• Save
Semantic technologies at work
Upcoming SlideShare
Loading in...5
×
 

Semantic technologies at work

on

  • 1,222 views

highlights the use of semantic technologies to facilitate and empower semantic interoperability

highlights the use of semantic technologies to facilitate and empower semantic interoperability

Statistics

Views

Total Views
1,222
Views on SlideShare
1,222
Embed Views
0

Actions

Likes
1
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Semantic technologies at work Semantic technologies at work Presentation Transcript

  • Semantic technologies at work: The interoperability perspective A talk by Yannis Kalfoglou at Wednesday, 22 nd August 2007
  • Overview
    • Emergence ( and maturity of ) semantic technologies
      • from the academic silos to industrial settings
      • Semantic Web and Web 2.0 drivers
    • Interoperability as a key enabling technology
      • business incentives
      • semantic interoperability
      • application areas
    • Using semantic technologies for interoperability
      • ontology mapping
        • IF-Map method and tool
        • CROSI framework, API and CMS system
      • progressive ontology coordination
    • Exemplar scenarios and applications
      • academic organisational structures alignment
        • Enabling inter-organisational interoperability
      • e-Government units alignment
        • Enabling information exchange and collaborative processes
      • bioinformatics terminologies alignment
        • Enabling consensus building
      • emergency response services terminology disambiguation
        • Enactment of collaborative services and information discovery
    • Issues
      • STs adoption
      • Interoperability in other domains (mobile web 2.0)
    Semantic technologies Interoperability Using STs Experiences Issues 2/14
  • Semantic Technologies why do we need them?
    • business drivers
      • unprecedented exposure of information in digitised form  too much information available: “ infosmog ”
      • turn that information into usable knowledge
      • collaborative, inter-organisational business systems
      • globalisation of business processes – need for increased interoperability
      • empowerment of the individual
      • retention of knowledge
      • reduction of implementation time and cost
    • societal drivers
      • trends in employment: “up to 30% of EU’s working population will be directly employed in the production and diffusion of knowledge” [EU 2004]
      • knowledge based economy
      • ICTs affordable and available to everyone – participation is cheaper and global
      • (1.1bn web users, 17bn devices on the Internet by 2012 – [IDC 2005/6] )
    • technology triggers
      • syntax based systems can’t cope with information explosion ( semantic heterogeneity )
      • semantics can be codified and represented in computational form
      • maturity of highly distributed open-end environments, e.g., (Semantic) Web; and Web 2.0 approaches (social networking)
    3/14 Semantic technologies Interoperability Using STs Experiences Issues
    • Emergence of “meaningful computing”: emphasis on codified representations of semantics
      • represent meaning in computational forms;
      • enable automation and intelligent tasks;
      • better information management (search, discovery, integration);
      • enable re-use;
      • improved interoperability (semantic-based vs. syntax-based)
    • Wide range of technologies:
      • ontologies (OWL, KIF, Ontolingua, etc.)
      • data formats (RDF & RDF(S), ebXML )
      • query languages (SPARQL)
      • APIs (HP Jena)
      • open standards (OG(eo)C, SUO, CL)
      • SOA
    • Variety of application areas:
      • knowledge representation & reasoning (ontologies)
      • semantic annotation (semantic tagging)
      • semantic querying (SPARQL, OWL-driven)
      • semantic interoperability and integration (ontology/schema mapping)
      • semantically enriched storage (RDF)
    Semantic Technologies what are they? where do we apply them? 4/14 Semantic technologies Interoperability Using STs Experiences Issues
    • Semantic Web
      • (towards) an infrastructure made up of representation languages, communication protocols, access controls & authentication services for coordinated sharing of knowledge across particular domain-oriented applications
      • (vision) turning the web into a globally distributed knowledge base where software agents will assist in most, if not all, tasks of information management
    • Web 2.0
      • enabling technologies for mass participation in social networking activities on the Web
      • range of supporting technologies (AJAX, wikis, tagging, bloging, etc.)
      • no centralised control, no agreed vocabularies, “wisdom of the clouds” (collective intelligence)
