Representing Temporal Knowledge in the Semantic Web: The Extended 4D Fluents Approach

Abstract

Abstract-Representing information that evolves in time in ontologies, as well as reasoning over static and dynamic ontologies are the areas of interest in this work. Building upon well established standards of the semantic Web and the 4D-fluents approach for representing the evolution of temporal information in ontologies, this work demonstrates how qualitative temporal relations that are common in natural language expressions (i.e., relations between time intervals like "before", "after", etc.) are represented in ontologies. Existing approaches allow for representations of temporal information, but do not support representation of qualitative relations and reasoning. I. INTRODUCTIONOntologies offer the means for representing high level concepts, their properties and their interrelationships. Dynamic ontologies will in addition enable representation of information evolving in time. In particular, dynamic ontologies are not only suitable for describing static scenes with static objects (e.g., objects in photographs) but also enable representation of events with objects and properties changing in time (e.g., moving objects in a video). Representation of both static and dynamic information in ontologies, as well as reasoning over static and dynamic ontologies are exactly the problems this work is dealing with.Representation of dynamic features calls for mechanisms allowing representation of the notion of time (and of properties varying in time) [ [9] with the last being the most efficient. All approaches suffer from data redundancy as several objects are created for each binary relationship changing in time (i.e., for each new event, a new temporal object and an additional binary relationship for each temporal property of this object is created and associated with existing classes) thus complicating the ontology. Also, adding a time argument to binary relationships may (as in reification and named graphs) complicate application of OWL language constructs (e.g., cardinality constraints, inverse, transitive relations are no longer applicable) thus limiting OWL expressivity and obstructing reasoning. The 4D fluents approach, still suffers from data redundancy but maintains OWL expressiveness and reasoning support (i.e., an OWL reasoner such as Pellet can still be applied to fully exploit OWL

Comparison of Time Concept Modeling for Querying Temporal Information in OWL and RDF

“…4D-Fluents [48], [7]: 4D-fluent represents temporal concepts in OWL. Time is a fourth dimension used to represent temporal concepts.…”

An Integrated Software Framework to Support Semantic Modeling and Reasoning of Spatiotemporal Change of Geographical Objects: A Use Case of Land Use and Land Cover Change Study

“…In comparison with the widely-studied semantic interoperability problem in the LULC classification systems, fewer studies [31][32][33][34][35][36][37] on the semantic modeling of change information regarding its spatial, temporal and other attributes, and especially on integrating these semantic models into an operational spatial reasoning framework, are found. This is partially due to the complexity in modeling spatial objects, which contain four distinct dimensions: (1) geographical (i.e., location); (2) geometric dimension (i.e., shape or size); (3) topological (i.e., intersections with other spatial objects); and (4) temporal, (i.e., creation and cessation time).…”

“…of its water flow. Batsakis and Petrakis [37] developed a 4D-fluent model to express the existence of an entity that relates to some defined time interval. This is the most well-known model that can capture the dynamics of an entity in an ontology.…”

Event order generation using Reference Event based qualitative Temporal (REseT) relations in Time Event Ontology

“…It also enables modelling of domain dynamics using 4D fluent approach [2,3]. The 4D fluent approach represents time concepts as 4-dimensional objects with the 4th dimension being time to allow representation of complex temporal aspects such as process state transitions [9]. The interval based temporal ontology language TL-OWL was proposed to represent and reason temporal knowledge in Semantic Web [14].…”

“…Quantitative temporal relations handle intervals by considering the start and end points. But qualitative temporal relations are advantageous over quantitative relations as they a) can be inferred from quantitative information b) can be used as an alternative to missing quantitative information [8] c) increase the expressive power of the representation [9] d) enable reasoning over relations [7]. Allen's 13 pairwise disjoint relations between time intervals are qualitative.…”