Using formal ontology for integrated spatial data mining

1. Using formal ontology for integrated spatial data mining Julie Sungsoon Hwang Department of Geography State University of New York at Buffalo ICCSA04 Perugia, Italy May 14, 2004

2. Research purposes • Enlighten the role of formal ontology in KDD • Propose the conceptual framework for ontology-based spatial data mining • Case study: ontology-based spatial clustering algorithms

3. Problems in focus (cont.) • No single algorithm is best suited to all research purposes and application domains. • The same algorithm can yield results inconsistent with fact without considering domain knowledge • The same data may have to be analyzed in different ways depending on users’ goal

4. Re-using existing algorithms Domain Task Suited to domain and task Problems in focus • Developing new algorithms Algorithm D’ Algorithm D Algorithm A Algorithm C Algorithm B How can algorithms be customized to varying domain and task?

5. Ontology Ontology Construction (Knowledge acquisition) Level of abstraction Knowledge Knowledge Data Mining (Knowledge discovery) Information Data Relation between data mining and ontology construction

6. Guide algorithms such that they can be suitable for domain-specific and task-oriented concepts Role of formal ontology in KDD • Provide the context in which the knowledge extracted from data is interpreted and evaluated KDD Process Diagram

7. Using ontology for spatial data mining • Ontology formalizes how the knowledge is conceptualized, thereby making implicit meaning explicit • Data mining extracts a high-level knowledge from a low-level data, thereby enhancing the level of understanding Ontology Spatial Data Mining High-level knowledge Domain Model Task Model Low-level data

8. Domain-specific spatial data mining • Let’s compare two different domains: traffic accident versus retailers Event Physical object Outside of road network In road network Spatial data mining algorithms should take into account different conceptualization (domain-specific properties)

9. Task-oriented spatial data mining • Let’s compare two different tasks: detecting hotspots of traffic accident versus partitioning market areas based on the location of retail Depend on spatial distributn. Given (resource constraint) Varies with scale (depends on area of users’ interest) Doesn’t vary with scale Spatial data mining algorithms should take into account different tasks and users’ need

10. Ontology as an active component of information system e.g. space, time, matter, object, event Top-level Ontology Domain Ontology Task Ontology e.g. diagnosing e.g. medicine dependence Application Ontology subject From Guarino, 1998

11. Conceptual framework for ontology-based spatial data mining (OBSDM)

12. Component of OBSDM

13. OBSDM:: Input:: Metadata • Tag structure of XML can be utilized to inform domain ontology of the semantics of data

14. Component of OBSDM

15. OBSDM:: OBSDMM:: Domain Ont. • Terms within the “theme” tag in the metadata are used as a token to locate the appropriate domain ontology • Domain ontology specifies the definition, class, and properties • Class example: Accident is a Subclass-Of Temporal-Thing • Properties example: Road has a Geographic-Region as a Value-Type • Properties of class inherit from top-level ontology

16. Domain ontology := Traffic accident • Theory TRAFFIC-ACCIDENT-DOMAIN • As a spatial thing, • Point(x)  On(x, y)  Roadway(y) • Line(y)  In(y, z)  Geographic-Region(z) • As a temporal thing, • Point(x)  At(x, y)  Time(y) • Event(x) <=> Occurrence(x)  Notification(x)  Response(x)  Arrival(x) • Before(Occurrence(x), Notification(x)) • As an intangible thing, • Accident (x)  RelatedTo(x, y)  Vehicle(y)

17. Component of OBSDM

18. OBSDM:: Input:: User Interface • Users can specify a goal, level of detail, and geographic area of interest through UI

19. Component of OBSDM

20. OBSDM:: OBSDMM:: Task Ont. • The inputs specified by users in the user interface are translated into task ontology • Task ontology explicitly specify goal, methods, requirements, and constraint

21. Task ontology := Spatial clustering • Theory SPATIAL-CLUSTERING-TASK • Documentation: • This theory defines a task ontology for the spatial clustering task. The spatial clustering task, which is a class of clustering task, is a problem of grouping similar spatial objects into classes. • Super classes: Clustering • Subclasses: • Sub goal: • “Find hot spots” • “Group similar patterns” • “Partition into k-clusters” • Requirement: • Assignment-Object • Source: Spatial Objects • Target: Clusters • Geographic-Scale • Detail-Level • Constraint: • Spatial Objects • Operational Constraints

22. Component of OBSDM

23. OBSDM:: OBSDMM:: Alg. BuilderOBSDM:: Output:: GVis tool • Algorithm builder puts together requirements for building the best algorithm suited to domain of data and users’ input (task). • Data content is filtered through domain ontology, and the users’ requirement is filtered through task ontology. • The geographic visualization tool displays results (pattern discovered)

24. Input: 353 features in Erie Output: 18 clusters in Erie County Case study: ontology-based spatial clustering of traffic accidents Setting Metadata Theme := Traffic Accident User interface Goal := “identify hot spots” LevelOfDetail := State PlaceName := New York Method Algorithm := SMTIN Constraint := Named-Roadway OBSC

25. Case study:Effect of scale (Task ontology) Control Algorithm OBSC Algorithm TASK LevelOfDetail := Null PlaceName := Null DOMAIN Constraint := Roadway TASK LevelOfDetail := County PlaceName := New York DOMAIN Constraint := Roadway Specifying area of interest doesn’t mask details • OBSC clusters reflect spatial distribution specific to the scale of users’ interest

26. Case study:Effect of constraint (Domain ontology) Control Algorithm OBSC Algorithm TASK LevelOfDetail := State PlaceName := New York DOMAIN Constraint := Null TASK LevelOfDetail := State PlaceName := New York DOMAIN Constraint := Roadway Separated by body of water • OBSC clusters identify the physical barrier due to concept implicit in domain

27. Case study:Benefit of using ontology in spatial clustering • Incorporating ontology in spatial clustering algorithms enhances the quality of spatial clustering results • Task ontology makes clusters usable • Responsive to users’ view • Domain ontology makes clusters natural • Dictated by concept implicit in domain

28. Conclusion (cont.) • Presents how ontology are incorporated in spatial data mining algorithms • Semantic linkage between ontologies and algorithms through parameterization • Scale as a task-oriented property • Constraint as a domain-specific property

29. Conclusion • Ontology is examined as a means to customize algorithms to varying domain and task • Ontology enables algorithms to reflect concepts implicit in domain, and adapt to users’ view • Ontology provides the semantically plausible way to re-use existing algorithms • Ontology provides the systematic way of organizing various factors that dictate mechanisms underlying data mining process