Next Generation Semantic Web Applications

1
Next Generation Semantic Web Applications

• Enrico Motta, Mathieu DAquinSofia Angeletou,
  Claudio Baldassarre, Martin Dzbor, Laurian
  Gridinoc, Davide Guidi, Ainhoa Llorente, Vanessa
  Lopez, Marta Sabou
• Knowledge Media InstituteThe Open
  UniversityMilton Keynes, UK

2
Introduction

• This talk presents a number of projects, which
  are part of an integrated effort at exploring the
  possibilities opened by the Semantic Web, viewed
  as a domain-independent, large scale supplier of
  formally encoded background knowledge, with
  respect to enabling intelligent problem solving.
• We call the resulting applications
• Next Generation Semantic Web Applications

3
Organization of the Talk

• The Semantic Web
• The Semantic Web in the context of AI research
• Next Generation Semantic Web Applications
• What are they?
• Why are they different from 1st generation SW
  Applications?
• Infrastructure needs
• Examples
• Ontology Matching
• Integrating Web2.0 and SW
• Semantic Web Browsing
• Question Answering
• Conclusions

4
The Semantic Web

• The collection of all formal, machine
  processable, web accessible, ontology-based
  statements (semantic metadata) about web
  resources and other entities in the world,
  expressed in a knowledge representation language
  based on an XML syntax (e.g., OWL, DAML,
  DAMLOIL, RDF, etc)

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Ontology
Metadata
UoD
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Semantic Web Document
ltfoafPersonalProfileDocument rdfabout"http//km
i.open.ac.uk/people/rdf.cfm/idstring/sofia-angelet
ou/"gt ltdctitlegtSofia Angeletouaposs RDF
Descriptionlt/dctitlegt ltrdfslabelgtSofia
Angeletous RDF Descriptionlt/rdfslabelgt
ltdcdescriptiongtRDF description for Sofia
Angeletou in machine-readable RDF/XMLlt/dcdescript
iongt ltdccreator rdfresource"http//identifier
s.kmi.open.ac.uk/people/sofia-angeletou/" /gt
ltfoafmaker rdfresource"http//identifiers.kmi.o
pen.ac.uk/people/sofia-angeletou/"/gt
ltfoafprimaryTopic rdfresource"http//identifier
s.kmi.open.ac.uk/people/sofia-angeletou/"/gt lt/foaf
PersonalProfileDocumentgt ltfoafPerson
rdfabout"http//identifiers.kmi.open.ac.uk/peopl
e/sofia-angeletou/"gt ltfoafnamegtSofia
Angeletoult/foafnamegt ltfoaffirstNamegtSofialt/foa
ffirstNamegt ltfoafsurnamegtAngeletoult/foafsurna
megt ltfoafmbox_sha1sumgtF78114D4E45CFC6AC811E6191
F50182FB9838938lt/foafmbox_sha1sumgt ltfoafphone
rdfresource"tel[phone]"/gt
ltfoafjabberIDgts.angeletouopen.ac.uk_at_msg.open.ac.
uklt/foafjabberIDgt ltfoafhomepage
rdfresource"http//"/gt ltfoafpublications
rdfresource"http//kmi.open.ac.uk/publications/p
ublications.cfm?id167"/gt ltfoafworkplaceHomepag
e rdfresource"http//kmi.open.ac.uk/"/gt
ltfoafworkInfoHomepage rdfresource"http//kmi.op
en.ac.uk/people/index.cfm?id167"/gt
ltfoafdepiction rdfresource"http//kmi.open.ac.u
k/img/members/Sofia4F8E0276.jpg"/gt
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Increasing Semantic Content
ltrdfRDFgt ltFeature rdfabout"http//sws.geonames.
org/2638049/"gt ltnamegtShenley Church
Endlt/namegt ltalternateNamegtShenleylt/alternateNamegt
ltinCountry rdfresource"http//www.geonames.org/c
ountries/GB"/gt lt/rdfRDFgt
9
Charting the web
10
Charting the web (2)
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Domain Coverage on the SW

• Great variety Some topics are almost not covered
  (e.g. Adult), while some are over represented
  (e.g. Society, Computers)
• As we can expect, a large number of narrow
  coverage documents and a small number of large
  coverage ones.