    • Semantic technologies are a core component for both
    Semantic Technologies The Semantic Web and Web 2.0 impact 5/14 Semantic technologies Interoperability Using STs Experiences Issues
  • Interoperability a key enabling technology
    • business incentives
      • disparate and open-ended environments for applications
      • complex tasks and cross sector applications
      • Inevitably, heterogeneity inherited in the system
        • variety of protocols, domains, languages, processes, interpretations
      • preserve the meaning of concepts used in everyday transactions of information sharing
        • a key enabler for automated collaborative tasks
    • semantic interoperability
      • syntax alone is not sufficient to capture the subtle differences in meaning
      • semantics allow to express meaning in structured computational forms
      • use these forms, alongside syntactic based approaches, to enable semantic interoperability
    • application areas ( non-exhaustive list )
      • database schema integration ( construct global views )
      • data warehouses ( data format transformations )
      • e-commerce ( message exchange )
      • semantic query processing ( query terms disambiguation )
      • ontology integration/merging ( construct top-level/global ontologies )
      • ontology alignment/mapping (concepts’ alignment)
      • SW agents interoperability ( agents vocabulary alignment )
    6/14 Semantic technologies  Interoperability Using STs Experiences Issues
  • Using STs for interoperability ontology mapping – IF-Map
    • aligns the vocabularies of two ontologies that share the same domain of discourse in such a way that the mathematical structure of ontological signatures and their intended interpretations – as specified by ontological axioms, are respected.
      • Structure preserving mechanisms – morphisms
      • establish a collection of binary relations between the vocabularies
    7/14 Semantic technologies  Interoperability  Using STs Experiences Issues
    • Information flow based ontology mapping
    • 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:
    • IF-Map
    • A method and a system that implements the idea of Information flow based ontology mapping
    • Ontologies are represented as IF classifications (tokens to types)
    • Customised parsers to extract features from the ontology (tokens, types) Java front end, API, output in RDF
  • Using STs for interoperability ontology mapping - CROSI 8/14
    • Too many disparate ontology alignment and semantic integration systems
    • Complementary functionalities, lack of consensus, lack of a principled process for alignment
    • CROSI ( C apturing R epresenting and O perationalising S emantic I ntegration) – HP funded project
    • a state-of-the-art survey of semantic integration (SI) technologies
      • a principled characterisation of SI systems
      • a modular architecture for developing ontology mapping systems
      • A fully functional, proof-of-concept prototype system (CMS) for ontology mapping
        • Structure and semantics based
        • Multiple matchers approach
        • Aggregating alignments and tuning algorithms (via API or command line prompt)
    Semantic technologies  Interoperability  Using STs Experiences Issues
  • Using STs for interoperability progressive ontology coordination
    • Ontology coordination: progressively sharing information on instances from a common domain of discourse and how these are classified under distinct agents’ ontologies
    • Two agents A1 and A2 want to interoperate on the SW, their knowledge is represented according to their own conceptualisations – explicitly modelled as ontologies which are not open for inspection, learning each others’ ontologies can only be based on interaction
      • Exchange instance classification information
      • A1 and A2 will have distinct set of instances
      • A priori knowledge of a common domain of instances necessary
      • 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
    • Token connection as a result of instances passing between agents
    • 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 )
    9/14 Semantic technologies  Interoperability  Using STs Experiences Issues
  • Scenarios and applications Aligning academic organisational structures
    • 5 university computer science departments organisational structures
      • differences in conceptualisation of similar tasks, roles, concepts
    • use of IF-Map methodology and system
    10/14 Semantic technologies  Interoperability  Using STs  Experiences Issues
    • Alignment of concepts and relations
      • Dependant on type checking
      • Classified instances
      • Kick-off heuristics to fix the initial map
