Integration of Semantic Web Technologies

Dr David Mott, Dave Braines, Gareth Jones (IBM UK)

Dr David Mott, Dave Braines, Gareth Jones (IBM UK)

International Technology AllianceIn Network & Information Sciences

International Technology AllianceIn Network & Information Sciences

Context• Research Focus

– Collaborative problem solving across a network– Shared understanding between a team– How semantic web technology may use and integrate sources of information

• Hypothesis: shared understanding and collaboration facilitated by:– Standard set of shared concepts for building solutions (e.g. CPM)– ITA Controlled English for human expression of facts, rules and rationale– Rationale for showing how conclusions were arrived at– Argumentation for guiding how rationale is explored

• Demonstrate:– The planning of FIRE support, collaborating with Brigade Commander, exposing

hidden assumptions• “this problem is endemic in planning” LWS

– The planning of an NGO, integrating this planning information with public government information

Planning of FIRE Support

FIRES Planner

Brigade Planner

“I need fire support to cover my troops”

Plans must synchronise

Brigade Plan

Cross bridge, defeat, assuming peace talks fail Rationale for planStandard Set of Concepts

FIRES support requirement

FIRES receives problem (in CPM) Must supply Fire Support Sees rationale from the level up

FIRES must allocate resource

FIRES has 3 gun batteries Request to satisfy task fire_support Why must fire support start by 6?

To Task “fire_support”

Rationale for support latest start 6

Rationale shows reasoning We want users to see this!Graph of CE premises to conclusions

All rationale for latest start 6

Brigade and FIRES rationale Dependent on assumptionHuman and machine rationale

Plan fragment for latest start 6

Rationale mapped onto plan Alternative view for user Dependent on assumption

Fire support unachievable

A too small, B unavailable, C out of range Problem not solved But rationale for C Bty is ROUGH

Start by asking terrain ready reckoner

Travel time on LONG? Calculation (3 hrs) includes rationale Uses SemWeb technology (CPM)

Start to build rationale for “C Bty out of range”

FIRES constructs case for “C out of range” Why cant we get there? GUI or Controlled English

Construct full rationale

Analysis is complex Because BG1 using SHORT and LONG too slow How explain to BDE Cdr?

Divide into areas of reasoning

Abstract irrelevant detailNeed full detail for validation BUT need to summarise for Cdr

FIRES: key lines of reasoning

Main areas abstracted to single fact Full picture in 1 page Linked to detail if required

BDE Cdr: rebuts claim

BDE Cdr reviews Argues against doctrine by tactical imperative FIRES hidden assumption revealed

FIRES problem no longer unachievable

“Doctrine” Assumption unmade Knock on effects calculated C available to complete plan

NGO Planning Task

• To look after schools and other local services• To ensure that the schools are evacuated as

required for the current or future operation.

Source of data - data.gov.uk

UK Government initiative Publically available Uses Semantic Web RDF

(The previous planning map)

For reference, the planning map Geographic areas correspond (but colours not the same)

Demonstration …

Schools in the area

Map-based “Mashup” Obtain schools data from Web Overlay in area of operations

Overlay areas exported from plan

Previous Plan data “published” Overlay operational areas Uses Semantic Web RDF

An area has information based on assumptions

Area data from “published” plan Start and end times Plan rationale – the assumption

Need to Contact school

Schools in affected area Contact schools for evacuation Assumptions useful in decision making

Suppose the time is 2, and peace negotiations have

not yet broken down, might be worth waiting to see if peace is established

before evacuating

Suppose the time is 3.30, and peace negotiations

have not yet broken down, then probably need to

evacuate

Suppose peace negotiations have broken

down, then must evacuate

It is assumed that “peace negotiations broken down by 4”

NGO demonstration summary

• Semantic Web allows common representation of information and meaning– Ways to reference information– Ways to define common models

• Planning data made available in Semantic Web form:– private access– includes rationale and assumptions

• Existing social data (schools) available through pre-existing sources in Semantic Web form

