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OntoPlan: Knowledge Fusion Using Semantic Web Ontologies

OntoPlan: Knowledge Fusion Using Semantic Web Ontologies. H é ctor Mu ñ oz-Avila Jeff Heflin. Overview. Motivation Background Semantic Web Ontologies Hierarchical (HTN) Plan Representation OntoPlan Architecture for Knowledge Fusion Task-Oriented Knowledge Fusion Knowledge Filtering

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OntoPlan: Knowledge Fusion Using Semantic Web Ontologies

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  1. OntoPlan: Knowledge Fusion Using Semantic Web Ontologies Héctor Muñoz-Avila Jeff Heflin

  2. Overview • Motivation • Background • Semantic Web Ontologies • Hierarchical (HTN) Plan Representation • OntoPlan • Architecture for Knowledge Fusion • Task-Oriented Knowledge Fusion • Knowledge Filtering • Coping with Heterogeneity • Dealing with dynamic Environments • Future Work • Final Remarks

  3. Motivation • Multiple, heterogeneous data sources including various kinds of sensors and databases • Bandwidth connection to some sources may be low • Too much information may be potentially relevant • Which information to provide to the warfighter? Low bandwidth UGS J-2 …

  4. Challenges • Task-Oriented Knowledge Fusion: Gap between the information available and the information needed • Knowledge Filtering: Large number of distributed information sources • Heterogeneity: Information sources commit to different schemas • Dynamic environments: Information changes rapidly • Information costs/value trade-off: latency time versus potential benefit

  5. Semantic Web Ontologies • Berners-Lee, et al. (Scientific American 01) • The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. • Ontology • a logical theory that accounts for the intended meaning of a formal vocabulary (Guarino 98) • has a formal syntax and unambiguous semantics • AI algorithms can compute what logically follows • Relevance to Web: • identify context • provide shared definitions • eases the integration of distinct resources

  6. OWL • Web Ontology Language • released as a W3C recommendation in February 2004 <rdf:Description rdf:about=“”> <owl:imports resource=“www.dod.mil/weapons.owl”> <rdf:Description> <Tank rdf:ID=“m1a1”> <name>M1A1 Abrams</name> <topSpeed>41.5</topSpeed> <hasArmament rdf:resource=“#cannon120mm”></Tank> … Logistics DBs <owl:Class rdf:ID=“Tank”> <rdfs:subclassOf resource=“#Armored”> </owl:Class> <owl:Class rdf:ID=“Armored”/> <Property ID=“topSpeed”> <domain resource=“#Tank”> </Property> <Property ID=“hasArmament”> <rdfs:domain rdf:resource=“#Tank”> <rdfs:range rdf:resource=“#Weapon”> </Property> … imports Weapons Ontology

  7. OWL Inference <owl:Property rdf:ID=“head”> <rdf:subPropertyOf rdfs:resource=“member” /></owl:Property> <owl:Class rdf:ID=“Terrorist”> <owl:sameClassAs> <owl:Restriction> <owl:onProperty rdf:resource=“member” /> <owl:someValuesFrom rdf:resource=“TerroristOrg” /> </owl:Restriction> </owl:sameClassAs></owl:Class> • The head of an organization is also a member of it • A member of a terror organization is a terrorist • Therefore, the head of a terror organization is a terrorist type Bin Laden Terrorist head type Al Qaeda TerrorOrg Main point: the various sources may be heterogeneous

  8. Hierarchical Task Networks (HTNs):Motivation • Practical: Can be used to encode information extraction strategies Strategic National JCS / NCA CINC Strategic Theater JTF Operational Tactical • Theoretical: Strictly more expressive than action-based representation

  9. Hierarchical Task Networks (HTNs): Example • Select Helicopter Launching Base • Select possible area (A) • Transport sec. force (F,A,H) • Embark sec. force (F,H) • Fly(H,A) • Disembark (F,H,A) • Position security force (F,A) • Transport fuel to (A) • ... Select Helicopter Launching Base alternative COAs Launch from Carrier Battle Group Establish ISB within Flying Distance Transport helicopters available (H) Transport helicopters available (H) Security force available (F) Helicopters have air refuel. capability (H) Complex tasks are decomposed into simpler ones

  10. Hierarchical Task Networks (HTNs) : Knowledge Artifacts Subtasks: Task: • Select possible area(A) Establish Base within Flying Distance • Transport sec. force (F,A,H) Conditions: Transport helicopters available (H) Position security force (F,A) Security force available (F)

