SOWL:Spatiotemporal Representation , Reasoning and Querying over the Semantic Web

1. SOWL:SpatiotemporalRepresentation, ReasoningandQueryingovertheSemantic Web Sotiris Batsakis, Euripides G.M. Petrakis Technical University Of Crete Intelligent Systems Laboratory

2. Introduction • SOWL • Spatiotemporal OWL ontology • Representation of quantitative and qualitative information • Spatiotemporal reasoning using SWRL rules • Spatiotemporal query language Techical University Of Crete Intelligent Systems Laboratory

3. State-of-the- art • Temporal relations involve at least three arguments • OWL represents binary relations • Evolution of information in time? • Existing approaches: Temporal RDF, Named Graphs, Reification, Versioning, 4D fluents • No qualitative information • No mixing of spatial and temporal information • Limited OWL reasoning support • Querying of spatiotemporal information is also a problem Techical University Of Crete Intelligent Systems Laboratory

4. 4D Fluents[Welty & Fikes 2006] Classes TimeSlice, TimeInterval are introduced Dynamic classes 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

5. 4-D fluents example Techical University Of Crete Intelligent Systems Laboratory

6. 4-D fluents: Comments • Advantages • Changes affect only related objects, not the entire ontology • Reasoning mechanisms and semantics of OWL fully supported • OWL 2.0 compatible • Disadvantages • Data redundancy Techical University Of Crete Intelligent Systems Laboratory

7. Extending 4-D fluents • SOWL shows how temporal and spatial information is unified and represented within the same ontology • Information: Points, intervals, list of points.. • Quantitative and Qualitative information • Qualitative Allen Relations (e.g., Before, After) are introduced • Qualitative relations connect temporal intervals • Intervals with unknown endpoints Techical University Of Crete Intelligent Systems Laboratory

8. Allen Temporal Relations Techical University Of Crete Intelligent Systems Laboratory

9. Reasoning • Infer new relations from existing ones • SOUTH(x,y) AND SOUTH(y,z) SOUTH(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 [Renz & Nebel 2007]

10. Temporal Reasoning(1/2) • Based on Path Consistency,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 (2/2) • Compositions and intersections of relations are defined and implemented in SWRL: • During(x,y) AND Meets(y,z) Before(x,z) • (Before(x,y) OR Meets(x,y)) AND Meets(x,y)Meets(x,y) Techical University Of Crete Intelligent Systems Laboratory

12. Spatial Representation • Different forms of spatial information are supported in SOWL • Topologic Relations (inside/outside, connected/disconnected …) • Directional (North, South, East, SouthEast …) • Computed from raw data (images, points, shape contours), spatial projections

13. Topologic Relations

14. Directional Relations

15. Spatial Reasoning • Apply path consistency on topologic and directional relations • Restrict to tractable sets of spatial relations [Renz, Nebel 2007] • Polynomial with respect to the number of spatial entities • Example SWRL rules: • NTPP(x,y) AND DC(y,z)DC(x,z) • SOUTH(x,y) AND SOUTH(y,z) SOUTH(x,z)

16. SOWL Query Language • SQL-like query language supporting spatiotemporal operators SELECT … AS… FROM … AS … WHERE …LIKE “string” AND… • Additional operators • Temporal (AT, Allen), Spatial Operators (Topologic, Directional) • General purpose operators (MINUS,UNION, UNION ALL, INTERSECTS, ALL, ANY, IN)

17. Temporal Operators • AT specifies time instants or intervals for which fluent properties hold true • TIME: Return interval that fluent 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

18. AT Temporal Operator Example SELECT Company.companyName FROM Company, Employee WHERE Company.hasEmployee:Employee AT(3,5)AND Employee.employeeName LIKE“x” Techical University Of Crete Intelligent Systems Laboratory

19. Allen Operator Example SELECT Company.companyName FROM Company, Employee AS E1, Employee AS E2 WHERE Company.hasEmployee:E1 BEFORE Company.hasEmployee:E2 AND E1.employeeName like “x” AND E1.employeeName LIKE “y” Techical University Of Crete Intelligent Systems Laboratory

20. Spatial Operators • Apply on locations • Topologic (e.g., INTO), • Directional (e.g., NORTH-OF) and • Range (e.g., IN RANGE > “distance’’) Techical University Of Crete Intelligent Systems Laboratory

21. Spatial Query Example SELECT Company.companyName FROM Company WHERE Company NORTH-OF “Attica” AT(3)AND Company.companyName LIKE “x” Techical University Of Crete Intelligent Systems Laboratory

22. Conclusion • SOWL is a mechanism for representing and querying spatiotemporal information in OWL ontologies • Offers temporal and spatial reasoning support over qualitative relations Techical University Of Crete Intelligent Systems Laboratory

23. Future Work • SPARQL based query language • Optimizations for large scale applications • Alignment with existing vocabularies (OWL-Time, GeoRSS) Techical University Of Crete Intelligent Systems Laboratory

24. Thank you Questions ?