• Easy to integrate these sources to provide new functionality:– What about road control? hospitals… weather …– Could be used within military too

• Achieves a shared understanding between the military and other organisations– within limits (e.g. security)

“CURIOUS” Demonstration Architecture

CPM(rules)

Brigade Plan (+Rationale)

FIRES Plan(+Rationale)

Map, terrain

BDE Planner

FIRES PlannerTerrain speed

the L118 Light Gun moves at 20 km on desertthe L118 Light Gun moves at 40 km on metalled

the L118 Light Gun moves at 10 km on woodland

SPAR

QL

endp

oint

“Mashup” applicationfor geographic social

effects

e.g. Hospitals, Schools

SPARQL endpoint

Mapping Data

Tasks, Areas, Rationale

OWL/RDF/CE

Engineer

NGO

Plan Visualiser

Plan Visualiser

Argumentation Visualiser

BDE Plan

Argumentation

FIRES Plan

Some Discoveries

Collaborative Problem Solving Model

basic logic and rationale

Agent, Assumption, ConceptualSpace, Container, Entailment, Inconsistency, PossibleWorld, Proposition, PropositionIndex, Quantity, ReasoningStep, Set, Triple, VarBinding, WorldState

general purpose ConceptualThing, Constraint, Synchronisation

temporal Precede, TemporalConstraint, TemporalEntity, TimeInterval, TimeLine, TimePoint

space Area, Elevation, Line, Point, SpatialConstraint, SpatialCoordinateSystem, SpatialEntity, SpatialIntersection, SpatialLocation, SpatialUnion

resources Resource, ResourceAllocated, ResourceCapability, ResourceConstraint, ResourceQuantity, ResourceSet

actions Activity, Effect, Precondition

collaborative problem solving

Choice Point, Collaboration, Commitment, Communication, ConstraintViolated, Decision, GoalSpecification, Influence, Issue, JointPersistentGoal, MutualGoal, Problem, Solution, Trust,

planning Allocation, Evaluation, EvaluationCriterion, InitialState, Plan, PlanTask, PlanTaskDescription, PlanTaskTemplate, PlanningProblem, PlanningProblemContext, ResourceCommitment, ResourceReq, TaskCommitment

A planning model should contain both the plan and the problem solving state

Statements the task 'Build Bridge' is achieved after the task 'Clear Road A‘ .the task ‘Build Bridge’ has 18 as the earliest start time.

Define Facts

Assumptions it is assumed by the agent A that the task ‘Build Bridge’ has 18 as earliest start time

Explore Hypotheses

Uncertainty It is true to degree A2 that the area a3 is a woodland terrain. Express Uncertainty

Logical Relations if ( the task T1 has the value X as earliest start time ) and( the task T1 is achieved after the task T )then( the task T has the value X as earliest completion time ) .

Capture logical connections between things, and use these to infer new information from existing data

Query for which task T is it true that the task T has the agent joe as executor Query for information

Rationale the task 'Clear Road A' has 18 as earliest completion time because the task 'Build Bridge' is achieved after the task 'Clear Road A' and the task 'Build Bridge' has 18 as earliest start time.

Explain reasoning, capture dependencies

New Concepts conceptualisethe task T ~ is achieved after ~ the task T1 .

conceptualisea ~ task ~ T that has the value V as ~ earliest start time ~ .

Create new models of things

Argumentation! in the argument arg1, by stating “she always lies” the agent fred disputes the claim that “helen told us that all feedback is good”

Analyse by challenging

ITA Controlled English

Controlled English is “curiously useful” for human and machine communication

Writing Controlled English

it is true that "the enemy is on the other side of the bridge".

there is an artillery unit named 'C Battery' that is a company and is a US unit and has friendly as affiliation .

there is a resource pool named 'C Bty Guns' that has '18' as quantity .

the unit 'FIRES' has OPCOM of the artillery unit 'C Battery' .

there is a plan named 'BDE Plan' that has the agent 'BDE' as executor and contains the objective 'Bridge Crossed' and contains the objective 'Deploy BG1' and contains the objective 'Enemy destroyed' and contains the task cross_bridge and contains the task destroy_enemy and contains the task move_to_oa .