Distribution of documents in the 16 top
categories of DMOZ
Distribution of the documents according to their
coverage
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Example Annotating the queen's birthday dinner
ltRDF triplegt ltRDF triplegt ltRDF triplegt ltRDF
triplegt ltRDF triplegt ltRDF triplegt ltRDF
triplegt ltRDF triplegt ltRDF triplegt ltRDF
triplegt ltRDF triplegt ltRDF triplegt
13
Knowledge Sparseness

• Nr.(t1, t2, t3)
  (t1) (t2) (t3) (t1, t2) (t1, t3)
  (t2, t3) (t1, t2, t3)
• 1 (project, article, researcher)
  84 90 24 9 13 9
  8
• 2 (researcher, student, university)
  24 101 64 16 15 38 13
• (research, publication, author) 15
  77 138 8 5 36 4
• 4 (adventurer, expedition, photo)
  1 0 32 0 1 0
  0
• 5 (mountain, team, talk)
  12 25 9 2 1 1
  1
• 6 (queen, birthday, dinner)
  0 9 2 0 0 1
  0
• 7 (project, relatedTo, researcher)
  84 11 24 0 13 0 0
• 8 (researcher, worksWith, Ontology) 24
  9 52 0 3 0 0
• 9 (academic, memberOf, project) 21
  36 84 0 3 5 0
• 10 (article, hasAuthor, person)
  90 14 371 8 32 2 0
• 11 (person, trip, photo)
  371 7 32 1 20 1
  1
• 12 (woman, birthday, dinner)
  32 9 2 1 1 1
  1
• 13 (person, memberOf, project) 371
  36 84 16 46 5 5
• 14 (publication, hasAuthor, person) 77
  14 371 2 52 2 2

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Example Annotating the queen's birthday dinner
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The Rise of Semantics
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Thesis 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
• In other words the Semantic Web is no longer an
  aspiration but a reality
• The availability of such large scale amounts of
  formalised knowledge is unprecedented in the
  history of AI

17
Thesis 2

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

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Knowledge Representation Hypothesis in AI

• 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
20
The Knowledge Acquisition Bottleneck
KA Bottleneck
Intelligent Behaviour
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SW as Enabler of Intelligent Behaviour
Intelligent Behaviour
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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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First Generation Semantic Web 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

Hence current SW applications are 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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Next Generation Semantic Web Applications
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Architecture of NGSW Apps
Semantic Web Gateway
34
Issue Semantic Web Infrastructure
35
Current Gateway to the Semantic Web
36
Limitations of Swoogle

• Limited quality control mechanisms
• Many ontologies are duplicated
• Limited Query/Search mechanisms
• Only keyword search no distinction between types
  of elements
• No support for formal query languages (such as
  SPARQL)
• Limited range of ontology ranking mechanisms
• Swoogle only uses a 'popularity-based' one
• Limited API
• No support for ontology modularization and
  combination

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A New Gateway to the Semantic Web
http//watson.kmi.open.ac.uk
38

• Sophisticated quality control mechanism
• Detects duplications
• Fixes obvious syntax problems
• E.g., duplicated ontology IDs, namespaces, etc..
• Structures ontologies in a network
• Using relations such as extends,
  inconsistentWith, duplicates
• Provides efficient API
• Supports formal queries (SPARQL)
• Variety of ontology ranking mechanisms
• Modularization/Combination support
• Plug-ins for Protégé and NeOn Toolkit (under
  devpt.)
• Very cool logo!

