Athena

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  • Advantages: Oracle is the DMBS chosen in Geopkdd for storing sptaio-temporal data. It also has been chosen for data warehouse. We aim at experimenting the reasoning capabilities. Disadvantages: The tools is not very friendly to use, the OWL reasoning engine is available from version 11g, just distributed on the market. The OWL fragment is a subset of OWL DL called OWLPRIME. Limited expressive power
  • Advantages: this is the simplest case, exploiting of the ontology reasoning capabilities. The user query the ontology directly. Disadv: size of the ontology may grow, the query are possible only on the ontology: no possibility to query ontology joint with relational data Other scenarios are possible
  • Advantages: this is the simplest case, exploiting of the ontology reasoning capabilities. The user query the ontology directly. Disadv: size of the ontology may grow, the query are possible only on the ontology: no possibility to query ontology joint with relational data Other scenarios are possible
  • This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
  • Advantages: this is the simplest case, exploiting of the ontology reasoning capabilities. The user query the ontology directly. Disadv: size of the ontology may grow, the query are possible only on the ontology: no possibility to query ontology joint with relational data Other scenarios are possible
  • This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
  • This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
  • Advantages: this is the simplest case, exploiting of the ontology reasoning capabilities. The user query the ontology directly. Disadv: size of the ontology may grow, the query are possible only on the ontology: no possibility to query ontology joint with relational data Other scenarios are possible
  • This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
  • This step allow to link the ontology concepts with the data in the database. All the data become triples and populate the ontology.
  • Athena