  11. HTN t1 commit to • Objects mentioned in the tasks (e.g., resources) are terms defined in an ontology Ontology t11 t12 • Tasks in the HTN can be accomplished by other agents and/or by gathering information from other information sources. Objects used by these agents/information sources commit to their own ontologies t21 t22 commit to Ontology OntoPlan: Combine Hierarchical Task Networks and Ontologies • Hierarchical task networks (HTN) can be used to represent an on-going operation at different levels of abstraction

  12. Agent Planner KB HTN Plan Generator Semantic Web Mediator OntoPlan: Architecture for Knowledge Fusion System task executed plan Message decoder S1 S2 S3 HTN Ontologies

  13. Task: Subtasks: … • … S2 • … Conditions: commits to … … … Task-Oriented Knowledge Fusion Task: Classify a contact commits to Ontologies

  14. Goal-Oriented Knowledge Fusion (II) Task: Classify a contact HTN S2 S3

  15. Example Task: Classify contact inform command staff OntoPlan Message decoder query: previous enemy activity in the region msg: contact detected request: activate & scan J-2 Sensor Sensor Ontology

  16. Example (con’t) Task: inform troops in area about nature of contact OntoPlan Message decoder query: forces in the area msg: inform forces about contact query: forces in the area command

  17. Knowledge Filtering By Using LCW Statements • Use meta-level information about the information maintained by the information sources • Local completeness: the information source knows all information about a particular query. • Example: The US Embassy in Albonia may have complete information about the threat in that country: LCWTF(US_Tank(t) AND in-area(t,a)). • During HTN planning LCW information may be inferred “get all available M-113 armored vehicles available at the ISB”

  18. Example: Local Closed-Word Information OntoPlan query: previous activity in the region … Area J-2 Local J-2 command Ontology Ontology Ontology lcw(own activity, region) Ontology lcw(enemy activity, region)

  19. Semantic Web Mediator • A knowledge fusion system for the Semantic Web • contains a knowledge base with meta information • completeness information • relevance information • Selects information sources and processes the query • checks its Kb to find sources that have completeness information • if found - selects and queries that source • if not checks its KB to find sources that have relevant information • if found - selects and queries those sources • Can perform ontology-based query translation when needed

  20. Semantic Web Knowledge Fusion Ontologies SW Wrapper Intel Ont Threat Ont Intel Information Analysis extends commits to SW Wrapper Sensor Ont Location Ont Information extraction commits to extends SW Wrapper Monitoring NOAA NOAA Ont Weather Ont commits to extends

  21. Dealing with Dynamic Environments • Various sources: • Data feed • New events (e.g., received data from a previously unavailable sensors) • Is the outcome invalid? • Should the agent start the whole process from the scratch? • How to “safe” some effort but still guarantee accuracy of information extracted?

  22. Problem: Determine Effects of Changes Changed? Task: Classify a contact inform command staff HTN ? Changed! ? ? S2 ? ? ? S3

  23. Idea: Build Structure Maintaining Dependencies Task: Classify a contact inform command staff HTN Dependency Graph

  24. Propagating changes Task: Classify a contact inform command staff HTN Dependency Graph

  25. Propagation Mechanism • Based on the ideas Redux for Constrained Decision Revision (Petrie, 1992) • Annotates all decisions made in a dependency graph • A 1-to-1 map can be made between HTNs and the dependency graph (Xu & Muñoz-Avila, 2004)

  26. Planned Evaluation:Empirical • Testbed: • Create several information sources • Sources commit to their own OWL ontologies • Sources contain HTN knowledge artifacts (represented in OWL) about tasks they can solved • Measures: • The time required by OntoPlan to complete tasks • Size of the remote data accessed • The ratio of the information gathering actions over the total number of actions in the resulting plans

  27. Planned Evaluation:Theoretical • Conditions for soundness • Conditions for completeness • Complexity • Expected reduction in size of the search space.

  28. Final remarks • We propose to build a system, OntoPlan, that exhibit the following capabilities: • Goal-Oriented Knowledge Fusion. Mechanisms for reasoning on the relationship between the information-gathering search and the information gathering tasks being solved • Heterogeneity. Allow heterogeneous data sources to commit to OWL ontologies. The content of the sources themselves will be described using OWL. • Knowledge Filtering. We also propose the use of meta-level information to control search. • Dynamic repair. Use of dependency maintenance techniques to avoid starting process from the scratch when changes occur • We built a prototype

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