ArgQL: A Declarative Language for Querying Argumentative Dialogues

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1 ArgQL: A Declarative Language for Querying Argumentative Dialogues R U L E M L + R R : I N T E R N AT I O N A L J O I N T C O N F E R E N C E O N R U L E S A N D R E A S O N I N G D I M I T R A Z O G R A F I S T O U, G I O R G O S F L O U R I S, D I M I T R I S P L E X O U S A K I S F O U N D AT I O N F O R R E S E A R C H A N D T E C H N O L O G Y ( F O R T H ) J U LY FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 1

2 The big picture The recent growth of the social side of the web offers new incentive for users to: Easily upload digital content Communicate with each other Express themselves Outcome: online communities populated with tons of unstructured and unexploited data about: Opinions and beliefs about political or social topics Criticisms and consultations and Reviews on products, services etc. Objective: Development of methods to make data, created from users participation in online communities, machine interpretable, retrievable and computable. FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 2

3 Role of Computational Argumentation Branch of Artificial Intelligence which studies the process of human reasoning while arguing and debating. Theoretical and computational models addressing miscellaneous issues like: The process of resolving controversial positions through reasoning Mental factors that affect that process like preferences, intents, beliefs, desires, trust, etc. Evaluation of debates Persuasiveness and impact of arguments Basic component is the argument. User created dialogues as an application field for argumentation models FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 3

4 Problem description Need for mechanisms for navigation and information identification and extraction from a graph of interconnected arguments ArgQL: a declarative query language designed on a data model for argumentation Why is it important? Focus on understanding the information requirements when extracting information from dialogues. Familiar terminology to the community of argumentation Why not using traditional query languages like SPARQL? We can use traditional languages for our purpose but Argumentation defines distinct and solid concepts different than the triples of RDF e.g. Generates much simpler and descriptive queries for its purpose. FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 4

5 Similar approaches No similar approach to a query language for arguments Significant efforts towards the realization of a web of opinions A number of tools that facilitate the participation in online debates. Argument mining from text (Natural Language Processing) Visualization (e.g. Debate Graph, Argunet, Opinion Space etc.) Reasoning (e.g. Parmenides, Avicenna, etc. ) User engagement (CreateDebate, Debate.org, etc.) Storage and Searching engines (AIFdb, ArgDF, DourcourseDB etc.) AIF (Argument Interchange Format). Interlingua that bridges arguments among the tools. None of those tools defines a pure language for argumentation Application fields for traditional languages like SQL, SPARQL. FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 5

6 Argumentation data model (1/3) P : infinite set of propositions P P: contrariness relation p 1, p 2 : p 1 in conflict with p 2 P P: equivalence relation p 1, p 2 : p 1 equivalent to p 2 L = P,,, : Inference as commonly used a, b, c d : propositions a, b, c imply proposition d FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 6

7 Argumentation data model (2/3) Arguments: A = pr, c where pr P, c P, pr c Attack Relations: Support c 1 c 2 c 1 c 2 Rebut Endorse { p 1 p 2 p n } { s 1 s 2 s m } { p 1 p 2 p n } { s 1 s 2 s m } c 1 c 2 c 1 c 2 : equivalence : conflict Undercut { p 1 p 2 p n } { s 1 s 2 s m } Backing { p 1 p 2 p n } { s 1 s 2 s m } FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 7

8 Argumentation data model (3/3) Debate graph : D = (A, R) A : set of arguments R A A : set of relations < { prop 1, prop 2 }, concl 1 > < { prop 3 }, concl 2 > Data < { prop 1, prop 2 }, concl 1 > <{ prop 3 }, concl 2 > <{ prop 4 }, concl 3 > <{ prop 5, prop 6 }, concl 4 > concl 1 in_conflict concl 2 concl 3 in_conflict prop 3 concl 4 equivalent concl 1 Attacks/Undercut < { prop 4 }, concl 3 > <{ prop 5, prop 6 }, concl 4 > FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 8

