A Multi-Agent System for Building Dynamic Ontologies K evin Ottens - PowerPoint PPT Presentation

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives A Multi-Agent System for Building Dynamic Ontologies K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize Institut de Recherche en Informatique de Toulouse (IRIT) SMAC team AAMAS 2007 – May 14–18 2007, Honolulu, Hawai’i, USA. K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 1/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Plan 1 Introduction 2 Introducing the Dynamo System 3 Distributed Clustering Algorithm 4 Multi-Criteria Hierarchy 5 Discussion & Perspectives K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 2/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Plan 1 Introduction 2 Introducing the Dynamo System 3 Distributed Clustering Algorithm 4 Multi-Criteria Hierarchy 5 Discussion & Perspectives K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 3/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Introduction Current situation Text analysis makes ontology building easier NLP analysis examination is a difficult and slow process Emerging technics based on machine learning Our proposal Keep the user in the production loop Allow the ”Living Design” of ontologies Reorganization following user modifications K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 4/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Plan 1 Introduction 2 Introducing the Dynamo System 3 Distributed Clustering Algorithm 4 Multi-Criteria Hierarchy 5 Discussion & Perspectives K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 5/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Overview Architecture Multi−Agent System Interface Ontologist ?? Terms Extraction Tool Term network Concept Agent Term K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 6/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Overview Term Network Produced by Syntex ”Head-Expansion” graph knowledge engineering from text knowledge engineering Term contexts used to compute similarity Multi-Agent System Each agent represents a concept of the taxonomy Each agent tries to position itself Based on a condition/action rule set K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 7/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Plan 1 Introduction 2 Introducing the Dynamo System 3 Distributed Clustering Algorithm 4 Multi-Criteria Hierarchy 5 Discussion & Perspectives K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 8/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Distributed Clustering Algorithm Local view P A1 A1 A2 ...... Ak−1 Ak ...... An Steps 1 Evaluating similarity and ”votes” 2 Partitioning and intermediate layer creation 3 Parent change K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 9/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Distributed Clustering Algorithm Local view P P’ P’ P’ ...... ...... A1 A2 Ak−1 Ak An Steps 1 Evaluating similarity and ”votes” 2 Partitioning and intermediate layer creation 3 Parent change K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 9/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Distributed Clustering Algorithm Local view P P’ ...... ...... A1 A2 Ak−1 Ak An Steps 1 Evaluating similarity and ”votes” 2 Partitioning and intermediate layer creation 3 Parent change K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 9/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Distributed Clustering Algorithm Global View P P P P’ P’ P’ P’ A1 A2 ...... Ak−1 Ak ...... An A1 A2 ...... Ak−1 Ak ...... An A1 A2 ...... Ak−1 Ak ...... An P P P P’ P’ P’ P’ ...... ...... ...... ...... A1 A2 Ak−1 Ak An A1 A2 ...... Ak−1 Ak ...... An A1 A2 Ak−1 Ak An K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 10/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Experimental Complexity Results 180000 1. Distributed algorithm (on average, with min and max) 2. Logarithmic polynomial 160000 3. Centralized algorithm 140000 Amount of comparisons 120000 100000 80000 60000 40000 20000 0 10 20 30 40 50 60 70 80 90 100 Amount of input terms Average complexity: O ( n 2 log ( n )) Maximum variance: around 5% K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 11/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Qualitative Point of View Automated run Permanent view on the built hierarchy Allow to obtain a ”first draft” User modification No algorithm adjustment required Dynamicity, revision of the structure K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 12/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Plan 1 Introduction 2 Introducing the Dynamo System 3 Distributed Clustering Algorithm 4 Multi-Criteria Hierarchy 5 Discussion & Perspectives K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 13/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Head Coverage Rules Intended Behavior Observations Similarity can’t be always computed for term pairs Humans have specific heuristics for low-level structuring Goal Take care of those terms Implement a similar heuristic Parent Adequacy Function The best parent for C is the P agent that maximizes a ( P , C ). When an agent C is unsatisfied by its parent P, it evaluates a ( B i , C ) with all its brothers (noted B i ) the one maximizing a ( B i , C ) is then chosen as the new parent. K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 14/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Managing Several Criteria Guidelines How? Keeping it simple Local criteria Nominal values for those criteria Use cooperation heuristic Cooperation Minimizing non-cooperation Priority system Determine the current problems Find the most urgent one Try to fix it K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 15/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Managing Several Criteria Actual Implementation Minimize non cooperation µ H ( A ): ”head coverage” non cooperation degree of A µ B ( A ): ”brotherhood” non cooperation degree of A µ M ( A ): ”message” non cooperation degree of A µ ( A ) = max ( µ H ( A ) , µ B ( A ) , µ M ( A )) Take care of the worst problem first µ ( A ) = µ H ( A ) → Try to find a better parent µ ( A ) = µ B ( A ) → Improve structuring through clustering µ ( A ) = µ M ( A ) → Process other agent message K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 16/22

Introduction Dynamo Distributed Clustering Algorithm Multi-Criteria Hierarchy Discussion & Perspectives Experimental Complexity Revisited 25000 1. Dynamo, all rules (on average, with min and max) 2. Distributed clustering only (on average) 2. Cubic polynomial 20000 Amount of messages 15000 10000 5000 0 10 20 30 40 50 60 70 80 90 100 Amount of input terms Average complexity: O ( n 3 ) Maximum variance: around 0.6% K´ evin Ottens , Marie-Pierre Gleizes & Pierre Glize — A Multi-Agent System for Building Dynamic Ontologies 17/22