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Networked Ontologies
M1
M2
target
source
source
priorVersionOf
priorVersionOf
relatedWith
O1
O1
O2
O1
incompatibleWith
dependsOn
extends
O3
O4
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• Sophisticated quality control mechanism
• Detects duplications
• Fixes obvious syntax problems
• E.g., duplicated ontology IDs, namespaces, etc..
• Structures ontologies in a network
• Using relations such as extends,
  inconsistentWith, duplicates
• Provides efficient API
• Supports formal queries (SPARQL)
• Variety of ontology ranking mechanisms
• Modularization/Combination support
• Plug-ins for Protégé and NeOn Toolkit (under
  devpt.)
• Very cool logo!

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Examples of Next Generation Semantic Web
Applications
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Example 1 Ontology Matching
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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
51
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.
52
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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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
Semantic Web
B1
B2
Bn

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
56
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 Overall
performance comparable to best in class
M. Sabou, M. dAquin, W.R. van Hage, E. Motta,
Improving Ontology Matching by Dynamically
Exploring Online Knowledge. In Press
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Chart 2
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Thesis 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
  heterogeneous resource, has been successfully
  used to address a real-world problem

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Thesis 4

• The claim that the information on the SW is of
  poor quality and therefore not useful to support
  intelligent problem solving is a myth not
  supported by concrete experience
• Our experience in the NALT/Agrovoc ontology
  matching benchmark problem shows that without any
  particularly intelligent filter, the info
  available on the SW already allows a 85
  theoretical precision for our algorithm, well
  beyond the performance of any other ontology
  matching algorithm

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Example 2 Integrating SW and Web2.0
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Features of Web2.0 sites

• Tagging as opposed to rigid classification
• Dynamic vocabulary does not require much
  annotation effort and evolves easily
• Shared vocabulary emerge over time
• certain tags become particularly popular

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Limitations of tagging

• Different granularity of tagging
• rome vs colosseum vs roman monument
• Flower vs tulip
• Etc..
• Multilinguality
• Spelling errors, different terminology, plural vs
  singular, etc
• This has a number of negative implications for
  the effective use of tagged resources
• e.g., Search exhibits very poor recall

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Giving meaning to tags
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What does it mean to add semantics to tags?

• 1. Mapping a tag to a SW element
• "japan"
  ltaktCountry Japangt

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Applications of the approach

• To improve recall in keyword search
• To support annotation by dynamically suggesting
  relevant tags or visualizing the structure of
  relevant tags
• To enable formal queries over a space of tags
• Hence, going beyond keyword search
• To support new forms of intelligent navigation
• i.e., using the 'semantic layer' to support
  navigation

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Folksonomy
Clustering
Analyze co-occurrence of tags
Co-occurence matrix
Cluster tags
Cluster1
Cluster2
Clustern

Concept and relation identification
Yes
2 related tags
SW search engine
Remaining tags?
Wikipedia
Find mappings relation for pair of tags
No
Google
END
ltconcept, relation, conceptgt
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Pre-processing

• Scope Subsets of Flickr and del.icio.us tags.
• Pre-processing (thresholds)
• To be similar, Levenshtein gt 0.83
• A tag has to occur at least 10 times.

Total Total Distinct Distinct Distinct
entries tags users resources tags
del.icio.us 19,605 89,978 7,164 14,211 11,960
Flickr 49,087 167,130 6,140 49,087 17,956
Total Total Distinct Distinct Distinct
entries tags users resources tags
del.icio.us 18,882 70,194 7,090 13,579 1,265
Flickr 44,032 127,098 5,321 44,032 2,696
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Clustering

1. Each pair of similar tags, as determined by a
   co-occurrence analysis (e.g., audio and mp3), is
   a seed constituting an initial cluster
2. The cluster is enlarged by including tags that
   are similar to both the initial tags
3. Repeat procedure recursively for all tags each
   new candidate tag for a cluster must be similar
   to the whole (possibly enlarged) set of tags in
   that cluster.
4. If there are no more candidates for the cluster,
   go to step 1 with a new seed (e.g., audio and
   music).

