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On the Emergent Semantic Web and Overlooked Issues - 2004
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On the Emergent Semantic Web and Overlooked Issues - 2004

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highlight areas and vulnerabilities of semantic web for large scale adoption and deployment

highlight areas and vulnerabilities of semantic web for large scale adoption and deployment

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  • 1. Yannis Kalfoglou Advanced Knowledge Technologies (AKT) Southampton, UK SW vulnerabilities On the emergent Semantic Web and overlooked issues Chris Walton Advanced Knowledge Technologies (AKT) Edinburgh, UK Harith Alani Advanced Knowledge Technologies (AKT) Southampton, UK Marco Schorlemmer IIIA, Barcelona, Spain
  • 2. Overview
    • Emergent SW
    • Needs, expectations, deliverables and an oxymoron….
    • Vulnerabilities:
      • Agency coordination & mechanised trust
      • Robust reasoning
      • Semantic interoperability
    • Pragmatics
    • take away message….
    • small print…
    • We do not provide solutions
    • We critically review work done and identify weaknesses
    • We wholeheartedly support the idea of the SW and work hard to realise it but we aim to raise awareness of potential pitfalls which could hinder further development
  • 3. The emergent SW
    • Less than five years in existence but already seen:
      • a plethora of prototypical systems and tools
      • massive interest ( and investment! ) from academia and industry alike
      • lots of published works
      • Worldwide interest and a variety of diverse projects
      • Emerging standards (mostly languages: RDF, OWL, etc.)
    • But, so far there is a dearth of:
      • Critiquing work
      • Identification of weaknesses
      • Killer-apps
      • Massive user involvement – most SW users are its practitioners!
      • SW tasks’ definitions ( often Web tasks are seen as SW tasks )
  • 4. Needs, expectations and deliverables’ oxymoron
    • Semantics are seen as the differentiating factor from the Web, hence, provide:
      • content providers with help to annotate their data with semantics – RDF
      • a common ontology language (OWL) to manifest K-sharing on the SW
      • Sophisticated tools for SW developers – robust APIs (HPJena, OWL-API, etc)
    • There is a need to keep things simple and ease to use – compromise KR
    • We are expecting the SW to be an infrastructure
    • Oxymoron: “[…] you can’t have a compromised KR to support an infrastructure that aims to provide the means for accessing and integrating disparate data in semantically rich manners.”
    • Best practices today: find a compromise on both ends (CSAKTive Space vs. KAON, two exemplar systems)
    Needs, expectations and deliverables’ oxymoron
    • Semantics are seen as the differentiating factor from the Web, hence, provide:
      • content providers with help to annotate their data with semantics – RDF
      • a common ontology language (OWL) to manifest K-sharing on the SW
      • Sophisticated tools for SW developers – robust APIs (HPJena, OWL-API, etc)
    • There is a need to keep things simple and easy to use – compromise KR
    • We are expecting the SW to be an infrastructure
    • Oxymoron: “[…] you can’t have a compromised KR to support an infrastructure that aims to provide the means for accessing and integrating disparate data in semantically rich manners.”
    • Best practices today: find a compromise on both ends (CSAKTive Space vs. KAON, two exemplar systems)
    “ The SW is what we will get if we perform the same globalisation process to KR that the Web initially did to Hypertext. We remove the centralised concepts of absolute truth, total knowledge, and total provability, and see what we can do with limited knowledge .” “ […]the perception that the SW is concerned with AI is not helpful to its widespread adoption in the IT industry[…]the SW is often presented as the technology that will achieve marvellous things […]these are not what the SW is about. The SW is about creating an infrastructure in which information from a variety of sources can be integrated on demand to achieve some task . The SW provides mechanisms that enable access to and integration of data .”
  • 5. Vulnerabilities – agency coordination
    • It is envisaged that externally defined and distributed resources on the SW will be assessed by some form of mechanised trust and accessed by agents….
