1. Query Rewriting Framework for Spatial Data
III-SGER: Spatio-Temporal-Thematic Queries of Semantic Web Data: a Study of Expressivity and Efficiency
Query Rewriting Framework for Spatial Data
Funded by NSF Award#IIS : "III-SGER: Spatio-Temporal-Thematic Queries of Semantic Web Data: a Study of Expressivity and Efficiency”

2. Increased retrieval of spatial information

3. But spatial querying still needs improvement

4. User expected to pose proper query

5. Question: “Find schools in NJ”,
What’s the problem
There is a need to analyze spatial information and queries beyond lexical and syntactic level.
Datasets on which queries are executed might have information, but not in the form expressed by query constraints.
Question: “Find schools in NJ”,
Answer: Sorry! Information is available for schools in counties.
Semantic relationship such as “part of” between query terms may or may not be understood properly
“Find Schools in Greene County”

6. What needs to be done?
Information should be retrieved even if mismatch exists between granularity of query constraints and knowledge base
User should not be burdened with knowing how the ontology is structured
Words which express semantic relations between entities should be included in the query mechanism

7. Proposed approach
Ease writing of expressive queries to implicitly exploit relations between entities.
Propose operators which hides the complexity of user knowing and expressing named relations between entities in query constraints
Behind the scene utilization and identification of semantic relations such as partonomy in answering queries.
Define transformation rules for operators to use work by Winston’s taxonomy of part-whole relations.
Rule based approach allows applicability in different domains such as life science with appropriate modifications.

8. Geonames schema modeling schools
County
School
Contains feature
Contains feature
Ohio

9. SPARQL query for schema
PREFIX rdf: PREFIX geo: < SELECT ?school WHERE { ?state geo:featureClass geo:A ?schools geo:featureClass geo:S. ?state geo:name "Ohio“ ?state geo:childrenFeatures ?county . ?county geo:childrenFeatures ?schools . }
Users must know exactly how schools, counties, and states are modeled, which is not trivial

10. Our approach Query Re-Writer SELECT ?school WHERE {
?state geo:featureClass geo:A ?schools geo:featureClass geo:S.
?state geo:name "Ohio“
?schools in ?state . }
Identify triple constraints to be replaced
Query Re-Writer
Triple constraints and variables rewritten using transformation rules
SELECT ?school
WHERE {
?state geo:featureClass geo:A ?schools geo:featureClass geo:S.
?state geo:name "Ohio“
?state geo:childrenFeatures ?county .
?county geo:childrenFeatures ?schools . }
Rewritten constraints and variables plugged into query envelope
Approach allows writing queries more naturally and independent of the underlying knowledge base

11. Domain Specific Transformation Rules
Architecture
SELECT ?school WHERE { ?school in Ohio. }
User submits SPARQL Query
Triple Constraints
Query Variables
Mapping of ontology properties to Winston’s categories
Query Rewriting Engine + =
Domain Specific Transformation Rules
Meta rules for Winston’s Categories
Altered Triple Constraints
Altered Query Variables
Rewritten Query according to the data structure
SELECT ?school
WHERE { ?state geo:name "Ohio“
?state geo:childrenFeatures ?county .
?county geo:childrenFeatures ?schools .}

12. Evaluation plan
Utilize publicly available datasets Geonames and Sensor Ontology for identifying partonomical relationships
Identify meaningful queries and their rewrite
Expose infrastructure as a web resource for utilization on other datasets.

13. Publication/Community Contribution
“A Framework to Support Spatial, Temporal and Thematic Analytics over Semantic Web Data” ,Matthew Perry, Amit Sheth and Prateek Jain (Under review in Transaction of Database Systems)
Submission for International Semantic Web Conference in June 2009.
Sensor Ontology, linked with Geonames Dataset to be made part of Linked Data Cloud (

14. Thanks