    1. 1. Enriching trajectory and trajectory pattern semantics with background knowledge Chiara Renso, Roberto Trasarti KDDLab, ISTI, CNR, Italy Stefano Spaccapietra, Christine Parent, Jose Macedo , Zhixian Yan EPFL, Lausanne, Switzerland Miriam Baglioni Pisa University, Italy Monica Wachowicz Technical University of Madrid, Spain
    2. 2. Athena: Trajectories and city places Athena the greek goddess of wisdom Hotel University Monuments Show kinds of points of interest and landmarks
    3. 3. And more … select kinds of trajectories according to the application domain TouristTrajectory ≡ Trajectory ⊓ ∃hasStop.∃isLocatedIn.TouristPlace ⊓∃hasStop.∃isLocatedIn.AccomodationPlace SELECT trajectory FROM ‘?trajectory rdf:type :TouristTrajectory’; Tourist Trajectories
    4. 4. GeoPKDD Tasks
    5. 5. The idea (1/2) <ul><li>Exploit Ontologies as a formal knowledge representation language and reasoning engine to add a semantic layer on top of trajectories and data mining patterns </li></ul><ul><li>Advantages: </li></ul><ul><ul><li>allows a better interpretation of trajectories and patterns based on an encoded domain knowledge </li></ul></ul><ul><ul><li>User can query on well known concepts a part from data details </li></ul></ul><ul><li>Problems: </li></ul><ul><ul><li>At the moment no explicit support for spatial and spatio temporal reasoning in ontology formalisms and engines </li></ul></ul><ul><ul><li>Poor support in available tools for reasoning on large datasets </li></ul></ul>
    6. 6. The idea (2/2) <ul><li>Exploit semantic trajectories (e.g. stop and moves) </li></ul><ul><ul><li>They have a simpler and compact representation respect to raw trajectories </li></ul></ul><ul><ul><li>They already encode semantic information </li></ul></ul><ul><li>Use an ontology formalism: </li></ul><ul><ul><li>OWL as a representation language and an efficient reasoning engine for large datasets (instances): Oracle 11g </li></ul></ul><ul><li>Devise a process that allows to conciliate trajectory and trajectory patterns with an ontology; </li></ul>
    7. 7. Our Contribution so far … <ul><li>Ontology modules for organize knowledge about geography, domain and trajectory; </li></ul><ul><li>Process for conciliating data with ontologies; </li></ul><ul><li>A prototype based on Oracle11g Semantic Technologies: ATHENA. </li></ul>
    8. 8. Organizing the Knowledge …
    9. 9. The trajectory ontology Geography Ontology (GO) Traffic Domain Ontology (ADO) Geometric Trajectory Ontology (GTO) StreetG Time Instant SimpleTime Geo Line SimpleGeo B.E.S Move Point Interval Person Trajectory Surface hasGeometry hasGeometry hasGeometry hasTime islocatedIn hasTrajectory from is-a is-a is-a is-a is-a is-a Crossing between follows RoadWork islocatedIn StreetT sameAs hasMove Stop Begin End to hasStop hasEnd hasBegin is-a isLocatedIn is-a is-a is-a PointOfInterest Museum Hotel is-a is-a hasHome hasWork locatedIn LongTerm RoadWork GasStation Car hasCar LongTime Interval is-a is-a is-a
    10. 10. All Data are inside DB !! Geometric Trajectory Geographic Knowledge Domain Knowledge HERMES Mobility Pattern Mobility Data Raw Data
    11. 11. Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst
    12. 12. Taxonomies and Axioms Time CityPlace Bridge Church … TouristTrajectory ≡ Trajectory ⊓ ∃hasStop.∃isLocatedIn.TouristPlace ⊓∃hasStop.∃isLocatedIn.AccomodationPlace Morning Geometric Trajectory Ontology (GTO) Time Instant SimpleTime Geo Line SimpleGeo B.E.S Move Point Interval Trajectory Surface hasGeometry hasGeometry hasTime from is-a is-a is-a is-a is-a is-a follows hasMove Stop Begin End to hasStop hasEnd hasBegin is-a isLocatedIn is-a is-a is-a LongTime Interval is-a Afternoon Evening Monument Museum
    13. 13. Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst
    14. 14. Mapping Ontology to DB <ul><li>Using a MAP file where each concept in Ontology is mapped to a query in the DB (GAV approach) </li></ul><ul><li>Use this mapping to import domain data into ontology </li></ul><ul><li>Space elements</li></ul><ul><li>SPACE_Monument = Select rownum as id, border as area from cells where name like '%G1‘ </li></ul><ul><li>ime elements</li></ul><ul><li>TIME_Morning = Select * from Intervals where type = 'Morning‘ </li></ul><ul><li>rajectories</li></ul><ul><li>Moving_objects = Select * from Milano_dataset </li></ul>
    15. 15. Semantic Trajectory HasTrajectory hasComponents BEStop 0:N list 1:1 2:N list 1:1 IsIn 0:1 0:N Move ƒ(T) To 0:1 1:1 1:1 0:1 Its personalization --> IsIn 0:1 0:N The hooks TravelingOT Trajectory SpatialOT1 Bird name birth year location Does Migration year North/South StopsIn Country 0:N list 1:1 2:N list 0:N From SpatialOT2
    16. 16. Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst
    17. 17. From Tables to Triples <ul><li>Populate the ontology with semantic trajectories and trajectory patterns. </li></ul><ul><li>Consider the trajectory 1 stops at place_1 and place_2 in the morning, we represent these tuples as: </li></ul><ul><li>[.. definition of the place and the time ..] (Trajectory_1, rdf:type, trajectory) (Trajectory_1, rdf:has_id, 1) (Trajectory_1, rdf:has_stop, stop_1) (Trajectory_1, rdf:has_stop, stop_2) (stop_1, rdf:type,stop) (stop_1, rdf:has_id,1) (stop_1, rdf:is_at, place_1) (stop_1, rdf:has_time, morning) (stop_2, rdf:type,stop) (stop_2, rdf:has_id,2) (stop_2, rdf:is_at, place_2) (stop_2, rdf:has_time, morning) </li></ul>
    18. 18. Reasoning Services <ul><li>Run reasoning services to infer new knowledge: </li></ul><ul><li>EXECUTE sem_apis.create_entailment( </li></ul><ul><li>'owltst2_idx', </li></ul><ul><li>sem_models('geopkdd_owl_ontology'), </li></ul><ul><li>sem_rulebases('OWLPRIME','USER_RULEBASE'), </li></ul><ul><li>SEM_APIS.REACH_CLOSURE, null, 'USER_RULES=T'); </li></ul>TouristTrajectory ≡ Trajectory ⊓ ∃hasStop.∃isLocatedIn.TouristPlace ⊓∃hasStop.∃isLocatedIn.AccomodationPlace
    19. 19. Semantic Enrichment Process ATHENA ORACLE + (SPATIO-TEMPORAL & DATA MINING & SEMANTIC FEATURES) TRAJECTORY ONTOLOGY Semantic Trajectories stops, moves,etc Trajectories Patterns TAS, Domain Information Domain Geography Geometric Import ABOX mapping Import TBOX Create Ontology Query SQL+Semantics 1 5 3 2 4 Analyst
    20. 20. Querying only the ontology <ul><li>Which are the Tourist Trajectories? </li></ul><ul><li>SELECT m </li></ul><ul><li>FROM table( </li></ul><ul><li>SEM_MATCH(' ( ?m rdf:type :TouristTrajectories ) ', </li></ul><ul><li>SEM_Models('geopkdd_owl_ontology'),null, </li></ul><ul><li>SEM_ALIASES(SEM_ALIAS ('','http://www.owl-ontologies.com/GeoPKDDOnto.owl#')),null)); </li></ul>
    21. 21. Querying the ontology + original data <ul><li>Give me tourist trajectories and the name of his user? </li></ul><ul><li>SELECT t.object, t.user_name </li></ul><ul><li>from GEOPKDD.MILANO_SMALL t, </li></ul><ul><li>(SELECT get_id(m) as id </li></ul><ul><li> FROM table(SEM_MATCH(' (?m ?s :TouristTrajectory) ', </li></ul><ul><li> SEM_Models('MODELGEOPKDD'), </li></ul><ul><li> SEM_rulebases('owlprime'), </li></ul><ul><li> SEM_ALIASES( </li></ul><ul><li>SEM_ALIAS('','http://www.com/GeoPKDDOnto.owl#')),null))) r </li></ul><ul><li>WHERE t.id = r.id </li></ul>
    22. 22. Ongoing work <ul><li>We are currently extending this work in two main directions: </li></ul><ul><ul><li>Integrating data mining in the scenario: understand people behaviour analyzing large amount of data. </li></ul></ul><ul><ul><li>Experimenting the system in a larger dataset coming from GPS on cars and moving in Milan and Rio de Janeiro data set . </li></ul></ul><ul><li>Automatize the process as much as possible (mapping, triples insertion) </li></ul><ul><li>Study solutions to cope with the limitations of OWLprime </li></ul><ul><li>Integration with CommonGIS visualization tool </li></ul>

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