Integration of Semantic Web Technologies

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

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 Brigade Planner Plans must synchronise “I need fire support to cover my troops” FIRES Planner

4. Brigade Plan Cross bridge, defeat, assuming peace talks fail Standard Set of Concepts Rationale for plan

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

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

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

8. All rationale for latest start 6 Brigade and FIRES rationale Human and machine rationale Dependent on assumption

9. Plan fragment for latest start 6 Alternative view for user Dependent on assumption Rationale mapped onto plan

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

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

12. Start to build rationale for “C Bty out of range” Whycant we get there? FIRES constructs case for “C out of range” GUI or Controlled English

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

14. Divide into areas of reasoning Need full detail for validation BUT need to summarise for Cdr Abstract irrelevant detail

15. FIRES: key lines of reasoning Full picture in 1 page Main areas abstracted to single fact Linked to detail if required

16. BDE Cdr: rebuts claim BDE Cdr reviews Argues against doctrine by tactical imperative FIRES hidden assumption revealed

17. FIRES problem no longer unachievable “Doctrine” Assumption unmade Knock on effects calculated C 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 - data.gov.uk UK Government initiative Publically available Uses Semantic Web RDF

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

21. Demonstration …

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

23. Overlay areas exported from plan Previous Plan data “published” Overlay operational areas Uses Semantic Web RDF

24. An area has information based on assumptions Area data from “published” plan Start and end times Plan rationale – the assumption

25. Need to Contact school It is assumed that “peace negotiations broken down by 4” Suppose the time is 3.30, and peace negotiations have not yet broken down, then probably need to evacuate 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 peace negotiations have broken down, then must evacuate Schools in affected area Contact schools for evacuation Assumptions useful in decision making

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 OWL/RDF/CE CPM (rules) Plan Visualiser “Mashup” application for geographicsocial effects Tasks, Areas, Rationale BDE Planner BDE Plan Brigade Plan (+Rationale) NGO Argumentation Visualiser Map, terrain SPARQL endpoint Mapping Data Argumentation FIRES Plan (+Rationale) Plan Visualiser Terrain speed FIRES Planner FIRES Plan 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 e.g. Hospitals, Schools Engineer

28. Some Discoveries

29. Collaborative Problem Solving Model A planning model should contain both the plan and the problem solving state

30. 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". Handwritten Domain Application 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. Editors Language “Extensions” the L118 Light Gun moves at 20 km on desert There are many ways to make writing CE easier, but CE should be readable by itself

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". Handwritten User Rationale Domain Application [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 ) . Automated Reasoning Argumentation “Patterns” Rationale must be integrated between human and machine to facilitate shared reasoning

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

34. The “CURIOUS” Reasoning Infrastructure MODELS Concepts Logic Rules Events Integration of common concepts, CE, rationale and logic will help facilitate shared understanding in collaborative operations “Shared Understanding” Visualisation of Logic Controlled English Collaborative Reasoning Applications Hybrid user and machine Domain specific Rationale Explanation Dependencies Assumptions

35. BACKUP

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 “More Natural” CE Basic CE . User-defined Linguistic transformation rule Examples the person fred attended the meeting finance1 and the person joe attended the meeting finance1 the person fred attended the meeting finance1 with the person joe if ( the Mk1 Tank X fires the thing Y ) then ( the thing Y is an L15 round ) . the Mk1Tank only fires the L15 round. 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” Design Principles All information in one place in one format Designing syntax clarified understanding of semantics 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

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 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 Argumentation

43. Argument Response Claim A: we got good feedback Challenge Justification Query B: How do you know that? Justification Subargument Subresponse Claim A: Helen just said all feedback was good Challenge Justification Justification Query B: You think that client was nice to us? Subargument Subresponse Claim A: If all feedback good then he didn’t write anything bad Undercutting defeater Subargument Subresponse Claim B: Maybe there was NO feedback Subresponse Rebutting defeater Rebutting defeater Subargument Subargument Subresponse Claim A: Helen couldnt have said all feedback good Subresponse Claim A: Helen talking about “all feedback received” implies its existence Rebutting defeater Rebutting defeater Subargument Subargument Claim B: No. The only situation she couldn’t say it would be feedback that was bad Subresponse Claim B: Maybe she was being ironic , “the best I can say is… Subresponse Rebutting defeater Accepter A: OK Subargument Feedback Good Subresponse Claim A: No Helen is never ironic Accepter B: OK well done Using Lance J Rips Notation

44. Argument structures Undercut (via alternative) ARG2 rebut Got good feedback ARG3 ARG1 expand No bad feedback NO feedback ARG4 H says all feedback good Surely, if there is no X then you cant say all X is Y rebut ARG7 Undercut If you mention something it must exist H is ironic ARG5 (logic) No, according to logic all X is Y is true even if there is no X Incompatible ARG8 H is not ironic Incompatible Some feedback must exist rebut ARG6 (linguistic) Undercut (via alternative)

45. Argumentation – Rebut Claim Working with CUNY to explore this idea • 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: Argumentation CE Rationale CE

46. BDE Cdr rebuts claim BDE Cdr reviews Argues against doctrine by tactical imperative Hidden assumption revealed