Towards a new generation of semantic web applications

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Towards a new generation ofsemantic web
applications

• Prof. Enrico Motta, PhDKnowledge Media
  InstituteThe Open UniversityMilton Keynes, UK

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The Semantic Web

• A large scale, heterogenous collection of formal,
  machine processable, web accessible,
  ontology-based statements (semantic metadata)
  about web resources and other entities in the
  world, expressed in a XML-based syntax

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The Semantic Web (pragmatic def.)

• The collection of all statements expressed in one
  of the following formalismsOWL, RDF, DAML,
  DAMLOIL, RDF-A, which can be accessed on the
  web

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hasAffiliation
Organization
Person
Ontology
worksInOrgUnit
partOf
hasJobTitle
String
Organization-Unit
Metadata
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Ontology
Metadata
ltRDF triplegt ltRDF triplegt ltRDF triplegt ltRDF
triplegt ltRDF triplegt ltRDF triplegt
ltRDF triplegt ltRDF triplegt ltRDF triplegt ltRDF
triplegt ltRDF triplegt ltRDF triplegt
ltRDF triplegt ltRDF triplegt ltRDF triplegt ltRDF
triplegt ltRDF triplegt ltRDF triplegt
UoD
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Proposition 1

• The SW today has already reached a level of scale
  good enough to make it a very useful source of
  knowledge to support intelligent applications
• This is unprecedented in the history of AI

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So, let's have a look at the semantic web as
it is today.
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Charting the web
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Charting the web (2)
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Proposition 2

• The SW may well provide a solution to one of the
  classic AI challenges how to construct and
  manage large volumes of knowledge to construct
  truly intelligent problem solvers and address the
  brittleness of traditional KBS

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1982
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Knowledge Representation Hypothesis

• Any mechanically embodied intelligent process
  will be comprised of structural ingredients that
• we as external observers naturally take to
  represent a propositional account of the
  knowledge that the overall process exhibits, and
• independent of such external semantic
  attribution, play a formal but causal and
  essential role in engendering the behaviour that
  manifests that knowledge
• Brian Smith, 1982

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Intelligence as a function of possessing domain
knowledge
KA Bottleneck
Intelligent Behaviour
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The Knowledge Acquisition Bottleneck
KA Bottleneck
Intelligent Behaviour
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SW as Enabler of Intelligent Behaviour
Intelligent Behaviour
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KBS vs SW Systems
Classic KBS SW Systems
Representation 'Clean' 'Dirty'
Size Small/Medium Extra Huge
Repr. Schema Homogeneous Heterogeneous
Quality High Very Variable
Degree of trust High Very Variable
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Key Paradigm Shift
Classic KBS SW Systems
Intelligence A function of sophisticated, task-centric problem solving A side-effect of size and heterogeneity (Collective Intelligence)
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Overall Goal
Our research programme is to contribute to the
development of this large-scale web of data and
develop a new generation of web applications able
to exploit it to provide intelligent
functionalities
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So, how can we exploit this emerging, large scale
semantic resource?

• Some examples.

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Ontology Matching
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New paradigm use of background knowledge
Background Knowledge (external source)
R
B
A
A
B
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External Source One Ontology

• Aleksovski et al. EKAW06
• Map (anchor) terms into concepts from a richly
  axiomatized domain ontology
• Derive a mapping based on the relation of the
  anchor terms

Assumes that a suitable (rich, large) domain
ontology (DO) is available.
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External Source Web

• van Hage et al. ISWC05
• rely on Google and an online dictionary in the
  food domain to extract semantic relations between
  candidate terms using IR techniques

OnlineDictionary
Does not rely on a rich Domain Ont,
IR Methods
Precision increases significantly if domain
specific sources are used 50 - Web 75 -
domain texts.
rel
A
B
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External Source SW

• Proposal
• rely on online ontologies (Semantic Web) to
  derive mappings
• ontologies are dynamically discovered and
  combined

Semantic Web
Does not rely on any pre-selected knowledge
sources.
rel
A
B
M. Sabou, M. dAquin, E. Motta, Using the
Semantic Web as Background Knowledge inOntology
Mapping", Ontology Mapping Workshop, ISWC06.
Best Paper Award
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• How to combine online ontologies to derive
  mappings?

