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Document Sample
Finding and Ranking
Knowledge on the
Semantic Web
Li Ding, Rong Pan, Tim Finin, Anupam
Joshi, Yun Peng and Pranam Kolari
University of Maryland,
Baltimore County
http://creativecommons.org/licenses/by-nc-sa/2.0/
This work was partially supported by DARPA contract F30602-97-1-0215, NSF
UMBC
an Honors University in Maryland
grants CCR007080 and IIS9875433 and grants from IBM, Fujitsu and HP.
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This talk
• Motivation
• Swoogle overview
• Bots navigate the Semantic Web
• Ranking Semantic Web content
• Use cases and applications
• Conclusions
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Google has made us smarter
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But what about our agents?
tell
register
A Google for knowledge on the Semantic
UMBC Web is needed by people and software agents
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This talk
• Motivation
• Swoogle overview
• Bots navigate the Semantic Web
• Ranking Semantic Web content
• Use cases and applications
• Conclusions
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title
• text
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Swoogle Architecture
data IR analyzer SWD analyzer interface
analysis Web Server
Web Service
SWD Cache SWD Metadata
metadata Agent Service
creation
SWD Reader
SWD
discovery The Web
Candidate
URLs Web Crawler
Swoogle 2: 340K SWDs, 48M triples, 5K SWOs, 97K classes,
55K properties, 7M individuals (4/05)
Swoogle 3: 700K SWDs, 135M triples, 7.7K SWOs, (11/05)
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Demo
1 Find “Time” Ontology
We can use a set of keywords to search
ontology. For example, “time, before, after”
are basic concepts for a “Time” ontology.
Demo
2(a) Digest “Time” Ontology (document view)
Demo
2(b) Digest “Time” Ontology (term view)
TimeZone
before
………….
intAfter
Demo
3 Find Term “Person”
Not capitalized! URIref is case sensitive!
Demo
4 Digest Term “Person”
167 different properties
562 different properties
Demo
5(a) Swoogle Today
Demo
5(b) Swoogle
Statistics
FOAF
Trustix
W3C
Stanford
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Swoogle’s Triple Store lets you shop
And check
out your
triples into
any of
several
reasoners
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Summary
2004
Swoogle (Mar, 2004) Automated SWD discovery
SWD metadata creation and search
Ontology rank (rational surfer model)
Swoogle watch
Web Interface
Ontology dictionary
Swoogle2 (Sep, 2004) Swoogle statistics
Web service interface (WSDL)
Bag of URIref IR search
Triple shopping cart
Better (re-)crawling strategies
2005 Better navigation models
Index instance data
Swoogle3 (July 2005) More metadata (ontology mapping
and OWL-S services)
Better web service interfaces
UMBC IR component for string literals
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This talk
• Motivation
• Swoogle overview
• Bots navigate the Semantic Web
• Ranking Semantic Web content
• Use cases and applications
• Conclusions
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The Semantic Web Onion
The “Semantic Web”
(About 10M documents)
Universal RDF Graph
Physically hosting knowledge
(About 100 triples per SWD in average)
RDF Document
triples modifying the same subject
Literal Class-instance
Molecule Finest lossless set of triples
Resource
Triple
Atomic knowledge block
Swoogle maintains metadata about objects in
different layers of the Semantic Web Onion.
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Semantic Web Navigation Model
sameNamespace, sameLocalname
Term Search
Extends class-property bond
1
RDF graph Resource
literal
SWT
2
uses 4 5
populates 3 defines
isUsedBy officialOnto
isPopulatedBy isDefinedBy
Web SWD rdfs:subClassOf
SWO
6 7
rdfs:seeAlso owl:imports
rdfs:isDefinedBy …
Document Search
Navigating the HTML web is simple; there’s just one kind of link.
The SW has more kinds of links and hence more navigation paths.
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Semantic Web Navigation Model
sameNamespace, sameLocalname
Term Search
Extends class-property bond
1
RDF graph Resource
literal
SWT
2
uses 4 5
populates 3 defines
isUsedBy officialOnto
isPopulatedBy isDefinedBy
Web SWD rdfs:subClassOf
SWO
6 7
rdfs:seeAlso owl:imports
rdfs:isDefinedBy …
Document Search
Relations in 1 and 3 and parts of 4 require a global view to discover
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This talk
• Motivation
• Swoogle overview
• Bots navigate the Semantic Web
• Ranking Semantic Web content
• Use cases and applications
• Conclusions
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Rank has its privilege
• Google introduced a new approach to ranking query
results using a simple “popularity” metric.
– It was a big improvement!
• Swoogle ranks its query results also
– When searching for an ontology, class or property,
wouldn’t one want to see the most used ones first?
