Next Generation Semantic Web Applications

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Next Generation Semantic Web Applications

• Prof. Enrico MottaDirector, Knowledge Media
  InstituteThe Open UniversityMilton Keynes, UK

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Structure of the Talk

• Quick Recap What is the Semantic Web?
• State of the art 1st Generation SW Applications
• Emphasis on ontology-driven data aggregation
• Limited with respect to their ability to exploit
  large scale, heterogeneous semantic markup
• Key research issues
• What needs to be done to enable the effective
  development of the next generation of SW
  Applications
• Need for a different approach to some key res.
  areas
• How the SW itself can be exploited to address
  such key research issues

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Quick Recap What is the Semantic Web?
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The Semantic Web

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

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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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hasAffiliation
Organization
Person
worksInOrgUnit
partOf
hasJobTitle
String
Organization-Unit
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SW A Conceptual Layer over the web
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SW is Heterogeneous!
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Generating semantic markup
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
ltRDF triplegt ltRDF triplegt ltRDF triplegt ltRDF
triplegt ltRDF triplegt ltRDF triplegt
ltRDF triplegt ltRDF triplegt ltRDF triplegt ltRDF
triplegt ltRDF triplegt ltRDF triplegt
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Key aspects of the SW

• Size ( Huge)
• Sem. markup (eventually to reach) the same order
  of magnitude as the web
• Conceptual Heterogeneity ( Big)
• Sem. markup based on many different ontologies
• Rate of change ( Very High)
• Data generated all the time from human and
  artificial agents
• Provenance ( Very Heterogeneous)
• .Hence provenance itself is extremely
  heterogeneous
• Trust ( very variable and subjective)
• A side-effect of heterogeneous provenance
• Data Quality ( very variable)
• No guarantee of correctness
• Intelligence ( by-product of size and
  heterogeneity)
• Rather than a by-product of sophisticated problem
  solving

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Compare with traditional KBS

• Size ( Small or Medium)
• KBS normally small to medium size
• Conceptual Heterogeneity ( Not an issue)
• KBS normally based on a single conceptual model
• Rate of change ( Very Low)
• Change rate under developers' control (hence,
  low)
• Provenance ( Not an issue)
• KBS are normally created ad hoc for an
  application by a centralised team of developers
• Trust ( not a major issue)
• Centralisation of devpt. process implies no
  significant trust issues
• Data Quality ( not a major issue)
• Again, centralisation guarantees data quality
  across the board
• Intelligence ( by-product of complex,
  task-centric reasoning)
• E.g., sophisticated diagnostic, planning systems

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The Semantic Web today

• 1st Generation SW Applications

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Bibliographic Data
CS Dept Data
AKT Reference Ontology
RDF Data
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Features of 1st generation SW Applications

• Typically use a single ontology
• Usually providing a homogeneous view over
  heterogeneous data sources Limited use of
  existing SW data
• Closed to semantic resources
• Limited interactivity
• In contrast with typical web 2.0 applications

Hence current 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
2006
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Next Generation SW Applications
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Next generation SW applications
NG SW Application

• Able to exploit the SW at large
• Hence Multi-Ontology
• Supporting interactivity
• E.g., allowing users to add semantic data
• Hence, open with respect to SW resources
• Ideally also able to exploit non-SW data
• E.g., folksonomies
• Hence, embedding powerful information extraction
  engines

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Two systems we have built
Magpie
AquaLog
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Magpie Components
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AquaLog Ontology-Driven Question Answering

• Which is the capital of Spain?

Madrid
NL SENTENCE INPUT
ANSWER
(?, capital, Spain)
ltSpain, has-capital-city, Madridgt
QUERY TRIPLES
RESULT TRIPLES
Linguistic Analysis
Mapping Engine
NL Generation
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PowerMagpie Semantic browsing on the 'open' SW
Need for mechanisms for automatically
identifying semantic markup relevant to the
current page, user, browsing session, etc..
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PowerAqua QA on the 'open' semantic web
Need for mechanisms for automatically locating
ontologies relevant to the current query, map
user terminology to ontologies,integrate info
from different ontologies, etc..
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What needs to be done to facilitate the
development of such 2nd generation SW
applications?
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Dynamic Ontology Selection

• First powerful support for ontology selection
• Both PowerAqua and PowerMagpie heavily rely on
  ontology selection to locate possibly relevant
  knowledge in response to
• User queries (PowerAqua)
• Accessing web pages (PowerMagpie)
• Hence, ontology selection is a crucial task for
  both systems

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Current support for ontology selection
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Limitations of Swoogle

• Query/Search
• Only keyword search, we need more powerful query
  methods (e.g., ability to pose formal queries)
• Repository structure
• Very weak in Swoogle, not even duplicates are
  dealt with
• Need for automatic derivation of relations
  between ontologies
• E.g., same-ontology-as, ontology-extends,
  ontology-incompatible-with, etc..
• We need these relations to structure the
  repository and to support more powerful ranking
  methods (see next bp)
• Ontology ranking
• Swoogle only uses a 'popularity-based' one, we
  need other methods as well

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We also need

• Methods for fast extraction of ontology modules
• Typically we only want the part of the ontology
  relevant to our current needs
• Methods for the integration of information
  derived from different ontologies
• In the context of QA this problem typically
  reduces to that of deciding whether two instances
  denote the same entity

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Even more importantly..