9 Query examples 1. Find arguments which "defend (attack their attackers) all arguments with conclusion concl 1 ". Data match?arg (attack/attack)+ <?pr, "concl 1 "> return?arg <{ prop 1, prop 2 }, concl 1 > Results: <{ prop 4 }, concl 3 > 2. Find arguments which support an argument with the proposition prop 2 in its premise set. match?arg support <?pr[/{ prop2 }],?c> return?arg <{ prop 3 }, concl 2 > Attacks/Undercut <{ prop 4 }, concl 3 > <{ prop 5, prop 6 }, concl 4 > Results: <{ prop 5, prop 6 }, concl 4 > FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 9

10 ArgQL Syntax V: an infinite set of variables Start with the character? (e.g.?x) Values for propositions are quoted (e.g. prop i ) Language expressions combined by the two basic constructs: Argument patterns: expressions used to match arguments in the debate graph Path patterns: expressions used to match sequences of arguments in the debate graph FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 10

11 ArgQL Syntax Argument patterns A single variable, or be of the form: pr V a variable that matches the premise set c P V a variable or proposition value that matches with the conclusion. the part [filters] is optional and adds constraints on the premise set Given that p i < pr [filters], c > P, the supported filters are: Inclusion: [/{p 1,, p n }] propositions p i must be included in pr. Join: [.?x] or [.{p 1,, p n }] pr must be joint with the given set. Disjoin: [!.?x] or [!.{p 1,, p n }] pr must be disjoint with the given set Equality: [=?x] or [= {p 1,, p n} ] pr must have exactly the same elements with the given set FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 11

12 ArgQL Syntax Path patterns Keywords for relations: rebut, undercut, attack, endorse, back, support Symbol for relation sequence: / E.g. rebut/endorse/attack Numerical indicators + : one or more occurrences of the path e.g. (attack/support)+ *n :exactly n occurrences of the path e.g. (endorse/undercut)*3 Arbitrary combinations to support many complex types of path patterns E.g. ((attack/support)*2)/rebut, ((rebut/undercut)*2/(support/attack)*3)+ Issues to be addressed at performance evaluation stage: Complexity of + Cycles on the paths FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 12

13 ArgQL Syntax BNF grammar for the syntax of ArgQL FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 13

14 Semantics description Semantics are defined above a function μ: S V S, where S = P 2 P A A1: <{ p 1, p 2 }, c 1 > A2: <{ p3 }, c2 > Undercut A3: <{ p4, p2 }, c3 > Endorse A4: <{ p5 }, c4 > A7: <{ p7, p8 }, c6 > Rebut A8: <{ p9 }, c7 > A5: <{ p6 }, c5 > Query: Match?arg (attack/support)+ <?pr, c1 > return?arg FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 14

15 Semantics description Semantics are defined above a function μ: S V S, where S = P 2 P A A1: <{ p 1, p 2 }, c 1 > A2: <{ p3 }, c2 > Undercut A3: <{ p4, p2 }, c3 > Endorse A4: <{ p5 }, c4 > A7: <{ p7, p8 }, c6 > Rebut A8: <{ p9 }, c7 > A5: <{ p6 }, c5 >?arg μ { p1, p2,, c7, { p1, p2 }, { p1,p3 }., Α1, Α2,..Α8} Query: Match?arg (attack/support)+ <?pr, c1 > return?arg FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 15

16 Semantics description Semantics are defined above a function μ: S V S, where S = P 2 P A A1: <{ p 1, p 2 }, c 1 > A2: <{ p3 }, c2 > Undercut A3: <{ p4, p2 }, c3 > Endorse A4: <{ p5 }, c4 > A7: <{ p7, p8 }, c6 > Rebut A8: <{ p9 }, c7 > A5: <{ p6 }, c5 > μ <?pr, c1 > <μ(?pr), μ( c1 )> = A1: <{ p1, p2 }, c1> Query: Match?arg (attack/support)+ <?pr, c1 > return?arg FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 16

17 Semantics description Semantics are defined above a function μ: S V S, where S = P 2 P A A1: <{ p 1, p 2 }, c 1 > A2: <{ p3 }, c2 > Undercut A3: <{ p4, p2 }, c3 > Endorse A4: <{ p5 }, c4 > A7: <{ p7, p8 }, c6 > Rebut A8: <{ p9 }, c7 > A5: <{ p6 }, c5 > (attack/support)+ = {attack/support, attack/support/attack/support, attack/support/attack/support/attack/support.} Query: Match?arg (attack/support)+ <?pr, c1 > return?arg FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 17

