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Integration of Semantic Web Technologies Dr David Mott, Dave Braines, Gareth Jones (IBM UK) International Technology Alliance In Network & Information.

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Presentation on theme: "Integration of Semantic Web Technologies Dr David Mott, Dave Braines, Gareth Jones (IBM UK) International Technology Alliance In Network & Information."— Presentation transcript:

1 Integration of Semantic Web Technologies Dr David Mott, Dave Braines, Gareth Jones (IBM UK) International Technology Alliance In Network & Information Sciences International Technology Alliance In Network & Information Sciences

2 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

3 Planning of FIRE Support FIRES Planner Brigade Planner I need fire support to cover my troops Plans must synchronise

4 Brigade Plan Cross bridge, defeat, assuming peace talks failRationale for planStandard Set of Concepts

5 FIRES support requirement FIRES receives problem (in CPM)Must supply Fire SupportSees rationale from the level up

6 FIRES must allocate resource FIRES has 3 gun batteries Request to satisfy task fire_supportWhy must fire support start by 6? To Task fire_support

7 Rationale for support latest start 6 Rationale shows reasoningWe want users to see this!Graph of CE premises to conclusions

8 All rationale for latest start 6 Brigade and FIRES rationaleDependent on assumptionHuman and machine rationale

9 Plan fragment for latest start 6 Rationale mapped onto plan Alternative view for userDependent on assumption

10 Fire support unachievable A too small, B unavailable, C out of rangeProblem not solved But rationale for C Bty is ROUGH

11 Start by asking terrain ready reckoner Travel time on LONG?Calculation (3 hrs) includes rationaleUses SemWeb technology (CPM)

12 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

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

14 Divide into areas of reasoning Abstract irrelevant detailNeed full detail for validationBUT need to summarise for Cdr

15 FIRES: key lines of reasoning Main areas abstracted to single factFull picture in 1 pageLinked to detail if required

16 BDE Cdr: rebuts claim BDE Cdr reviewsArgues against doctrine by tactical imperativeFIRES hidden assumption revealed

17 FIRES problem no longer unachievable Doctrine Assumption unmadeKnock on effects calculatedC available to complete plan

18 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.

19 Source of data - UK Government initiativePublically availableUses Semantic Web RDF

20 (The previous planning map) For reference, the planning mapGeographic areas correspond(but colours not the same)

21 Demonstration …

22 Schools in the area Map-based MashupObtain schools data from WebOverlay in area of operations

23 Overlay areas exported from plan Previous Plan data publishedOverlay operational areasUses Semantic Web RDF

24 An area has information based on assumptions Area data from published planStart and end timesPlan rationale – the assumption

25 Need to Contact school Schools in affected area Contact schools for evacuationAssumptions 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

26 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)

27 CURIOUS Demonstration Architecture CPM (rules) Brigade Plan (+Rationale) FIRES Plan (+Rationale) Map, terrain BDE Planner FIRES Planner Terrain speed the L118 Light Gun moves at 20 km on desert the L118 Light Gun moves at 40 km on metalled the L118 Light Gun moves at 10 km on woodland SPARQL endpoint Mashup application for geographic social effects e.g. Hospitals, Schools SPARQL endpoint Mapping Data Tasks, Areas, Rationale OWL/RDF/CE Engineer NGO Plan Visualiser Argumentation Visualiser BDE Plan Argumentation FIRES Plan

28 Some Discoveries

29 Collaborative Problem Solving Model basic logic and rationale Agent, Assumption, ConceptualSpace, Container, Entailment, Inconsistency, PossibleWorld, Proposition, PropositionIndex, Quantity, ReasoningStep, Set, Triple, VarBinding, WorldState general purposeConceptualThing, Constraint, Synchronisation temporalPrecede, TemporalConstraint, TemporalEntity, TimeInterval, TimeLine, TimePoint spaceArea, Elevation, Line, Point, SpatialConstraint, SpatialCoordinateSystem, SpatialEntity, SpatialIntersection, SpatialLocation, SpatialUnion resourcesResource, ResourceAllocated, ResourceCapability, ResourceConstraint, ResourceQuantity, ResourceSet actionsActivity, Effect, Precondition collaborative problem solving Choice Point, Collaboration, Commitment, Communication, ConstraintViolated, Decision, GoalSpecification, Influence, Issue, JointPersistentGoal, MutualGoal, Problem, Solution, Trust, planningAllocation, 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

30 Statementsthe task 'Build Bridge' is achieved after the task 'Clear Road A. the task Build Bridge has 18 as the earliest start time. Define Facts Assumptionsit is assumed by the agent A that the task Build Bridge has 18 as earliest start time Explore Hypotheses UncertaintyIt is true to degree A2 that the area a3 is a woodland terrain.Express Uncertainty Logical Relationsif ( 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 Queryfor which task T is it true that the task T has the agent joe as executorQuery for information Rationalethe 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 Conceptsconceptualise the task T ~ is achieved after ~ the task T1. conceptualise a ~ 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

31 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". HandwrittenDomain 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

32 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

33 RATIONALE Logical Mappings between languages Common Logic ITA CE RDF/S/OWL RIF-FLD Representations for different purposes must share a common semantics

34 MODELS Concepts Logic Rules Events Rationale Explanation Dependencies Assumptions Collaborative Reasoning Applications Hybrid 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


36 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

37 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

38 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

39 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 CE Any new CE syntax must make sense Even if not executing rules, still define the reasoning in CE All information in one place in one format Designing syntax clarified understanding of semantics Design Principles

40 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.

41 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

42 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

43 Argument Claim A: we got good feedback Response Challenge Justification Query B: How do you know that? Justification Subargument Claim A: Helen just said all feedback was good Subresponse Challenge Justification Query B: You think that client was nice to us? Justification Subargument Claim A: If all feedback good then he didnt 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 couldnt 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 Accepter A: 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 Accepter B: OK well done Subresponse Using Lance J Rips Notation

44 Got good feedback H says all feedback good No bad feedback Some feedback must exist H 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 ARG2 Undercut (via alternative) ARG3 ARG1 ARG5 (logic) ARG6 (linguistic) ARG7 ARG8 Argument structures rebut ARG4 expand Undercut (via alternative) rebut Undercut

45 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 CERationale CE

46 BDE Cdr rebuts claim BDE Cdr reviewsArgues against doctrine by tactical imperativeHidden assumption revealed

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