    • But, there are a number of challenges to resolve before agents can automatically utilise semantically annotated information on the SW
    • Clear definition of a SW agent
      • AI definitions vs. simple agents (Web services)
    • Agency coordination
      • Semantic markup itself it is envisaged to solve the problem of coordination
      • It is not clear how this could be done with autonomous agents that have different internal representations – need for mapping or adhere to a standard
      • Mainstream solution: use of performative languages (i.e., FIPA-ACL)
    • Agency sincerity
      • There is an underlying sincerity assumption in these languages: Agents always act in accordance to their intentions
      • But insincere agents can exist as we cannot access its internal state
      • This is the semantic verification problem – remedies: restrict behaviour, expose internal states
  • 6. Vulnerabilities – agency coordination – cnt’d
    • Dialogue protocols
      • Performative languages define how agents should communicate while the protocols define if and when they should communicate
      • example: Electronic Institutions MAS
      • But there are no straightforward means to disseminate the institutions thus agents must be designed in advance for a specific institution
    • Social policy
      • Establish shared commitments between agents
      • These restrict and control agents’ actions and are stored in MAS’ social states
      • But considerable implementation issues remain in maintaining them
    • Dialogue games
      • Used to study fallacious reasoning for NLP
      • Could be used as a basis for interaction between autonomous agents
      • But frameworks defined at a formal level and no implementations are avail.
    • Dynamic scripting
      • Scripts are build dynamically during evaluation when agents communicate
      • These express protocols – early implementations
  • 7. Vulnerabilities – mechanised trust
    • Mechanising trust
      • need automated methods for measuring trust
      • KBSs statements vs. distributed knowledge systems trustworthiness
    • Modelling & representation of trust
      • generic trust models might not be suitable for supporting MAS on the SW
      • need for an expressive language to represent trust
    • Sourcing trust
      • trust gathered and broadcasted in centralised units vs. trust in distributed forms for agents to collect
      • centralised: trustworthiness of feedback and of the warehouse itself
    “ Lucy’s agent, having complete trust in Pete’s agent in the context of the present task, automatically assisted by supplying access certificates[…]”
  • 8. Vulnerabilities – mechanised trust
    • Measuring trust
      • agents combine their experiences when measuring trustworthiness of other agents – but: availability/accessibility problems
      • mainstream work: extract trust values from decentralised, interconnected semantic nets – but: trust values often given by users
    • Contextualizing trust
      • lack of context in most measuring trust approaches
      • associating context with trust measurement increases calculation and representational complexity
      • transitivity and propagation of trust on the SW (subjective and context-dependent)
  • 9. Vulnerabilities – robust reasoning
    • Handling soundness and completeness
      • precise technical terms that describe properties of formal systems or set of sentences – sound – complete
      • traditional KRR views shaped standardisation efforts for current SW technology for obvious practical reasons
        • But: the envisioned applications of the SW are clearly beyond the capacity of FOL-based and DL-based technology
      • problem with preserving and mechanising soundness and completeness on the SW is the lack of referential integrity and inconsistent knowledge produced by multiple resources
    A formal system is sound when every sentence produced by the system’s inference rules operating on the system’s initial set of axioms logically follows from that set It is complete when every sentence that logically follows from the system’s initial set of axioms can be formally derived using the inference rules. A set of sentences are said to be complete if every sentence of the language can be proved or disproved using those rules. In an environment the size of the web we must abandon the classical idea of sound and complete reasoners, our reasoners will almost certainly have to be incomplete (no longer guaranteeing to return all logically valid results), but most likely also unsound : sometimes jumping to a logically unwarranted conclusion[…] answers will have to approximate.
  • 10. Vulnerabilities – robust reasoning
    • Open and closed worlds
      • traditional CWA: “ everything that is not known or cannot be proved to be true must be false ”
      • common in DB design but DLs adopt an “open world” semantics: absence of information in a DB is regarded as negative information in an ABox as lack of knowledge – in ABoxes information is generally viewed as incomplete
      • despite its controversial reception, CWA could be useful .
