How can Computer Science contribute to Research Publishing

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How can Computer Science contribute to Research
Publishing?
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Introduction to KRR Group
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Who are we?

• Academics
• Ian Horrocks
• A. N. Other
• Research Students
• Héctor Pérez-Urbina
• Rob Shearer

• Research Staff
• Bernardo Cuenca Grau
• Birte Glimm
• Yevgeny Kazakov
• Boris Motik
• Rob Shearer

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What Do We Do?

• Knowledge representation (obviously)
• Ontologies and ontology languages
• Description logics
• Formal underpinnings of ontology languages
• Reasoning problems and algorithms
• Implementation and optimisation of reasoning
  systems

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Relevance to Publishing?
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Annotations

• Key role will be played by annotations

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Annotations

• Key role will be played by annotations
• But how can meaning be understood by software?

Now... that should clear up a few things around
here
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Annotation Semantics

• Agree on meaning of a set of terms
• E.g., Dublin Core
• Limited flexibility and extensibility
• Limited number of things can be expressed
• Agree on language used to define meanings
• E.g., an ontology language
• Flexible and extensible
• New terms can be formed by combining existing
  ones
• Meaning (semantics) of such terms is formally
  specified

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What is an Ontology?

• A model of (some aspect of) the world
• Introduces vocabulary relevant to domain
• Often includes names for classes and
  relationships
• Specifies intended meaning of vocabulary
• Typically formalised using a suitable logic
• Closely related to schemas in the DB world
• Instantiated by set of individuals and relations
• Defines constraints on possible instantiations

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Supporting Ontology Engineering

• Developing and maintaining quality ontolgies is
  very challenging
• Users need tools and services, e.g., to help
  check if ontology is
• Meaningful all named classes can have instances

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Supporting Ontology Engineering

• Developing and maintaining quality ontolgies is
  very challenging
• Users need tools and services, e.g., to help
  check if ontology is
• Meaningful all named classes can have instances
• Correct captures intuitions of domain experts

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Supporting Ontology Engineering

• Developing and maintaining quality ontolgies is
  very challenging
• Users need tools and services, e.g., to help
  check if ontology is
• Meaningful all named classes can have instances
• Correct captures intuitions of domain experts
• Minimally redundant no unintended synonyms

?
Banana split
Banana sundae
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Supporting Ontology Engineering

• Range of new non-standard services supporting,
  e.g.
• Error diagnosis and repair

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Supporting Ontology Engineering

• Range of new non-standard services supporting,
  e.g.
• Error diagnosis and repair
• Modular design and integration
• What is the effect of merging O2 into O1?
• Module Extraction
• Extract a (small) module from O capturing all
  relevant information about some vocabulary V
• Bottom-up design
• Find a (small and specific) concept describing a
  set of individuals

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Supporting Query Answering

• In an Ontology based Information System
  (OIS),Query answering ¼ computing logical
  entailment
• Reasoner needed in order to answer queries, e.g.
• C is a sub-class of D iff O ² 8 x . C(x) ! D(x)
• a is an instance of C iff O ² C(a)
• OIS with no reasoner ¼ DBMS with no query engine

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Information Integration

• Ontologies provide unifying schema
• Bridging between different data sources
• Query answering w.r.t.ontology
• Date retrieved from relevant sources
• Similar to data integration in DBs
• More flexible
• Deductive capabilities

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Driving User Interfaces

• Interface reflects structure of knowledge
• Query by navigation
• Semantically meaningful presentation of data
• Easier understanding
• Context aware

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Research Themes
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Ontology Languages

• Standards crucial
• Interoperability
• Tool support
• W3C OWL ontology language standard
• Central role in development of OWL language
• Leading development of OWL 2
• Extension to OWL driven by application
  requirements
• OWL 3?
• Graphs
• Integrity constraints

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Scalability

• Integration of DLs with DBs
• Tractable ontology languages
• Lightweight languages for data-intensive
  applications
• Reasoning can be reduced to SQL querying
• New HermiT DL reasoner
• Implements optimised hypertableau algorithm
• Already outperforms existing reasoners
• Aim is to push the limits of practical reasoning

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New Reasoning Services

• Integration extraction of modules
• Algorithms and practical techniques
• Incremental reasoning
• Methodologies and tool support

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New Reasoning Services

• Conjunctive query answering
• Views
• Definition and application in ontologies
• Algorithms and tool support
• Information hiding and privacy
• Lift/transform ideas from DB research
• Reformulate as reasoning problems

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Thank you for listening
Any questions?