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

 SOWL ◦ Spatiotemporal OWL ontology ◦ Representation of quantitative and qualitative information ◦ Spatiotemporal reasoning using SWRL rules ◦ Spatiotemporal query language Techical University Of Crete Intelligent Systems Laboratory2

 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  Q uerying of spatiotemporal information is also a problem Techical University Of Crete Intelligent Systems Laboratory3

 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 4

Techical University Of Crete Intelligent Systems Laboratory5

 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 Laboratory6

 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 Laboratory7

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 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]

 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 Laboratory10

 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 Laboratory11

 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

 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)

 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)

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 Laboratory17

SELECT Company.companyName FROM Company, Employee WHERE Company.hasEmployee:Employee AT(3,5) AND Employee.employeeName LIKE “x” Techical University Of Crete Intelligent Systems Laboratory18

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 Laboratory19

Spatial Operators  Apply on locations  Topologic (e.g., INTO),  D irectional (e.g., NORTH-OF) and  Range (e.g., IN RANGE > “distance’’) Techical University Of Crete Intelligent Systems Laboratory20

SELECT Company.companyName FROM Company WHERE Company NORTH-OF “Attica” AT(3) AND Company.companyName LIKE “x” Techical University Of Crete Intelligent Systems Laboratory21

 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 Laboratory22

 SPARQL based query language  Optimizations for large scale applications  Alignment with existing vocabularies (OWL- Time, GeoRSS) Techical University Of Crete Intelligent Systems Laboratory23

Questions ?