• Need to look at a number of key research issues
  in the context provided by NG-SW applications
• Example Ontology Mapping
• 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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A new application scenario

• NG-SW applications require algorithms able to
  perform tasks such as selecting, modularizing,
  and mapping ontologies at run time
• Moreover, in such a context, mapping is concerned
  with mapping ontology fragments, rather than
  complete ontologies

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So What?

• Time to go beyond 1st generation applications
• 2nd generation SW applications will exploit much
  more fully the large scale semantic markup
  provided by the SW
• Many issues to be addressed
• Better ontology crawling, indexing, retrieving
  and ranking support
• Mapping, selection, and modularization methods
  appropriate for NG-SW applications
• Further acceleration needed in the generation of
  semantic markup

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Exploiting the SW itself to tackle its
heterogeneity

• Interestingly, a NG-SW-based approach can also be
  used also to tackle key SW tasks, such as
  Ontology Mapping
• Based on the use of the SW itself as background
  knowledge

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Exploiting Large-Scale Semantics

• Case Study
• Using the Semantic Web as background knowledge in
  Ontology Mapping

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Ontology Mapping State of the Art

• State-of-the-art methods rely on a combination
  of
• Label similarity methods
• e.g., Full_Professor FullProfessor
• Structure similarity methods
• Using taxonomic information or information about
  domain and range of associated properties
• However, as pointed out by Aleksovski et al
  (EKAW, 2006)
• In many cases there is no sufficient lexical
  overlap
• In many cases source and target ontology have not
  sufficient structure to allow effective
  structure-based mapping

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Use of bkg. knowledge for ontology mapping
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External Source One Ontology

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

• Advantages
• Handles dissimilar ontologies
• Returns semantic mappings

B
rel
A

• Disadvantages
• Assumes that a suitable domain ontology is
  available.
• Approach only suitable for closed domains

rel
A
B
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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 mappings using IR techniques

• Advantages
• General purpose

OnlineDictionary
IR Methods

• Disadvantages
• IR Methods introduce noise

rel
A
B
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External Source WordNet

• Lopez et al. ESWC 05
• use wordnet to map queries expressed in the
  user's terminology to a domain ontology to
  support question answering

• Advantages
• General purpose

WordNet

• Disadvantages
• Knowledge sparseness
• Works best with concepts, not so useful with
  relations
• WordNet is not an ontology!!!

rel
A
B
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Knowledge-poor ontology mapping

• Actually isnt a bit strange that such complex
  and knowledge-poor methods are devised, when the
  SW already provides so much background
  knowledge?.

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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

• Advantages
• General purpose
• Does not introduce noise
• Works with any kind of domain entities (concepts,
  relations, instances)

rel
A
B
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Strategy 1 - Definition
Find ontologies that contain equivalent classes
to 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- Variants
Quick variant Stop as soon as a relation is
found
B1
Semantic Web
A1
O1
A
B
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Strategy 1- Variants
Precise variant Derive all possible mappings
from all ontologies and combine them into a final
mapping.

• Dealing with Contradictions
• Return all mappings even if contradictory
• Return a mapping only when there is no
  contradiction
• Return the most frequent mapping (i.e., the
  mapping derived from most ontologies)
• Return the mappings with 'higher authority'
  (based on metrics of ontology evaluation or
  trust)
• Try to combine mappings

B1
B2
Semantic Web
A1
A2
O1
O2
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.
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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Conclusions

• Using the SW as background knowledge for ontology
  mapping has several benefits
• Suitable for our NG-SW scenario as there is no
  need for design-time selection of a background
  knowledge
• Even when design-time selection is feasible, it
  is suitable for those cases where a suitable
  domain ontology cannot be found
• Reduces noise by exploiting only ontologies
• Can be tailored to handle multiple solutions
• Can be integrated with other approaches, based on
  lexical and structural analysis

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If you would like to find out more..

• '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).
• 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

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If you would like to find out more..

• Ontology Mapping
• Lopez, V., Sabou, M., Motta, E. (2006). "Mapping
  the real semantic web on the fly". International
  Semantic Web Conference, Georgia, Atlanta.
• Sabou, M., D'Aquin, M., Motta, E. (2006). "Using
  the semantic web as background knowledge for
  ontology mapping". ISWC 2006 Workshop on Ontology
  Mapping.
• 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, Podebrady, Czech Republic.

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