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Semantic Location Based Services for Smart Spaces

This paper explores the integration of semantic technologies into Location-Based Services (LBS) to improve navigation and user experience in smart spaces. We discuss the use of spatial ontologies and metadata for enhanced representation and management of spatial data. A hybrid navigation algorithm is proposed, addressing the complexities of multi-criteria searches and user compatibility. Key functionalities and implementation details, including the creation of spatial databases and the challenges faced in deploying effective reasoning engines, are presented. This research aims to facilitate universal access to optimized LBS experiences.

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Semantic Location Based Services for Smart Spaces

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  1. Semantic Location Based Services for Smart Spaces Kostas Kolomvatsos, Vassilis Papataxiarhis, Vassileios Tsetsos Pervasive Computing Research Group Communication Networks Laboratory Department of Informatics and Telecommunications University of Athens – Greece MTSR ‘07 @ Corfu, Greece

  2. Outline • Introduction • Spatial Ontology • GIS metadata and ontology population • Hybrid Navigation Algorithm • Conclusions

  3. Location Based Services • LBS: The core of “smart environments” • Are the most popular context-aware services • Navigation service: One of the biggest challenges due to its complexity • Current Objective • Universal and optimized access to LBSs • Advanced user experience • Current Limitation • Existing representation techniques lead to incapability of “smart” management and exploitation of spatial data

  4. Location Based Services (LBSs) + Semantic Web technologies Semantic LBS Motivation • Navigation • Typical representation formalism: spatial graphs • Problems • Multi-criteria search NP-hard • Redesign needed to extend an existing algorithm with more criteria • Conclusion: Traditional algorithmic approaches seem to fail • Proposed solution: Semantic enrichment of LBS

  5. Spatial Model • Indoor Navigation Ontology • It describes the basic elements of indoor environment and the basic relationships between them • It facilitates path searching http://p-comp.di.uoa.gr/projects/ontonav/INOdoc/index.html

  6. GIS Annotation • Layered architecture • Each layer corresponds to a basic concept (or set of concepts) in INO • Lower layer : Building blueprints • Second layer : Corridors (lines) • Points are defined upon corridors • Basic point metadata • x,y coordinates • Floor • Id • Label, etc.

  7. Ontology Population • Spatial Database creation • Transform GIS Layers to Spatial Database Tables • Automatic instantiation of INO through GIS metadata Instances Creation Algorithm Spatial Database Ontology Instances

  8. Instances Creation Algorithm • Based on GIS data • The algorithm involves the following steps for all floors in the building: • Find which points belong to each corridor • Find the ends of each corridor • Find the neighbors of each point • Create the instances in INO classes indicated by the GIS layers and the information extracted in the previous steps

  9. Navigation Algorithm • Hybrid rule-based algorithm. Takes into account : • Route complexity • Euclidean route distance • User profile (capabilities and preferences) • Steps: • Create “user compatible” building graph based on user profile and application of access rules • E.g. WheelChaired_User(?x) ^ Stairway(?y) isObstacleFor(?y,?x) • Find the k-simplest paths • Assign the total cost of each path as a function of bonuses and penalties of the total path distance, preferences and perceptual rules

  10. System Functionality (I) Building blueprints (GIS) User-compatible graph Building graph Data Migration Spatial DB INO instances User-compatible INO instances Indoor Navigation Ontology (INO) User profile (capabilities)

  11. System Functionality (II) COM : Complexity Path Computation SEM : Semantic Path Selection User-compatible graph User profile PRS : Path Presentation SEM COM PRS K-simplest paths Best Traversable Path User User location and destination Perceptual Rules

  12. Navigation Example A H: 4 possible paths 1) ACFGH shortest path 2) ABDIH simplest path 3) ABDEFGH, node E: stairs 4) ACFEDIH , node E: stairs Selected Path: ABDIH A little longer than ACFGH, but much easier to describe !

  13. Implementation Details • ESRI ArcGIS software • PostGIS spatial DB • Protégé Ontology Editor • Knowledge Representation Languages • Ontology models in OWL-DL • SWRL rules • Bossam for OWL and SWRL reasoning • Mascopt Library for graph creation and path search

  14. Semantics is not everything • Example: Orientation Issues • Two extra properties storing the real GIS coordinates of each door and not only its projection to the corridor • Compute the angle between the line vertical to user’s direction and the line specified by the user’s position to the door. • If angle θ > 90,the door is on the left side • Else, the door is on the right • Similar process for the turns.

  15. Contributions and Open Issues • Main Contributions • Semantic representation of GIS metadata with the aid of a spatial ontology • A rule-based hybrid combination of k-simplest paths search algorithm with Euclidean distance and other application parameters like user profile and abilities/preferences. • Support for flexible navigation schemes • Content-based navigation • Presentation-based navigation • Open Issues • Immature reasoning engines in terms of performance and interoperability with rule engines • Development/Improvement of tools for spatial ontology population

  16. Thank you! QUESTIONS? http://p-comp.di.uoa.gr

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