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? J-2 UGS … Low bandwidth

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 M1A1 Abrams 41.5 … … imports Weapons Ontology Logistics DBs

7. OWL Inference Bin Laden Al QaedaTerrorOrg Terrorist type head type 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 Main point: the various sources may be heterogeneous

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

9. Hierarchical Task Networks (HTNs): Example Complex tasks are decomposed into simpler ones Launch from Carrier Battle Group Security force available (F) Transport helicopters available (H) Establish ISB within Flying Distance alternative COAs 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)... Helicopters have air refuel. capability (H) Transport helicopters available (H)

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

11. 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 t 11 t 12 t 1 HTN Objects mentioned in the tasks (e.g., resources) are terms defined in an ontology Ontology commit to 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 t 21 t 22 Ontology commit to

12. OntoPlan: Architecture for Knowledge Fusion HTN S1S2S3 Ontologies HTN Plan Generator Semantic Web Mediator Agent Planner KB executed plan task System Message decoder

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

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

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

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

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: LCW TF (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 Area J-2 Ontology query: previous activity in the region Ontology Local J-2 Ontology … lcw(enemy activity, region) command lcw(own 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 Intel NOAA SW Wrapper Intel Ont Sensor Ont NOAA OntWeather Ont Threat Ont Location Ont commits to extends Ontologies Information Analysis Information extraction Monitoring 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 Task: Classify a contact S2 HTN S3 inform command staff Changed! Changed? ? ?? ?? ?

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

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