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Clustering
audio semantic-web adult apple chat
1 mp3 rdf girls mac aim
2 music ontology nude macintosh messenger
3 playlist owl babes tiger gtalk
4 streaming semweb pics osx msn
5 radio daml sex macosx icq
? Fruit
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Combining Clusters

• Smoothing heuristics are applied to avoid having
  a number of very similar clusters
• originated from distinct seeds that are similar
  amongst each other.
• For every two clusters
• If one cluster contains the other, i.e., if the
  larger cluster contains all the tags of the
  smaller, remove the smaller cluster
• If clusters differ within a small margin, i.e.,
  the number of different tags in the smaller
  cluster represents less than a percentage of the
  number of tags in the smaller and larger
  clusters, add the distinct words from the smaller
  to the larger cluster and remove the smaller.

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Extracting relations

• For each pair of tags for which the search engine
  retrieved information, investigate the possible
  relationships
• A tag can be an ancestor of the other. For
  example, in the FOOD ontology, apple is a
  subclass of fruit.
• A tag is the range or the value of a property of
  another tag. E.g., Class Zinfandel has a property
  hasColor, with value red
• Both tags have the same direct parent apple and
  pear are subclasses of fruit
• Both tags have the same ancestors assembly has
  as ancestors building (1st level) and
  construction (2nd), while formation has
  fabrication (1st) and construction (2nd) in
  WordNet.

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Examples
Cluster_1 admin application archive collection component control developer dom example form innovation interface layout planning program repository resource sourcecode
76
Examples
Cluster_2 college commerce corporate course education high instructing learn learning lms school student
1http//gate.ac.uk/projects/htechsight/Employment.
daml. 2http//reliant.teknowledge.com/DAML/Mid-lev
el-ontology.daml. 3http//www.mondeca.com/owl/mos
es/ita.owl. 4http//www.cs.utexas.edu/users/mfkb/R
KF/tree/CLib-core-office.owl.
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Faceted Ontology

• Ontology creation and maintenance is automated
• Ontology evolution is driven by task features and
  by user changes
• Large scale integration of ontology elements from
  massively distributed online ontologies
• Very different from traditional top-down-designed
  ontologies

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Lessons Learnt

• Approach proven to be feasible and promising.
• However
• Assumptions in initial experiments (e.g., single
  ontology coverage for pairs of tags focus on
  classes, clustering-based approach, etc..) too
  restrictive
• Swoogle is too limited to support a fully
  automated approach
• we are now using Watson for the current
  experiments
• Integration with SW-enabled ontology matching
  algorithm is essential to improve term matching

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Example 3 Semantics-Enhanced Web Browsing
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Magpie Architecture
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PowerMagpie Architecture
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Example 4 Question Answering on the Semantic
Web
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Aqualog QA for Corporate Semantic Webs
ltaktPerson rdfabout"aktPeterScott"gt
ltrdfslabelgtPeter Scottlt/rdfslabelgt
ltakthasAffiliation rdfresource"aktTheOpenUnive
rsity"/gt ltakthasJobTitlegtkmi deputy
directorlt/akthasJobTitlegt ltaktworksInUnit
rdfresource"aktKnowledgeMediaInstitute"/gt
ltakthasGivenNamegtPeterlt/akthasGivenNamegt
ltakthasFamilyNamegtScottlt/akthasFamilyNamegt
ltakthasPrettyNamegtPeter Scottlt/akthasPrettyNamegt
ltakthasPostalAddress rdfresource"aktKmiPosta
lAddress"/gt ltakthasEmailAddressgtpeter.scott_at_open
.ac.uklt/akthasEmailAddressgt ltakthasHomePage
rdfresource"http//kmi.open.ac.uk/people/scott/
"/gt lt/aktPersongt
Which KMi researcherswork on the Semantic Web?
Answer
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An Ontology-Modular System
Which premiership footballershave played for
Leeds and Chelsea?
ltftbFootballer rdfaboutftbWayneRooney"gt
ltrdfslabelgtWayne Rooneylt/rdfslabelgt
ltftbplaysFor ftbManUnitedgt ltftbhasPosition
ftbForwardgt ltftbhasPreviousClub ftbEvertongt
lt/ftbFootballergt ltftbFootballer
rdfaboutftbDavidBeckham"gt ltrdfslabelgtDavid
Beckhamlt/rdfslabelgt ltftbplaysFor
ftbRealMadridgt ltftbhasPosition
ftbRightMidfieldgt ltftbhasPreviousClub
ftbManUnitedgt lt/ftbFootballergt
AquaLog
Answer
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Coarse-grained Architecture
Linguistic Component obtains intermediate
representation from the input query Relation
Similarity Service maps the intermediate
representation to the ontology/kb
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Relation Similarity Service
Which are the KMi researchers in the semantic web
area?
Translated query
Ontological structures
KMi researchers (person/organization, semantic
web area)
Has-research-interest (kmi-research-staff-member,
Semantic-web-area)
MECHANISMS Ontology relationships and taxonomy,
String algorithms, WordNet, Learning Mechanism,
Users feedback

