temporal reasoning for supporting temporal queries in owl 2.0
DESCRIPTION
Temporal Reasoning for Supporting Temporal Queries in OWL 2.0. Sotiris Batsakis, Kostas Stravoskoufos Euripides G.M. Petrakis Technical University Of Crete Intelligent Systems Laboratory. Problem Definition. Representing evolution of information in time using OWL - PowerPoint PPT PresentationTRANSCRIPT
Temporal Reasoning for Supporting Temporal Queries in
OWL 2.0
Sotiris Batsakis, Kostas Stravoskoufos Euripides G.M. Petrakis
Technical University Of CreteIntelligent Systems Laboratory
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
Querying 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 Laboratory 2
Problem Definition
4D Fluents [Welty & Fikes 2006] 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
Techical University Of Crete Intelligent Systems Laboratory 4
4-D fluents example
N-ary approach [Noy & Rector 2006] 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
Techical University Of Crete Intelligent Systems Laboratory 6
N-ary and Reification example
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 Laboratory 7
4-D fluents/N-ary: Comments
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 Laboratory 8
Extending 4-D fluents/N-ary
Techical University Of Crete Intelligent Systems Laboratory 9
Allen Temporal Relations
Reasoning 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]
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 Laboratory 11
Temporal Reasoning(1/2)
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 Laboratory 12
Temporal Reasoning (2/2)
SOWL Query Language 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
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 Laboratory 14
SELECT ?x,?y WHERE {?x has-employee ?y AT “3-5-2007” }
Techical University Of Crete Intelligent Systems Laboratory 15
AT Temporal Operator Example
SELECT ?x,?y WHERE {?x has-employee ?y BEFORE company1 has-employee ?y }
Techical University Of Crete Intelligent Systems Laboratory 16
Allen Operator Example
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 Laboratory 17
Conclusion
Extending representation-query language for spatial information [Batsakis & Petrakis, RuleML 2011]
Optimizations for large scale applications
Techical University Of Crete Intelligent Systems Laboratory 18
Future Work
Thank you
Questions ?