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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 Re-using existing algorithms Algorithm D’ Domain Algorithm B Task Suited to domain and task How can algorithms be customized to varying domain and task? Relation between data mining and ontology construction Ontology (Knowledge acquisition) Knowledge Information Data Data Mining (Knowledge discovery) Level of abstraction Ontology Construction Role of formal ontology in KDD Guide algorithms such that they can be suitable for domain-specific and task-oriented concepts KDD Process Diagram Provide the context in which the knowledge extracted from data is interpreted and evaluated Using ontology for spatial data mining Ontology Spatial Data Mining High-level knowledge Domain Model Task Model Low-level data 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 Domain-specific spatial data mining Let’s compare two different domains: traffic accident versus retailers Domain of traffic accident Is-a Event Spatial In road network constraints Domain of retailers Physical object 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 Partition market traffic accident areas to a retailer # of clusters k Depend on spatial distributn. Given (resource constraint) Level of details 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 Ontology as an active component of information system Top-level Ontology Domain Ontology e.g. space, time, matter, object, event Task Ontology e.g. diagnosing e.g. medicine dependence Application Ontology subject From Guarino, 1998 Conceptual framework for ontologybased 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 TemporalThing 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, As a temporal thing, Point(x) On(x, y) Roadway(y) Line(y) In(y, z) Geographic-Region(z) 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 Geographic-Scale Detail-Level Constraint: Spatial Objects Operational Constraints Source: Spatial Objects Target: Clusters 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 Setting Metadata Theme := Traffic Accident User interface OBS C Goal := “identify hot spots” LevelOfDetail := State PlaceName := New York Method Algorithm := SMTIN Input: 353 features in Erie Constraint := Named-Roadway Output: 18 clusters in Erie County Case study: Effect of scale (Task ontology) Control Algorithm TASK OBSC Algorithm TASK LevelOfDetail := Null LevelOfDetail := County PlaceName := Null PlaceName := New York DOMAIN Constraint := Roadway DOMAIN Constraint := Roadway Specifying area of interest doesn’t mask details OBSC clusters reflect spatial distribution specific to the scale of users’ interest Case study: Effect of constraint (Domain ontology) Control Algorithm TASK TASK LevelOfDetail := State LevelOfDetail := State PlaceName := New York PlaceName := New York DOMAIN Constraint := Null OBSC Algorithm DOMAIN Constraint := Roadway Separated by body of water OBSC clusters identify the physical barrier due to concept implicit in domain 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 reuse existing algorithms Ontology provides the systematic way of organizing various factors that dictate mechanisms underlying data mining process