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Learning Mechanism
Which academics
work
in Akt ?
academic
project
User Lexicon
Which academics
work
in Akt ?
User Disambiguation
Ontology Concepts
project
Has-project-member
academic
Mapping
work
has-project-member
(inverse-of)
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Learning Mechanism
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Interpretation Mechanisms Ontology structure,
String algorithms, WordNet, Machine Learning,
Users feedback
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User Feedback
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AquaLog --gt PowerAqua
ltaktPerson rdfabout"aktPeterScott"gt
ltrdfslabelgtPeter Scottlt/rdfslabelgt
ltakthasAffiliation rdfresource"aktTheOpenUnive
rsity"/gt lt/aktPersongt
NL Query
ltftbFootballer rdfaboutftbWayneRooney"gt
ltrdfslabelgtWayne Rooneylt/rdfslabelgt
ltftbplaysFor ftbManUnitedgt ltftbhasPosition
ftbForwardgt ltftbhasPreviousClub ftbEvertongt
lt/ftbFootballergt
ltptbBuilder rdfaboutptbBobgt ltrdfslabelgtBob
the Builderlt/rdfslabelgt ltptbplayedBygt
lt/ptbBuildergt
PowerAqua
Answer
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PowerAqua vs AquaLog

• Challenges when consulting and aggregating
  (dynamically mapping) information derived from
  multiple heterogeneous ontologies
• Locating the right ontologies
• Intra-ontology semantic relevance analysis
• Filtering the right mappings
• Intg. heterogeneous information to provide an
  answer
• This reduces to deciding whether two instances
  specified according to different ontologies
  denote the same entity

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Conclusions

• SW provides an unprecedented opportunity to build
  a new generation of intelligent systems, able to
  exploit large scale background knowledge
• The large scale background knowledge provided by
  the SW may address one of the fundamental
  premises (and holy grails) of AI
• The SW is not an aspiration it is a concrete
  technology that is already in place today and is
  steadily becoming larger and more robust
• The new class of systems enabled by the SW is
  fundamentally different in many respects both
  from traditional KBS and even from early SW
  applications
• The examples shown in this talk provide an
  initial taste of the new generation of
  applications which will be made possible by the
  emerging Semantic Web

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References

• 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.
• Integration of Web2.0 and Semantic Web
• L.Specia, E. Motta, "Integrating Folksonomies
  with the Semantic Web", ESWC 2007.
• Angeletou, S., Sabou, M., Specia, L., and Motta,
  E., (2007). Bridging the Gap Between
  Folksonomies and the Semantic Web An Experience
  Report. ESWC 2007 Workshop on Bridging the Gap
  between Semantic Web and Web 2.0.
• 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

106
'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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