18 Semantics description Semantics are defined above a function μ: S V S, where S = P 2 P A A1: <{ p 1, p 2 }, c 1 > A2: <{ p3 }, c2 > Undercut A3: <{ p4, p2 }, c3 > Endorse A4: <{ p5 }, c4 > A7: <{ p7, p8 }, c6 > Rebut A8: <{ p9 }, c7 > A5: <{ p6 }, c5 > Expression evaluation in D = Query: Match?arg (attack/support)+ <?pr, c1 > return?arg FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 18

19 Semantics description Semantics are defined above a function μ: S V S, where S = P 2 P A A1: <{ p1, p2 }, c1 > A2: <{ p3 }, c2 > Undercut A3: <{ p4, p2 }, c3 > Endorse A4: <{ p5 }, c4 > A7: <{ p7, p8 }, c6 > Rebut A8: <{ p9 }, c7 > A5: <{ p6 }, c5 > Evaluation of expression in D = {A3 attack/support A1, Query: Match?arg (attack/support)+ <?pr, c1 > return?arg FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 19

20 Semantics description Semantics are defined above a function μ: S V S, where S = P 2 P A A1: <{ p1, p2 }, c1 > A2: <{ p3 }, c2 > Undercut A3: <{ p4, p2 }, c3 > Endorse A4: <{ p5 }, c4 > A7: <{ p7, p8 }, c6 > Rebut A8: <{ p9 }, c7 > A5: <{ p6 }, c5 > Evaluation of expression in D = {A3 attack/support A1, A5 attack/support/attack/support A1} Query: Match?arg (attack/support)+ <?pr, c1 > return?arg FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 20

21 Semantics description Semantics are defined above a function μ: S V S, where S = P 2 P A A1: <{ p1, p2 }, c1 > A2: <{ p3 }, c2 > Undercut A3: <{ p4, p2 }, c3 > Endorse A4: <{ p5 }, c4 > A7: <{ p7, p8 }, c6 > Rebut A8: <{ p9 }, c7 > A5: <{ p6 }, c5 > Evaluation of expression in D = {A3 attack/support A1, A5 attack/support/attack/support A1} Query Answer:?arg = {A3, A5} Query: Match?arg (attack/support)+ <?pr, c1 > return?arg FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 21

22 Query execution Translation into standard and well-optimized storage schemes Independence from the storage scheme. RDF and AIF scheme (target field is the Web, exploit SPARQL1.1 property paths) Definition of two translation functions: tra: translates Argumentation data model into RDF instances of AIF ontology trq: translates ArgQL queries into SPARQL Formally prove that translation is semantics preserving Argument Database D = (A, R) tra RDF Database AIF ontology QArgQL trq QSPARQL FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 22

23 Conclusions o Initial results from the definition of ArgQL, a declarative query language for querying dialogues constructed of arguments. Syntax and semantics o Future directions Complete the proof of correctness for translation Experimentation on real datasets and development of an endpoint to run queries Data model enrichment with more complicated concepts (e.g. topics) and expand the syntax with the new concepts. Integration with reasoning mechanisms for dynamic computations (e.g. Dung s acceptability) Smart searching facilities (like keyword searching, imprecise textual mappings etc.) Expressive power FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 23

24 Questions? FOUNDATION FOR RESEARCH AND TECHNOLOGY - FORTH 24

Agent-Based Systems. Agent-Based Systems. Michael Rovatsos. Lecture 13 Argumentation in Multiagent Systems 1 / 18

Agent-Based Systems. Agent-Based Systems. Michael Rovatsos. Lecture 13 Argumentation in Multiagent Systems 1 / 18 Agent-Based Systems Michael Rovatsos mrovatso@inf.ed.ac.uk Lecture 13 Argumentation in Multiagent Systems 1 / 18 Where are we? Last time... Bargaining Alternating offers Negotiation decision functions