ontoplan: knowledge fusion using semantic web ontologies
Post on 30-Dec-2015
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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
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-2UGS
…
Low bandwidth
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
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
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>…
<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
Logistics DBs
OWL Inference
Bin Laden
<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>
Al Qaeda TerrorOrg
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
Hierarchical Task Networks (HTNs):Motivation
Tactical
StrategicTheater
CINC
JCS / NCAStrategicNational
JTFOperational
• Practical: Can be used to encode information extraction strategies
• Theoretical: Strictly more expressive than action-based representation
Hierarchical Task Networks (HTNs): Example
Complex tasks are decomposed into simpler ones
Launch from Carrier Battle
Group
Security force available (F)
Transport helicoptersavailable (H)
Establish ISB within Flying
Distance
alternativeCOAs
Select Helicopter Launching Base 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 helicoptersavailable (H)
Hierarchical Task Networks (HTNs) : Knowledge Artifacts
Security force available (F)
Establish Base within Flying
Distance
Transport helicoptersavailable (H)
Task:
Conditions:
Select possible area(A)
Subtasks:
Transport sec. force (F,A,H)
Position security force (F,A)
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
t11 t12
t1
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
t21 t22
Ontologycommit to
OntoPlan: Architecture for Knowledge Fusion
HTN S1 S2 S3
Ontologies
HTN PlanGenerator Semantic Web
Mediator
Agent Planner
KB
executed plan
task
System
Message decoder
Task-Oriented Knowledge Fusion
Task: Classify a contact
…
…
…
Task:
Conditions:
…
Subtasks:
…
…commits to
Ontologies
S2
commits to
Example
Task: Classify contact OntoPlanOntoPlan
msg: contact detected
Sensor Sensor J-2
Ontology
request: activate & scan
query: previous enemy activity in the region
Message decoder
inform command staff
Example (con’t)
OntoPlanOntoPlan
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
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”
Example: Local Closed-Word Information
OntoPlanOntoPlan
Area J-2
Ontology
query: previous activity in the region
Ontology
Local J-2
Ontology
…
Ontology
lcw(enemy activity, region)
command
lcw(own activity, region)
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
Semantic Web Knowledge Fusion
Intel
NOAA
SW Wrapper
SW Wrapper
SW Wrapper
Intel Ont
Sensor Ont
NOAA Ont Weather Ont
Threat Ont
Location Ont
commits to
commits to
commits to
extends
Ontologies
Information Analysis
Information extraction
Monitoring
extends
extends
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?
Problem: Determine Effects of Changes
Task: Classify a contact
S2
HTN
S3
inform command staff
Changed!
Changed?
? ? ?
??
?
Idea: Build Structure Maintaining Dependencies
Task: Classify a contact
HTN
inform command staff
Dependency Graph
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)
Task
Task
Task Task
Planned Evaluation:Empirical
• Testbed:Create several information sourcesSources 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
Planned Evaluation:Theoretical
• Conditions for soundness
• Conditions for completeness
• Complexity
• Expected reduction in size of the search space.
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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