SEMANTIC AUGMENTATION OF GEOSPATIAL CONCEPTS:

Transcription

1 SEMANTIC AUGMENTATION OF GEOSPATIAL CONCEPTS: THE MULTI-VIEW AUGMENTED CONCEPT TO IMPROVE SEMANTIC INTEROPERABILITY BETWEEN MULTIPLES GEOSPATIAL DATABASES Mohamed Bakillah Mir Abolfazl Mostafavi, Center for Research in Geomatic Laval University, Canada 2010

2 Organization Context Problem Objective Proposed Approach Multi-View Augmented Concept () Model Generation Method How the Supports Improvement Semantic Interoperability Conclusion and Future Work

3 Context Growing number geospatial data producers, sophisticated data collection technologies and availability networking technologies create huge amount data for geospatial users People and organizations need to share geospatial data in order to maximise their resources and to reduce risks wrong decisions Semantic interoperability is a fundamental issue for ensuring data sharing and reuse among multiple geospatial databases and their users

4 Problematic Semantic heterogeneity geospatial data is one the main obstacle to semantic interoperability Semantic heterogeneity is caused by differences in meaning and representation s Difference arise because geospatial databases were build for different purposes, by different organizations

5 Problematic Semantics geospatial s are mostly implicit (ex: not representing context or semantics spatiotemporal properties) Some semantic mapping approaches rely on poor s representations Implicit semantics cannot be compared Results: semantic misinterpretations by different users the user is unable to select appropriate data sets for his use

6 Objectives Representation geospatial s with richer semantics: the Multi-View Augmented Concept () Model Develop a methodology for the generation s in support improved semantic interoperability multiple geospatial databases

7 Proposed Approach The Multi-View Augmented Concept () Approach: Multi-View Augmented Concept () Model Generation Method

8 Model Idea: s ontologies describing geospatial databases that have poor semantics can be semantically augmented The is a that: represents the different views that a can have under different contexts represents semantics spatiotemporal properties with new features called spatiotemporal descriptors augments the with () s features can be expressed with Description Logics (DL) to support reasoning

9 Model = < n(c), {p(c)}, {r(c)}, {spatial_d(c)}, {temporal_d(c)}, {v(c)}, {ctx}, {dep(c)} > n(c): name the {p(rp)}: set properties, with rp the range a property {r(rr)}: set, with rr the range a relation {spatial_d(rsd)}: set spatial descriptors, which role is to describe the spatiality the, with rsd the range the spatial descriptor {temporal_d(rtd)}: set temporal descriptors, which role is to describe the temporality the, with rtd the range the temporal descriptor {v(c)}: set views, where a view is a selection the s feature valid in a given context {ctx}: set different contexts for the {dep(c)} is the set features

10 Generation Method

11 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures rules consistency View extraction and verification completeness Views the For each view Formalization into rules features Augmentation Inference the with

12 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures 1) Specification Context Extraction Validation rules: rules that will rules be used consistency to extract views Ex: Context(Land, Flooding) State(Land, Waterlogged) View extraction Formalization and verification completeness into rules Views the For each view features Augmentation Inference the with

13 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures rules consistency View extraction and verification completeness Views the For each view Formalization into rules features Augmentation Inference the with

14 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures 2) Inference new rules rules: reasoning engine take as input ontology rules and produces new consistency rules (to ensure all rules are explicit) State(Land, Waterlogged) Geometry(Land, View extraction GML:MovingPolygon) and verification completeness Geometry(Land, GML:MovingPolygon) RefersTo(Geometry, WaterloggedArea) Views the Rule-based Formalization Reasoning Engine into rules For each view features Augmentation State(Land, Waterlogged) Inference RefersTo(Geometry, the with WaterloggedArea)

15 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures rules consistency View extraction and verification completeness Views the For each view Formalization into rules features Augmentation Inference the with

16 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures rules 3) rule consistency: to be conserved, inferred rules must consistency meet a minimal consistency threshold. A rule is consistent if it is verified by the instances the. View extraction Formalization and verification completeness into rules For State(Land, Waterlogged) each RefersTo(Geometry, Views the view Set B: instances WaterloggedArea) the land with features state = waterlogged Set A: instances and geometry refers Augmentation the land with to waterlogged Inference area state = waterloggedthe with

17 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures rules consistency View extraction and verification completeness Views the For each view Formalization into rules features Augmentation Inference the with

