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

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

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

Problems in focus  Developing new algorithms Algorithm D Algorithm C Algorithm A Algorithm B Algorithm D’ DomainTask  Re-using existing algorithms Suited to domain and task How can algorithms be customized to varying domain and task?

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

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

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 DomainModelTaskModel OntologySpatial Data Mining Low-level data High-level knowledge

Domain-specific spatial data mining  Let’s compare two different domains: traffic accident versus retailers Domain of traffic accident Domain of retailers Is-a Spatial constraints EventPhysical object In road network Outside of road network Spatial data mining algorithms should take into account different conceptualization (domain-specific properties)

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 Detect hotspots of traffic accident Partition market areas to a retailer # of clusters k Level of details Spatial data mining algorithms should take into account different tasks and users’ need Depend on spatial distributn. Given (resource constraint) Varies with scale (depends on area of users’ interest) Doesn’t vary with scale

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

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

Component of OBSDM

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

Component of OBSDM

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

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)

Component of OBSDM

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

Component of OBSDM

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

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

Component of OBSDM

OBSDM:: OBSDMM:: Alg. Builder OBSDM:: 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)

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

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

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

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

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

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