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Strategy 1 - Definition
Find ontologies that contain equivalent classes
for A and B and use their relationship in the
ontologies to derive the mapping.
For each ontology use these rules
B1
B2
Bn
Semantic Web

An
A1
A2
O2
On
O1
These rules can be extended to take into account
indirect relations between A and B, e.g.,
between parents of A and B
rel
A
B
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Strategy 1- Examples
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Strategy 2 - Definition
Principle If no ontologies are found that
contain the two terms then combine information
from multiple ontologies to find a mapping.
Details (1) Select all ontologies containing
A equiv. with A (2) For each ontology
containing A (a) if find
relation between C and B. (b) if
find relation between C and B.
Details (1) Select all ontologies containing
A equiv. with A (2) For each ontology
containing A (a) if find
relation between C and B. (b) if
find relation between C and B.
B
rel
C
Semantic Web
rel
B
C
A
rel
A
B
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Strategy 2 - Examples
Ex1
Vs.
(r1)
(midlevel-onto)
(Tap)
(Same results for Duck, Goose, Turkey)
Vs.
Ex2
(pizza-to-go)
(r1)
(SUMO)
Vs.
Ex3
(pizza-to-go)
(r3)
(wine.owl)
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Large Scale Evaluation
Matching AGROVOC (16k terms) and NALT(41k terms)
(derived from 180 different ontologies)
Evaluation 1600 mappings, two teams Average
precision 70 (comparable to best in class)
M. Sabou, M. dAquin, W.R. van Hage, E. Motta,
Improving Ontology Matching by Dynamically
Exploring Online Knowledge.
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Chart 2
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Proposition 3

• Using the SW to provide dynamically background
  knowledge to tackle the Agrovoc/NALT mapping
  problem provides the first ever test case in
  which the SW, viewed as a large scale
  heterogeneus resource, has been successfully used
  to address a real-world problem

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Next Generation Semantic Web Applications
NG SW Application

• Able to exploit the SW at large
• Dynamically retrieving the relevant semantic
  resources
• Combining several, heterogeneous Ontologies

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Contrast with 1st generation SW Applications

• Typically use a single ontology
• Usually providing a homogeneous view over
  heterogeneous data sources.
• Limited use of existing SW data
• Typically closed to semantic resources

1st generation SW applications are far more
similar to traditional KBS (closed semantic
systems) than to 'real' SW applications (open
semantic systems)
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It is still early days..
1895
2007
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Current Gateway to the Semantic Web
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Limitations of Swoogle

• Very limited quality control mechanisms
• Many ontologies are duplicated
• No quality information provided
• Limited Query/Search mechanisms
• Only keyword search no distinction between types
  of elements
• need for more powerful query methods (e.g.,
  ability to pose formal queries ability to
  distinguish between classes and instances, etc)
• Limited range of ontology ranking mechanisms
• Swoogle only uses a 'popularity-based' one
• No support for ontology modularization

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A New Gateway to the Semantic Web
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Ontology Structuring Relations
inconsistent-with
extends
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Ontology Structuring Relations
inconsistent-with
Inconsistent-with
extends
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Formal Queries and relation discovery
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Current state of Watson

• Initial version implemented
• Demo version available online
• See http//watson.kmi.open.ac.uk/
• However still rather unstable..
• Stable version to be available within 4-6 weeks
• Initial crawl of the SW has already produced
  interesting results.

