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Search Engines for Semantic Web Knowledge. Tim Finin University of Maryland, Baltimore County Joint work with Li Ding, Anupam Joshi, Yun Peng, Pranam Kolari, Pavan Reddivari, Sandor Dornbush, Rong Pan, Akshay Java, Joel Sachs, Scott Cost and Vishal Doshi.

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search engines for semantic web knowledge

Search Engines for Semantic WebKnowledge

Tim Finin

University of Maryland, Baltimore County

Joint work with Li Ding, Anupam Joshi, Yun Peng, Pranam Kolari, Pavan Reddivari, Sandor Dornbush, Rong Pan, Akshay Java, Joel Sachs, Scott Cost and Vishal Doshi

 http://creativecommons.org/licenses/by-nc-sa/2.0/ This work was partially supported by DARPA contract F30602-97-1-0215, NSF grants CCR007080 and IIS9875433 and grants from IBM, Fujitsu and HP.

this talk
This talk
  • Motivation
  • Semantic web 101
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • State of the Semantic Web
  • Conclusions
but what about our agents

tell

register

But what about our agents?

Agents still have a very minimal understanding of text and images.

this talk5
This talk
  • Motivation
  • Semantic web 101
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • State of the Semantic Web
  • Conclusions
xml helps
XML helps

“XML is Lisp\'s bastard nephew, with uglier syntax and no semantics. Yet XML is poised to enable the creation of a Web of data that dwarfs anything since the Library at Alexandria.”

-- Philip Wadler, Et tu XML? The fall of the relational empire, VLDB, Rome, September 2001.

semantic web adds semantics
Semantic Web adds semantics

“The Semantic Web will globalize KR, just as the WWW globalize hypertext”

-- Tim Berners-Lee

semantic web 101

<?xml version="1.0" encoding="utf-8"?>

<rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:foaf=http://xmlns.com/foaf/0.1/

xmlns:uni=http//ebiquity.umbc.edu/ontologies/uni/>

<uni:Student>

<foaf:name>Li Ding</foaf:name>

<foaf:mbox rdf:resource=“mailto:[email protected]”/>

</uni:Student>

</rdf:RDF>

foaf:name

Li Ding

uni:Student

rdf:type

Semantic Web 101
  • RDF/XML
  • rdf:RDF tag
  • namespaces  ontologies
  • Semantic graph, URIs as nodes & links
  • triples
but what about our agents9

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

Swoogle

tell

register

But what about our agents?

A Google for knowledge on the Semantic Web is needed by software agents and programs

this talk10
This talk
  • Motivation
  • Semantic web 101
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • State of the Semantic Web
  • Conclusions
slide11

http://swoogle.umbc.edu/

  • Running since summer 2004
  • 1.4M RDF documents, 250M RDF triples, 10K ontologies
swoogle architecture

Analysis

SWD classifier

Ranking

Index

Search Services

Semantic Web

metadata

IR Indexer

Web

Server

Web

Service

SWD Indexer

html

rdf/xml

Discovery

the Web

document cache

SwoogleBot

Semantic Web

Candidate

URLs

Bounded Web Crawler

Google Crawler

human

machine

Legends

Information flow

Swoogle‘s web interface

Swoogle Architecture
a hybrid harvesting framework
A Hybrid Harvesting Framework

true

Swoogle

Sample

Dataset

Manual submission

Inductive learner

would

Seeds R

Seeds M

Seeds H

Meta crawling

Bounded HTML crawling

RDF crawling

google

Google API call

crawl

crawl

the Web

this talk14
This talk
  • Motivation
  • Semantic web 101
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • State of the Semantic Web
  • Conclusions
applications and use cases
Applications and use cases
  • Supporting Semantic Web developers
    • Ontology designers, vocabulary discovery, who’s using my ontologies or data?, use analysis, errors,statistics, etc.
  • Searching specialized collections
    • Spire: aggregating observations and data from biologists
    • InferenceWeb: searching over and enhancing proofs
    • SemNews: Text Meaning of news stories
  • Supporting SW tools
    • Triple shop: finding data for SPARQL queries
slide17

80 ontologies were found that had these three terms

By default, ontologies are ordered by their ‘popularity’, but they can also be ordered by recency or size.

Let’s look at this one

slide18

Basic Metadata

hasDateDiscovered:  2005-01-17

hasDatePing:  2006-03-21

hasPingState:  PingModified

type:  SemanticWebDocument

isEmbedded:  false

hasGrammar:  RDFXML

hasParseState:  ParseSuccess

hasDateLastmodified:  2005-04-29

hasDateCache:  2006-03-21

hasEncoding:  ISO-8859-1

hasLength:  18K

hasCntTriple:  311.00

hasOntoRatio:  0.98

hasCntSwt:  94.00

hasCntSwtDef:  72.00

hasCntInstance:  8.00

slide21

These are the namespaces this ontology uses. Clicking on one shows all of the documents using the namespace.

All of this is available in RDF form for the agents among us.

slide24

10K terms associatged with “person”! Ordered by use.

Let’s look at foaf:Person’s metadata

umbc triple shop
UMBC Triple Shop
  • http://sparql.cs.umbc.edu/
  • Online SPARQL RDF query processing basedon HP’s Jena and Joseki with several interesting features
  • Selectable level of inference over model
  • Automatically finds SWDs for give queries using Swoogle backend database
    • Provide dataset creation wizard
    • Dataset can be stored on our server or downloaded
    • Tag, share and search over saved datasets
web scale semantic web data access
Web-scale semantic web data access

data access service

the Web

agent

Index RDF data

ask (“person”)

Search vocabulary

Search URIrefs

in SW vocabulary

inform (“foaf:Person”)

Compose query

ask (“?x rdf:type foaf:Person”)

Search URLs

in SWD index

Populate

RDF database

inform (doc URLs)

Fetch docs

Query local

RDF database

slide33

Who knows Anupam Joshi?

Show me their names, email address and pictures

slide35

No FROM clause!

Constraints on wherethe data comes from

slide36

PREFIX foaf: <http://xmlns.com/foaf/0.1/>

SELECT DISTINCT ?p2name ?p2mbox ?p2pix

WHERE {

?p1 foaf:name "Anupam Joshi" .

?p1 foaf:mbox ?p1mbox .

?p2 foaf:knows ?p3 .

?p3 foaf:mbox ?p1mbox .

?p2 foaf:name ?p2name .

?p2 foaf:mbox ?p2mbox .

OPTIONAL { ?p2 foaf:depiction ?p2pix } .

}

ORDER BY ?p2name

this talk45
This talk
  • Motivation
  • Semantic web 101
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • State of the Semantic Web
  • Conclusions
will it scale how
Will it Scale? How?

Here’s a rough estimate of the data in RDF documents on the semantic web based on Swoogle’s crawling

We think Swoogle’s centralized approach can be made to work for the next few years if not longer.

how much reasoning
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
this talk48
This talk
  • Motivation
  • Semantic web 101
  • Swoogle Semantic Websearch engine
  • Use cases and applications
  • State of the Semantic Web
  • Conclusions
conclusion
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
    • Swoogle is for Semantic Web 1.0
    • Semantic Web 2.0 will make different demands
slide50

For more information

http://ebiquity.umbc.edu/

Annotatedin OWL

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