• Ranking SW content requires different algorithms for
different kinds of SW objects
– For SWDs, SWTs, individuals, “assertions”,
molecules, etc…
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Google’s PageRank
• A page’s rank is a function of
how many links point to it and the
rank of the pages hosting those links.
• The “random surfer” model provides Jump to a
random page
the intuition:
(1) Jump to a random page
(2) Select and follow a random link on the
page and repeat until ‘bored’ yes
bored?
(3) If bored, go to (1) no
• Ranked pages by the relative
frequency with which they are visited. Follow a
random link
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Ranking Semantic Web Documents
• Target: a pure SW dataset
– Nodes: a collection of online SWDs (330K SWDs, 1.5%
are labeled as ontologies)
– Links: in addition to hyperlinks, term level relations are
generalized into TM, EX, IM.
• Rational surfer model (extension of weighted PageRank)
– Semantic content (term level relations) encoded into links
– rank of node iteratively spread via links
– weight/capacity of link vary according to link semantics
– propagate weight to imported ontologies
• Evaluation
– Method: Compare OntoRank with PageRank for
promoting ontologies even using the same Pure SW
Dataset
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An Example
http://www.w3.org/2000/01/rdf-schema
wPR =300 OntoRank =403
TM
TM http://xmlns.com/wordnet/1.6/
wPR =3 OntoRank =103
EX
http://xmlns.com/foaf/1.0/
TM
wPR =100 OntoRank =100
http://www.cs.umbc.edu/~finin/foaf.rdf
wPR =0.2 OntoRank =0.2
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Ontology Dictionary
• Motivation
– One ontology does not always provide all needed
vocabulary
– There could be many scenario that requires
assembling terms from multiple ontologies
• DIY ontology engineering
1. Search an appropriate class C
2. Search for popular properties used for modifying C’s
class instance
3. Go back to step 1 if more classes are needed
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Ranking Semantic Web Terms
• Pr(Term|Doc) can be measured by the normalized
value of the product of the term’s
– Popularity: how many SWDs is using the term.
– Frequency: how many times the term is used in the SWD
• SWDs are accessed non-uniformly by OntoRank
• TermRank estimates a term’s importance as
∑ Pr(Term|Doc) * OntoRank(Doc)
• Evaluation
– Compare TermRank with Term’s popularity for the top 10
highest rated terms and compose analytical evaluation.
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Class-Property Bonds
Class-Property Bond
(introduced by ontology) SWD1
• foaf:mbox
• foaf:name
foaf:mbox Class Definition
• rdfs:subClassOf -- foaf:Agent
Class-Property Bond • rdfs:label – “Person”
(introduced by instances) foaf:name rdfs:domain
• foaf:name
• dc:title
rdfs:domain
SWD2 SWD3
rdf:type
rdf:type owl:Class
foaf:Person
foaf:name rdfs:subClassOf
“Tim Finin” foaf:Agent
dc:title rdfs:comment
“Tim’s FOAF File” “a human being”
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This talk
• Motivation
• Swoogle overview
• Bots navigate the Semantic Web
• Ranking Semantic Web content
• Use cases and applications
• Conclusions
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Applications and use cases
• Supporting Semantic Web developers, e.g.,
– Ontology designers
– Vocabulary discovery
– Who’s using my ontologies or data?
– Etc.
• Searching specialized collections, e.g.,
– Proofs in Inference Web
– Text Meaning Representations of news stories in
SemNews
• Supporting SW tools, e.g.,
– Discovering mappings between ontologies
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This talk
• Motivation
• Swoogle overview
• Bots navigate the Semantic Web
• Ranking Semantic Web content
• Use cases and applications
• Conclusions
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Will it Scale? How?
Here’s a rough estimate of the data in RDF documents on the
semantic web based on Swoogle’s crawling
System/date Terms Documents Individuals Triples Bytes
Swoogle2 1.5x105 3.5x105 7x106 5x107 7x109
Swoogle3 2x105 7x105 1.5x107 7.5x107 1x1010
2005 2.5x105 5x106 5x107 5x108 5x1010
2008 5x105 5x107 5x108 5x109 5x1011
We think Swoogle’s centralized approach can be made to work
for the next few years if not longer.
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How much reasoning?
• SwoogleN (N<=3) does limited reasoning
– It’s expensive
– It’s not clear how much should be done
• More reasoning would benefit many use cases
– e.g., type hierarchy
• Recognizing specialized metadata
– E.g., that ontology A some maps terms from B to C
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Conclusion
• The web will contain the world’s knowledge in
forms accessible to people and computers
– We need better ways to discover, index, search and
reason over SW knowledge
• SW search engines address different tasks than
html search engines
– So they require different techniques and APIs
• Swoogle like systems can help create consensus
ontologies and foster best practices
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For more information
http://ebiquity.umbc.edu/
Annotated
in OWL
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