the resource request rr0 is required by the task 'Advance to OA Rome' .

the task destroy_enemy occurs after the task cross_bridge .

the agent 'FIRES' states that the resource allocation constraint rac2 constrains the task fire_support and prohibits the resource 'C Bty Guns' because "C Bty out of range".

the agent 'BDE Cdr' states that the task destroy_enemy occurs after the task cross_bridge because the task cross_bridge realises the objective 'Bridge Crossed' and the objective 'Bridge Crossed' enables the task destroy_enemy.

the agent 'BDE Cdr' states that the task cross_bridge occurs simultaneously with the task fire_support because the task fire_support realises the objective 'Crossing Supported' and the objective 'Crossing Supported' supports the task cross_bridge.

the task destroy_enemy has 11 as latest completion time because the objective 'Enemy destroyed' has 11 as latest completion time and the task destroy_enemy realises the objective 'Enemy destroyed'.

the agent terrainRR states that the minimum path transit time 'mil:L118_LightGun_on_LONG4' has '3.08557' as minimum because the land route 'LONG' has unmetalled as classification and the maximum terrain speed ru3 has 10 as speed and the maximum terrain speed ru3 has unmetalled as terrain and the maximum terrain speed ru3 has 'mil:L118_LightGun' as resource and the land route 'LONG' has '30.8557' as length.

it is true that "the enemy is on the other side of the bridge".

Handwritten Domain Application

Editors

There are many ways to make writing CE easier, but CE should be readable by itself

the L118 Light Gun moves at 20 km on desert

Language “Extensions”

Hybrid Rationale

the agent FIRES states that "route SHORT is not available between 4-6" because "BG1 using SHORT between 0-12" and “C Bty and BG1 cannot use SHORT simultaneously".

[if ( the temporal entity T has the value X as earliest completion time ) and( the temporal entity T1 occurs after the temporal entity T ) then( the temporal entity T1 has the value X as earliest start time ).

Argumentation “Patterns”

Domain Application

Automated Reasoning

Handwritten User Rationale

Rationale must be integrated between human and machine to facilitate shared reasoning

RATIONALE

Logical Mappings between languages

Common Logic

ITA CE

RDF/S/OWL

RIF-FLD

Representations for different purposes must share a common semantics

MODELSConcepts

Logic RulesEvents

RationaleExplanation

DependenciesAssumptions

CollaborativeReasoning

ApplicationsHybrid user and machine

Domain specific

“Shared Understanding”Visualisation of Logic

Controlled English

The “CURIOUS” Reasoning Infrastructure

Integration of common concepts, CE, rationale

and logic will help facilitate shared understanding in

collaborative operations

BACKUP

Controlled Natural Language

A Controlled Natural Language is a human readable subset of English (or other natural language) that can also be machine parsed

understandable by machine and human Improves “impedance matching” between human and agent as both can

use the same language

Needs: A syntax (grammar) A lexicon (set of words and their grammatical roles) A semantics (things and relationships in the world) A mapping from syntax/lexicon to semantics (how does a word refer to a

thing?)

A CNL is easy to read, but harder to write

Different languages used by researchers: Rabbit, ACE, Controlled English

Controlled English Extensions But CE can be “stilted”, users want more natural expressivity We are exploring an extension mechanism

User-defined Linguistic transformation rule

“More Natural” CE

Basic CE .

the person fred attended the meeting finance1 with the person joe

the person fred attended the meeting finance1 and the person joe attended the meeting finance1

the Mk1Tank only fires the L15 round.

if ( the Mk1 Tank X fires the thing Y ) then ( the thing Y is an L15 round ) .