  • Scenarios and applications e-Government: ministerial units alignment 11/14 Semantic technologies  Interoperability  Using STs  Experiences Issues e-Gov scenario of UK and US software agents trying to align ministerial units from 4 ministries/departments: UKFCO, UKHO, USDoJ, USDoS use taxonomy and ministry/dept. structure but also responsibility and how these are allocated within the ministry/dept. Use IF technology to represent the structure and infer dependencies in the lattice: Use initial partial alignment of UK to US responsibilities (tokens) and IF classifications and IF-Map mechanism to infer the type level alignment:
  • Scenarios and applications Bioinformatics: terminology alignment 12/14 Bioinformatics alignment scenario Part of the annual ontology alignment contest (sponsored by OAEI) FMA  Foundational Model of Anatomy large reference .owl ontology ~31MB, 73k classes, ~100 relations OpenGALEN  open source version of a healthcare ontology anatomy part, (compact .owl ontology, ~4MB, ~25 classes, ~30 relations)
    • Use of CROSI CMS system
    • Structure and linguistic matchers
    • Adjustments to handle the size of FMA
    Semantic technologies  Interoperability  Using STs  Experiences Issues
  • Scenarios and applications e-Response: term disambiguation and service enactment 13/14
    • Application of STs in an emergency response scenario
      • aim to facilitate planners to make sense of a complex situation as it unfolds in real time
      • used a simulator and realistic scenario
      • focussed on real world resources and emergency services
    • Technologies for: semantically enriched storage; semantic annotation, information extraction, sense making GUI, planning and scheduling, resource binding, communities of interest identification, service invocation and term disambiguation (modified CROSI CMS)
    Semantic technologies  Interoperability  Using STs  Experiences Issues
    • Semantic technologies adoption
      • “ start small with the big picture in mind”
      • (from lightweight semantic hypertext markup – microformats - to fully fledged ontologies)
      • Prepare for a major shift in business models
      • Long term investment
      • Keep it simple (semantics)
        • Irreconcilable arguments among engineers at design time
        • Inappropriate interpretations and usage at deployment time
        • Hard to maintain – not stable over time – obsolete knowledge
    • Interoperability as a means to enable intelligent tasks in a variety of domains
      • mobile web 2.0 trends (web 2.0 + mobile web)
      • more people with mobiles than PCs
      • Social networking (web 2.0) on the Web (single network) using a common driver (mobile device)
      • Global audience, global services, single marketplace
    • Web access for “internet of things”
      • Universal content, ubiquitous computing
      • Interoperability needs between:
        • Global services, universal content, to enable social networking and ubiquitous computing
    Issues technology & socio-political 14/14 Semantic technologies  Interoperability  Using STs  Experiences  Issues
  • Semantic technologies at work The interoperability aspect A talk delivered by Yannis Kalfoglou at Wednesday, 22 nd August 2007 Thank you for listening
  • A principled way of characterising semantic integration systems
  • Accessing CMS Command line input Web-based GUI Programmatic access
  • Versatile output Single matcher output SKOS output Combinations of matchers
    • h the distance between “Word” and Root
    • h’ the distance between “Word’” and Root
    • H the distance between common subsumer of “Word” and “Word’” and Root
    • Similarity between “Word” and “Word’” is computed as
    • 2H/(h+h’)
    Structure algorithm P1’(C’, B’) P2’ P3’ P1(C, B) P2 P3 f (name similarity, domain similarity, range similarity ) Canonical Name C=> A.B.D.C C’=> A’.B’.D’.E’.C’ Compute the similarity between C and C’ as well as the respective similarity between every pair of super classes of C and C’ Penalise the similarity between C and C’ with those of their super classes Root Common Subsumer Word’ Word H h’ h H G Domain of P1 H’ G’ I’ Domain of P1’ A B D Range of P1 E A’ B’ D’ E’ Range of P1’ F’ C’ A’ B’ D’ E’ A B C D
  • Agents exchange instances coordinated channel, so far… Complete coordination and IF theory on semantic relations:
    • W3C Mobile Web Initiative
      • Best Practices – online checker
      • mobileOK conformance mark – machine testable
    • dot mobi
      • mTLD (top level domain for mobiles – ICANN)
    Standards, early adopters, trends
    • Early adopters
      • Orange – Pikeo, bubbletop, Soundtribes
      • Vodafone – Betavine
      • NeoMedia/Qode, radar.net, ShoZu, TellMe
      • BBC + CNN mobile  web 2.0 features – UGC
    • Trends
      • think beyond numbers (mobile device not only a phone – URIs, tags, etc.)
      • Pushing out too much content leads to microcontent