      • remedies: use of extra-logical operators to state negative information (logic), use ASP to draw conclusions based on the lack of evidence of the contrary, use of Local World Assumption (LCW): use both CWA information and allow other information to be treated as unknown
      • caution: a priory knowledge of local completeness (not scalable), asserting LCW in vast knowledge sources could lead to inconsistencies
    “ [..]the language must be able to state that a given ontology can be regarded as complete . This would sanction additional inferences to be drawn from that ontology. The precise semantics of such statement (and the corresponding set of inferences) remains to be defined, but examples might include assuming complete property information about individuals, assuming completeness of class-membership , and assuming exhaustiveness of subclasses .”
  • 11. Vulnerabilities – robust reasoning
    • Semantic interoperability
      • proliferation of ontologies on the SW – need for standardisation (Cyc, IEEE SUO, etc.)
        • But: standardisation is costly and make sense for small communities – large standard ontologies need to be controlled by centralised agencies
        • also: need to be shared with other communities
      • distributed and decentralised nature of the SW calls for interoperability of communities’ at the semantic level (ontology mapping/merging)
      • Semantic interoperability on the SW should go beyond standardised representation formats to accommodate diverse representations and inference engines
      • other approaches for accommodating various understanding of semantics need to be explored (e.g., information flow)
  • 12. Pragmatics
    • We gave an honest picture of the SW realm – IOHO
    • We tapped on big scientific challenges we don’t realistically expect them to be fully resolved before the SW is available and commercially exploitable
    • SW’s model of evolution follows Web legacy: network effect
      • Linking up content
      • But linking up SW content is not enough – how to fix a broken semantic link? (“404” ontology not found)
      • content management on the SW not controlled by few but provided by many
      • need for interoperability and trust to feed useful content into our reasoners
    • ethnographical studies: we should come to expect erosion of trust
    “ of all the changes that are transforming the Internet, the loss of trust maybe the most fundamental. The simple model of early Internet – a group of mutually trusting users attached to a transparent network – gone for ever. A motto for tomorrow may well be ‘ global communication with local trust ’ .”
  • 13. Pragmatics
    • demand for trustworthy overall operation but inability to trust the behaviour of individual users
    • our reasoners should be able to cope with flawed content
      • current KRR prevents us from providing complete and sound automated reasoning over incomplete and/or inconsistent content
    • provide more links with DB and SE technology (SQL, UML, etc.)
    • cultural considerations: current Web users are expecting SW to be a truly superior product – functionally, ergonomically, technologically.
      • SW browser vs. Web browser
      • But: “SW vs. Web” does it still make sense to distinguish them?
  • 14. Closing remarks
    • Take away message (for this talk and the conference as a whole)
    • A lot have been achieved in a relatively short time, but more could have been done
    • Web legacy too heavy for the SW?
    • change of course:
      • Not focussing on building a SW but a series of SWs
      • All of which will be smaller in size but at least allow us to experiment, develop and deliver (whenever possible) the vision of the SW
      • once we master the art of managing these and have reached critical mass of users we can then link them, thus creating a Web of SWs
      • That could then evolve to the SW
    • Contrary to the current SW norm: hastingly annotate masses of content with dubious semantics in the hope that advanced, but yet-to-be built, reasoners will process that content on behalf of unidentified users
  • 15. Questions OWL Open World Semantics Local World Assumption Closed World Assumption SW browser SW vision SWRL DAML-S DAML OWL-S RDF(S) RDF OWL lite OWL full OWL DL erosion of trust SW agents Semantic interoperability Semantic integration completeness soundness Mechanizing trust FIPA Standards SW services WSDL Automated reasoning Model-theoretic semantics Incomplete information Unsound information RDQL RQL HP Jena OWL-API Outreach to industry SW users?