Ontoplan knowledge fusion using semantic web ontologies
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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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Ontoplan knowledge fusion using semantic web ontologies

OntoPlan: Knowledge Fusion Using Semantic Web Ontologies

Héctor Muñoz-Avila

Jeff Heflin


Overview

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

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


Challenges

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

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


Ontoplan knowledge fusion using semantic web ontologies

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


Owl inference

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


Hierarchical task networks htns motivation

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


Hierarchical task networks htns example

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


Hierarchical task networks htns knowledge artifacts

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)


Ontoplan combine hierarchical task networks and ontologies

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


Ontoplan architecture for knowledge fusion

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


Task oriented knowledge fusion

Task:

Subtasks:

S2

Conditions:

commits to

Task-Oriented Knowledge Fusion

Task: Classify a contact

commits to

Ontologies


Goal oriented knowledge fusion ii

Goal-Oriented Knowledge Fusion (II)

Task: Classify a contact

HTN

S2

S3


Example

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


Example con t

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


Knowledge filtering by using lcw statements

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

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)


Semantic web mediator

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

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


Dealing with dynamic environments

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

Problem: Determine Effects of Changes

Changed?

Task: Classify a contact

inform command staff

HTN

?

Changed!

?

?

S2

?

?

?

S3


Idea build structure maintaining dependencies

Idea: Build Structure Maintaining Dependencies

Task: Classify a contact

inform command staff

HTN

Dependency Graph


Propagating changes

Propagating changes

Task: Classify a contact

inform command staff

HTN

Dependency Graph


Propagation mechanism

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)


Planned evaluation empirical

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


Planned evaluation theoretical

Planned Evaluation:Theoretical

  • Conditions for soundness

  • Conditions for completeness

  • Complexity

  • Expected reduction in size of the search space.


Final remarks

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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