Examples

Definition of “only”

Anecdotal feedback on use of CE – Good Things• Non logical users can create models

Non-technical analyst SME could construct model on their own “As non formal logician, I can more easily construct models and instance data in CE”

• Improve Communication User requested a description of a planning scenario “in English”; the CE version satisfied their

request Use of text-based CE easily supported by Wikis, allowing easy communal sharing of CE models

and instances• Assists Design

“Concepts and rules are closer to my way of thinking and are easier to understand” Designing how to say something helped to clarify what the concepts really mean

• Common Language Rationale graph derived from human and agent reasoning can be seen as one due to use of

common language “Can combine queries of different information from totally different domains – “its all the same

language”

Anecdotal feedback on use of CE – need for improvement

Greater expressivity of syntax Multi-part relations

Greater expressivity of semantics Sets, embedded “Forall”

CE “intellisense” editors Context-sensitive words

“he”, “that”

Still experimental, BUT “Curiously useful”

ALL information must be represented in CEAny new CE syntax must make senseEven if not executing rules, still define the reasoning in CE

All information in one place in one formatDesigning syntax clarified understanding of semantics

Design Principles

Rationale may use structured or unstructured facts

• Rationale is defined in Controlled English– SENTENCE because SENTENCE– May contain structured facts and/or unstructured text

• Structured facts can match logical rules allowing further inferences– the person Fred is married to the person Jane because the person

Jane is married to the person Fred.• Unstructured text can represent information impossible to

capture in the model but cannot be used to match rules and generate new inferences– “I know Fred loves Jane” because “Jane told my brother”.

Why Rationale?

• Sharing of rationale enables team understanding of a solution (we hope)

• Human and machine reasoning may be integrated• Can be used to determine dependencies,

assumptions, knock on effects• Applications may generate rationale

automatically via the common conceptual model• BUT a standard to exchange for rationale is

required– The ITA “logic proposal” offers such a standard

Argumentation

Argumentation extends rationale to support informal reasoning

– Patterns of challenge and response• Why did you say that?• Your fact is wrong• Your reasoning is wrong

– Used to explore a problem when humans are uncertain – Can expose hidden assumptions and incorrect reasoning– Could be used to develop new concepts?

• Trying to argue may suggest missing properties or wrong conceptualisations

– Several Theories of argumentation

Argument

Claim A: we got good feedback

Response

Challenge

Justification QueryB: How do you know that?

Justification

Subargument

Claim A: Helen just said

all feedback was good

Subresponse

Challenge

Justification QueryB: You think that client

was nice to us?

Justification

Subargument

Claim A: If all feedback good then he didn’t write anything bad

Subresponse

Undercutting defeater

Subargument

Claim B: Maybe there

was NO feedback

Subresponse

Rebutting defeater

Subargument

Claim A: Helen couldnt have said

all feedback good

Subresponse

Rebutting defeater

Subargument

Claim B: No. The only situation she

couldn’t say it would be feedback that was bad

Feedback

Good

Rebutting defeater

Subargument

Claim A: Helen talking about “all

feedback received” implies its existence

Subresponse

AccepterA: OK

Subresponse

Rebutting defeater

Subargument

Claim B: Maybe she was being ironic

, “the best I can say is…

Subresponse

Rebutting defeater

Subargument

Claim A: No Helen is never ironic

Subresponse

AccepterB: OK well done

Subresponse

Using Lance J Rips Notation

Got good feedback

H says all feedback good

No bad feedback

Some feedback must existH is not ironic

NO feedback

No, according to logic all X is Y is true even if there is no X

H is ironic If you mention something

it must exist

Surely, if there is no X then you

cant say all X is Y

Incompatible

Incompatible

ARG2

Undercut (via alternative)

ARG3ARG1

ARG5 (logic)

ARG6 (linguistic)

ARG7

ARG8

Argument structures

rebut

rebut

ARG4

expand

Undercut (via alternative)

rebut

Undercut

Argumentation – Rebut Claim

• User clicks on rationale graph to add “Rebut Claim”• Argumentation CE generated in orange, and the corresponding rationale in blue

–Attempting to construct semantics of argumentation via:

Working with CUNY to

explore this idea

Argumentation CE Rationale CE

BDE Cdr rebuts claim

BDE Cdr reviews Argues against doctrine by tactical imperative Hidden assumption revealed