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Some initial figures

• Lots of ontologies are in OWL FULL (3x the number
  of OWL Lite)
• but most of the ontologies use only a very
  restricted sub-part of the expressivity of OWL
  and DAML, e.g.,
• only 147 go beyond ALC
• role transitivity is used in only 11
  ontologies..
• Almost 20 of semantic resources appear to be
  duplicates

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Next Generation Semantic Web Applications
PowerMagpie
PowerAqua
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Folksonomies
Tags are great to organize data!!!
But they dont help much when searching
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Finding tagged images
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Finding tagged images FLOWER
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What if
folksonomies were semantically richer
Flower
Tulip
Lilac
Rose
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Finding tagged images FLOWER (II)
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Learning Relations Between Tags
Tags
NLP/Clustering
camera, digital, photograph damage, flooding,
hurricane, katrina, Louisiana
Clusters
Find and combine Online ontologies
Ontology
L.Specia, E. Motta, "Integrating Folksonomies
with the Semantic Web", ESWC 2007.
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In More Detail
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Examples
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Examples
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Examples
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Key Research Tasks

• Overall Infrastructure
• crawling, storing, structuring, querying the SW
• Ontology Selection
• In the context of dynamically identifying the
  sources of knowledge relevant to the needs of a
  system
• Ontology Mapping
• When integrating information from different
  ontologies
• When mapping query/specs to ontologies
• Ontology Modularization
• Find the sub-modules relevant to a system's
  query.
• Semantic Markup Generation
• From various types of sources

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New task context

• Key point is that NG-SW applications require
  solutions in a new dynamic context (run-time
  rather than design-time)
• Example Ontology Mapping
• Much current work focuses on design-time mapping
  of complete ontologies
• Example Ontology Selection
• Current work focuses on user-mediated ontology
  selection
• Example Ontology Modularization
• Current work by and large assumes that the user
  is in the loop

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References

• Ontology Selection
• Sabou, M., Lopez, V., Motta, E. (2006). "Ontology
  Selection for the Real Semantic Web How to Cover
  the Queens Birthday Dinner?". Proceedings of
  EKAW 2006
• Ontology Modularization
• D'Aquin, M., Sabou, M., Motta, E. (2006).
  "Modularization A key for the dynamic selection
  of relevant knowledge components". ISWC 2006
  Workshop on Ontology Modularization
• Watson
• dAquin, M., Sabou, M., Dzbor, M., Baldassarre,
  C., Gridinoc, L., Angeletou, S. and Motta, E.
  "WATSON A Gateway for the Semantic Web". Poster
  Session at ESWC 2007

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References (2)

• Ontology Mapping
• Lopez, V., Sabou, M., Motta, E. (2006). "Mapping
  the real semantic web on the fly". ISWC 2006
• Sabou, M., D'Aquin, M., Motta, E. (2006). "Using
  the semantic web as background knowledge for
  ontology mapping". ISWC 2006 Workshop on Ontology
  Mapping.
• Intg. of folksonomies and SW
• L.Specia, E. Motta, "Integrating Folksonomies
  with the Semantic Web", ESWC 2007.

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'Vision' Papers

• Motta, E., Sabou, M. (2006). "Next Generation
  Semantic Web Applications". 1st Asian Semantic
  Web Conference, Beijing.
• Motta, E., Sabou, M. (2006). "Language
  Technologies and the Evolution of the Semantic
  Web". LREC 2006, Genoa, Italy.
• Motta, E. (2006). "Knowledge Publishing and
  Access on the Semantic Web A Socio-Technological
  Analysis". IEEE Intelligent Systems, Vol.21, 3,
  (88-90).

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Conclusions

• SW provides an unprecedented opportunity to build
  a new generation of intelligent systems, able to
  exploit large scale, heterogeneous KBs
• This new class of systems is fundamentally
  different in many respects both from traditional
  KBS and even from early SW applications
• The size of the SW is increasing steadily and the
  infrastructure is getting more and more robust.
  These developments should enable more and more
  new generation SW applications to emerge within
  2-3 years

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Current Gateway to the Semantic Web