MUGGES: User-aware Semantic Location Models for Service Provision
Semantic Location Based Services for Smart Spaces
description
Transcript of Semantic Location Based Services for Smart Spaces
Semantic Location Based Services for
Smart Spaces
Kostas Kolomvatsos, Vassilis Papataxiarhis, Vassileios Tsetsos
Pervasive Computing Research GroupCommunication Networks Laboratory
Department of Informatics and TelecommunicationsUniversity of Athens – Greece
MTSR ‘07 @ Corfu, GreeceMTSR ‘07 @ Corfu, Greece
Outline
Introduction
Spatial Ontology
GIS metadata and ontology
population
Hybrid Navigation Algorithm
Conclusions
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
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 LBSLocation Based Services
(LBSs)
+
Semantic Web technologies
Semantic LBS
Spatial Model Indoor Navigation Ontology It describes the basic elements of indoor environment
and the basic relationships between them It facilitates path searching
owl:Thing
Space Point_of_InterestPathObstacle
Exit Entrance
Transition_PointNavigational_Point
Vertical_Passage
Path_PointPassage
Path_Element
Horizontal_Passage Motor_Passage
Description
Junction
Turn_Point
End_Point
Image_Description
Video_Description
Audio_Description
Corridor
Floor
Room
Building Corridor_Segment
Door
Elevator
Escalator
Stairway
Ramp Building_Entrance
Open_Area_Entrance
Closed_Area_Entrance
Room_Entrance
Building_Exit
Open_Area_Exit
Closed_Area_Exit
Room_Exit
DangerousSpaceElements PositioningPoint
http://p-comp.di.uoa.gr/projects/ontonav/INOdoc/index.html
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
Flo o r M a p
C o rrid o rs
Na vig a tio n Po ints
Ro om Entra nc es
Sta irwa ys
...
Basic point metadata x,y coordinates Floor Id Label, etc.
Ontology Population Spatial Database creation
Transform GIS Layers to Spatial Database Tables Automatic instantiation of INO through GIS metadata
SpatialDatabase
Instances Creation
Algorithm
Flo or M a p
C o rrido rs
Na viga tio n Po ints
Ro om Entra nc e s
Sta irwa ys
...
Ontology Instances
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
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
System Functionality (I)
SpatialDB
Building blueprints (GIS)
Building graph
IndoorNavigation
Ontology (INO)
DataMigration
INOinstances
User profile(capabilities)
User-compatible INO
instances
User-compatible graph
System Functionality (II)
COM SEM
User profile
User location and destination
Best TraversablePath
K-simplestpaths
User-compatible graph
Perceptual Rules
SEM : Semantic Path Selection
PRS
User
PRS : Path Presentation
COM : Complexity Path Computation
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: ABDIHA little longer than ACFGH, but
much easier to describe !
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
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.
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
QUESTIONS?
Thank you!
http://p-comp.di.uoa.gr