose the adoption of a framework called temporal OWL 2 (τOWL), which is inspired by the τXSchema framework defined for XML data. In a way similar to what happens in τXSchema, τOWL allows creating a temporal OWL 2 ontology from a conventional (i.e., non-temporal) OWL 2 ontology and a set of logical and physical annotations. Logical annotations identify which elements of the ontology can vary over time; physical annotations specify how the time-varying aspects are represented in the OWL 2 document. Using annotations to integrate temporal aspects in the traditional Semantic Web, our framework (1) guarantees logical and physical data independence for temporal schemas and (2) provides a low-impact solution, since it requires neither modifications of existing Semantic B Zouhaier Brahmia Web ontologies, nor extensions to the OWL 2 recommendation and Semantic Web standards. Moreover, since the conventional schema and annotation documents could evolve over time to respond to new applications' requirements, τOWL supports temporal schema versioning by allowing changing these components and by keeping track of their evolution through the conventional schema versions and annotation document versions, respectively. Two complete sets of operations are proposed for changing the conventional schema and annotation documents; to complete the figure, a set of operations is also introduced for updating temporal schema which must be changed consequently each time one of the mentioned components evolves over time. To show the feasibility of our approach, a prototype tool, named τOWL-Manager, is presented.

$\tau\text{JOWL}$: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

“…Notice that extant JSON data are stored and managed in JOWL as in our previous temporal JSON JSchema framework [34,35] . Moreover, the OWL 2 ontologies are stored and managed in JOWL as in our previous temporal ontology OWL framework [36,37] . Notice that (temporal) reasoning [38][39][40][41][42] , which can be part of some AI-based data analytics or data valorization task, can be easily done on temporal OWL 2 ontologies managed in our framework, through the use of suitable tools.…”

Efficient management and storage of a multiversion OWL 2 DL domain ontology

“…Zekri et al () designed a τOWL (Temporal OWL 2) framework for versioning an OWL 2 ontology at the schema and data levels. It is based on different documents.…”

“…FIGURE 1 A naïve storage strategy to address the problem of ontology versioning. AST: application start time; AET: application end time FIGURE 2 A change-based storage strategy to store ontology versions Timestamp-based (TB) approach (Bedi & Marwaha, 2007;Chen & Matthews, 2008;Eder & Koncilia, 2004;Grandi, 2011;Grandi, 2016;Grandi & Scalas, 2009;Kirsten, Hartung, Groß, & Rahm, 2009;Klarman, Hoekstra, & Bron, 2008;Liu, 2012;Plessers, De Troyer, & Casteleyn, 2007;Zekri et al, 2016) This technique is adopted from temporal databases (Jensen & Dyreson, 1998) It is very efficient from space point of view as the entities and axioms are stored only once. To keep track of their evolution, they are marked by a valid/transaction time interval.…”

“… Timestamp‐based (TB) approach (Bedi & Marwaha, ; Chen & Matthews, ; Eder & Koncilia, ; Grandi, ; Grandi, ; Grandi & Scalas, ; Kirsten, Hartung, Groß, & Rahm, ; Klarman, Hoekstra, & Bron, ; Liu, ; Plessers, De Troyer, & Casteleyn, ; Zekri et al, ) This technique is adopted from temporal databases (Jensen & Dyreson, ) It is very efficient from space point of view as the entities and axioms are stored only once. To keep track of their evolution, they are marked by a valid/transaction time interval.…”

“…Most works have mainly focused on managing consistency in a posteriori way (Copeland et al, 2013). Others have mainly focused on some ontology versioning aspects (Grandi, 2016;Kondylakis & Plexousakis, 2013;Zekri, Brahmia, Grandi, & Bouaziz, 2016) other than the storage and the consistency preservation issues. Specifically, the state of the art storage strategies of ontology versions perform well some typical queries at the cost of a high storage space.…”