Temporal representation and reasoning in OWL 2

Abstract

The representation of temporal information has been in the center of intensive research activities over the years in the areas of knowledge representation, databases and more recently, the Semantic Web. The proposed approach extends the existing framework of representing temporal information in ontologies by allowing for representation of concepts evolving in time (referred to as "dynamic" information) and of their properties in terms of qualitative descriptions in addition to quantitative ones (i.e., dates, time instants and intervals). For this purpose, we advocate the use of natural language expressions, such as "before" or "after", for temporal entities whose exact durations or starting and ending points in time are unknown. Reasoning over all types of temporal information (such as the above) is also an important research problem. The current work addresses all these issues as follows: The representation of dynamic concepts is achieved using the "4D-fluents" or, alternatively, the "N-ary relations" mechanism. Both mechanisms are thoroughly explored and are expanded for representing qualitative and quantitative temporal information in OWL. In turn, temporal information is expressed using either intervals or time instants. Qualitative temporal information representation in particular, is realized using sets of SWRL rules and OWL axioms leading to a sound, complete and tractable reasoning procedure based on path consistency applied on the existing relation sets. Building upon existing Semantic Web standards (OWL), tools and member submissions (SWRL), as well as integrating temporal reasoning support into the proposed representation, are important design features of our approach.

Temporal Extensions to RDF

“…Batsakis et al extended 4D Fluents model to incorporate qualitative temporal relations that have unknown temporal information [7,8]. Such a relation is considered an object property between time intervals.…”

“…Instantiating-Identifying Concept/Relationship (IIR) 4D Fluents [7,8,58] tOWL [40] Singleton Property [41] Relationship Entity Conversion (REC) VT-OWL [44] N-ary [42] FrameBase [48] Named Graphs [11] τ SPARQL [53] RDF+ [50]…”

“…There are two temporal models in Instantiating-Identifying Concept/Relationship models. 4D Fluents [7,8,58] introduces temporal part for an entity changing over time. Each temporal part corresponds to a distinguishable timestamp.Afluent property associates two temporal parts.…”

“…The model provides a coverage of formal semantics of temporal graphs, query language prototype and complexity analysis. Similarly, 4D Fluents [7,8,58], N-ary relations [42] and Named Graphs [11] can all be implemented by RDF, RDFS and OWL vocabularies. These models benefit from available ontology tools, such as reasoners.…”

Evidence-Based Languages for Conceptual Data Modelling Profiles

“…Any statements can be about not only the present, but also the past and future (e.g. [80,66]). A typical example in favour of 4D is to represent as accurately as possible the notion of a holding or supra-organisation [66], such as Alphabet and Nestlé: these companies exist for some time and keep their identity all the while they acquire and sell other (subsidiary) companies.…”