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A Channel Theoretic Foundation for Ontology Coordination - 2004
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A Channel Theoretic Foundation for Ontology Coordination - 2004

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how information flow theory can be applied in the context of software agents' coordination using aligned ontologies

how information flow theory can be applied in the context of software agents' coordination using aligned ontologies

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A Channel Theoretic Foundation for Ontology Coordination - 2004 A Channel Theoretic Foundation for Ontology Coordination - 2004 Presentation Transcript

  • Yannis Kalfoglou Advanced Knowledge Technologies (AKT) Southampton, UK IF-based ontology coordination A Channel-Theoretic Foundation for Ontology Coordination Marco Schorlemmer Artificial Intelligence Research Institute (IIIA-CSIC) Barcelona, Spain
  • Overview
    • Semantic heterogeneity, Semantic Interoperability and Integration
    • Basic Information Flow concepts & ontologies
    • Ontology coordination
    • Progressive semantic integration example
    • Implementations & future directions
  • Semantic Interoperability & Integration
    • Exchanging syntax is insufficient for achieving interoperability
      • Semantic heterogeneity
    • Tackle the problem with agreed ontologies
      • Not easy to reach consensus
      • Completeness and stability
      • Hard to engineer in distributed and dynamic environments (a.k.a. SW)
    • Need to achieve interoperability progressively, by coordinating and negotiating meaning “on-the-fly” at interaction time
    • Focus on a commodity for meaning: information
    • Problem: how to engineer exchange of information for the sake of coordinating and negotiating meaning
  • Information flow basics
    • A theory of information flow for distributed systems
    • Each component is modelled by means of an IF classification
    • Connections between components are modelled by infomorphisms:
  • Information flow basics - cnt’d
    • Information flow channel:
  • Ontologies and information flow
    • Concepts organised in is-a hierarchies
    • Disjointness - no instance can be considered of both concepts
    • Coverage – all instances are covered by two concepts
    • Populated ontology : classify objects of a set X according to the concept symbols in C by defining a classification relation |= C
    • IF classification: C = (X,C ,|= c ), where X = tok( C ), C = typ( C )
    • Classification relation |= c is defined in such a way so that partial order, coverage and disjointness are respected.
  • Ontology coordination
    • Two agents A1 and A2 want to interoperate on the SW
    • Their knowledge is represented according to their own conceptualisations – explicitly modelled as ontologies, O1 and O2
    • A concept of O1 will always be considered semantically distinct from any concept of O2 unless there is sufficient evidence that it means the same to A1 as it means to A2
    • O1 and O2 are not open for inspection
    • Learning each others’ ontologies can only be based on interaction
      • Exchange instance classification information
      • i.e. A1 knows about rivers and streams , A2 about riviere and fleuve , so A1 tells A2 that Ohio is classified as a river
      • A1 and A2 will have distinct set of instances
      • A priori knowledge of a common domain of instances necessary
    • Ontology coordination: progressively sharing information on instances from a common domain of discourse and how these are classified under distinct agents’ ontologies
    “ In English, size is the feature that distinguishes river from stream ; in French, a fleuve is a river that flows into the sea, and a riviere is either a river or a stream that runs into another river.”
  • Progressive semantic integration
    • Four broadly defined steps for achieving semantic integration using IF
      • Model the populated ontologies using IF classifications
      • Define an IF channel that connects agents’ IF classifications
      • Define the IF logic at the core of the channel to represent IF
      • Distribute the IF logic to the sum of IF classifications to obtain an IF theory that describes the desired semantic interoperability
    • Not actual engineering steps, 2&3 will require complete knowledge of all agents involved.
    • If engineered, these steps will result in a global ontology of two semantically integrated agents
    • This classification determines the IF theory about how these concepts are semantically related:
  • Progressive semantic integration – cnt’d
    • But: (a) it is not clear where we gain the knowledge required to link the tokens in the way we did; (b) we can assume complete information in order to link all tokens and define the IF theory on the union of all types.
    • So, we argue for a coordinated channel:
      • Captures the degree of participation of each agent
      • This can be determined at the type and token level:
        • Agents will have attempted to explain a subset of their types
        • Other agents will have shared some instances, incrementing in this way the original set of instances.
    • Diagrammatically as infomorphisms between subclassifications :
  • Progressive semantic integration – cnt’d
    • The coordinated channel can be defined by taking the colimit of the diagram linking the IF subclassifications:
    • This is the general model, initially, when no coordination has taken place , we have:
    • assuming that A1 tells A2 that Ohio |= river and A2 tells A1 that Ohio |= riviere
      • coordinated channel, so far…
    • … and then, A2 tells A1 that Roubion |= riviere and A1 tells A2 that Roubion is a stream
      • coordinated channel, so far….
  • Progressive semantic integration – cnt’d
    • At each stage the distributed IF logic determined at the core of each new channel captures the semantic integration achieved so far:
    • Ideally, we could achieve complete semantic integration:
  • Closing remarks
    • Ontology coordination focuses on alignment at the type-level
      • IF approach advocates token-level connection in order to determine semantic integration at the type-level
    • We argued for token connection as a result of instances passing between agents
    • The formalisation we propose allows for a wider view of what could be considered to be a token what a connection between tokens.
      • so, different interoperability scenarios can be realised
    • We also highlighted that ontology coordination can hardly be absolute , but depends on
      • the way ontologies are used ( populated ontologies )
      • the particular understanding of semantics ( our choice of types/tokens )
      • how ontologies are linked together via connected tokens ( semantic integration guaranteed on connected tokens )
  • Questions