1. Sotiris Batsakis, Kostas Stravoskoufos Euripides G.M. Petrakis Technical University Of Crete Intelligent Systems Laboratory

2.  Representing evolution of information in time using OWL  Temporal relations involve at least three arguments ◦ OWL represents binary relations  Existing approaches: Temporal RDF, Named Graphs, Reification, Versioning, N-ary, 4D-fluents ◦ No qualitative information ◦ Limited OWL reasoning support  Q uerying of temporal information is also a problem  We propose a solution to all these problems based on 4D-fluents and N-ary relations. Techical University Of Crete Intelligent Systems Laboratory2

3.  Classes TimeSlice, TimeInterval are introduced  Dynamic objects become instances of TimeSlice  Temporal properties of dynamic classes become instances of TimeInterval  A time slice object is created each time a (fluent) property changes 3

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5.  Dynamic Properties are attached to reified objects representing events  Dynamic properties are represented as properties and not as objects of properties as in reification  Event objects ◦ Attached to specific static objects ◦ Connect to Time Intervals 5

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7.  Advantages ◦ Changes affect only related dynamic objects, not the entire ontology ◦ Reasoning mechanisms and semantics of OWL fully supported ◦ OWL 2.0 compatible  Disadvantages ◦ Proliferation of objects Techical University Of Crete Intelligent Systems Laboratory7

8.  Information: Points, intervals  Quantitative and Qualitative information  Qualitative Allen Relations (e.g., Before, After) are supported ◦ Qualitative relations connect temporal intervals ◦ Intervals with unknown endpoints ◦ Interval relations are translated into point relations (Before, After, Equals) Techical University Of Crete Intelligent Systems Laboratory8

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10.  Infer new relations from existing ones ◦ Before(x,y) AND Before(y,z)  Before(x,z)  Problem 1: Reasoning over a mix of qualitative and quantitative information is a problem ◦ Extract qualitative relations from quantitative ones ◦ Reasoning over qualitative information  Problem 2: Assertions may be inconsistent or new assertions may take exponential time to compute  Solution: Restrict to tractable sets decided by polynomial algorithms such as Path Consistency [ van Beek & Cohen 1990]

11.  Path Consistency suggests composing and intersecting relations until: ◦ A fixed point is reached (no additional inferences can be made) ◦ An empty relation is yielded implying inconsistent assertions  Path Consistency is tractable, sound and complete for specific sets of temporal relations Techical University Of Crete Intelligent Systems Laboratory11

12.  Compositions and intersections of relations are defined and implemented in SWRL:  Before(x,y) AND Equals(y,z)  Before(x,z)  (Before(x,y) OR Equals(x,y)) AND (After(x,y) OR Equals(x,y))  Equals(x,y) Techical University Of Crete Intelligent Systems Laboratory12

13.  SPARQL-like query language supporting temporal operators SELECT ?x, ?y… Where { ?x property ?y… At(date)…}  Additional operators are introduced to SPARQL ◦ AT, ALWAYS_AT, SOMETIMES_AT ◦ Allen operators

14. Temporal Operators  AT specifies time instants for which fluent properties hold true  ALWAYS_AT, SOMETIMES_AT: Return intervals that fluent always or sometime holds.  Allen’s operators: BEFORE, AFTER, MEETS, METBY, OVERLAPS, OVERLAPPEDBY, DURING, CONTAINS, STARTS, STARTEDBY, ENDS, ENDEDBY and EQUALS Techical University Of Crete Intelligent Systems Laboratory14

15. SELECT ?x,?y WHERE {?x has-employee ?y AT “3-5-2007” } Techical University Of Crete Intelligent Systems Laboratory15

16. SELECT ?x,?y WHERE {?x has-employee ?y BEFORE company1 has-employee ?y } Techical University Of Crete Intelligent Systems Laboratory16

17.  We extended 4D-fluents and N-ary relations for representing evolution of qualitative (in adition to quantitative) temporal information in OWL ontologies  Offers temporal reasoning support over qualitative and quantitative relations  Querying support by extending SPARQL with additional temporal operators Techical University Of Crete Intelligent Systems Laboratory17

18.  Extending representation-query language for spatial information [Batsakis & Petrakis, RuleML 2011]  Optimizations for large scale applications Techical University Of Crete Intelligent Systems Laboratory18

19. Questions ?