18 Specification Formulation 4) context first set Instances View Extraction : extraction rules s 4.1. Partial view extraction: each extraction rule is applied to the to create the sub- that respect this rule Computation Concept : Inference Land Geometry.(GML:Polygon validation GML:MovingPolygon) Extraction rule: new Context(Land, rules Flooding) Geometry(Land, measures users GML:MovingPolygon) Partial View: Land HasContext.Flooding Geometry. Validation GML:MovingPolygon rules consistency 4.2. View merging: partial views that pertains to the same context and that are non contradicting are merged into a final view View extraction Formalization Partial View 1: and Land verification HasContext.Flooding State.Waterlogged Partial View 2: completeness Land HasContext.Flooding into rules Geometry.MovingPolygon Merged View: Land HasContext.Flooding For Geometry.MovingPolygon State.Waterlogged each Views the view features Augmentation Inference the with

19 Specification context extraction rules Formulation first set Instances s Computation 5) View Completness Inference : Validation step - checking view completeness: applying validation the union operator new on rules all views must result in measures the itself users rules consistency View extraction and verification completeness Views the For each view Formalization into rules features Augmentation Inference the with

20 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures rules consistency Augmentation phase View extraction and verification completeness Views the For each view Augmentation the with Formalization into rules features Inference

21 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures Augmentation rules 6) Formulation consistency : for each view, phase that express logical implication links values each pair features (properties, or descriptors) are expressed as rules : View extraction Formalization Ex: water and level(land, verification high) role(land, navigable) water level(land, completeness high) role(land, practicable into rules by motor vehicle) water level(land, low) For role(land, navigable) water level(land, low) each role(land, practicable by motor vehicle). Views the view features this first set must be verified against instances views the to determine Augmentation the valid Inference the with

22 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures rules consistency Augmentation phase View extraction and verification completeness Views the For each view Augmentation the with Formalization into rules features Inference

23 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures Augmentation rules 7) Computation consistency rule validation measures: for each rule expressing phase a dependency generated in previous step, we determine the values confidence and support (association View extraction rule mining measures): Formalization and verification completeness into rules Ihead = number instances For a view that respects head rule IBody = number instances each a view that respects body rule Itotal = total number Views the instances view a view Support = Ihead Ibody Itotal Augmentation the with features Confidence = Inference Ibody Ihead

24 Specification Formulation context first set Instances extraction rules s 8) : we keep that meet a given support and confidence threshold 9) Formulation Computation Inference into rules: then integrated into the definition validation new rules measures users rules consistency Augmentation phase View extraction and verification completeness Views the For each view Augmentation the with Formalization into rules features Inference

25 Specification context extraction rules Formulation first set Instances s users Inference new rules Computation validation measures rules consistency Augmentation phase View extraction and verification completeness Views the For each view Augmentation the with Formalization into rules features Inference

26 Specification context 10) Inference extraction : rules Formulation first set Instances s - infer Inference (generalisation/specialisation, Computation and other) views ( the same or different validation new s) rules to complete the MVA ontology measures users - Infer global context the from contexts views Augmentation rules phase Ex: context(view1, consistency flooding) context(view2, drought) flooding disaster area drought disaster area View extraction Formalization context(land, disaster area) and verification completeness into rules Views the For each view Augmentation the with features Inference

27 : Example Final Result Concept Land Disaster Area Land with view 1 = <context(flooding), {State(waterlogged), Water level(high), Role(navigable) geometry(gml: MovingPolygon)}, {adjacent(river)}, {SpatialDescriptor: referto(waterlogged area)}, {water level(land, high) role(land, navigable)} view 2 = <context(drought), {State(dessication), Water level(null), Role(practicable by motor vehicle) geometry(gml: Polygon)}, {SpatialDescriptor: referto(dry area)}, {water level(land, null) role(land, practicable by motor vehicle)}

28 How supports improvement semantic interoperability Improves semantics explicitness Supports multi-context semantic interoperability: Semantic mapping different s depends on the context ; for example, the user may select only the view corresponding to the context that fits his own context

29 How supports improvement semantic interoperability with similar structures help to discover missing semantic mappings ontologies different geospatial databases Simple example: dependency a first : water level(land, high) role(land, navigable) dependency a second : depth(land, high) role(land, navigable) similar structures those suggest that depth and water level represent a similar property

30 Conclusions and Future Work Model can play an important role in a global semantic interoperability approach designed for ad hoc networks where ontologies databases are very heterogenous Model opens new research opportunities: Develop a semantic mapping model with ability to match needs to be done Integrate tool as a way to enrich existing geospatial databases ual models