UMBC an Honors University in Maryland 1 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  This work was partially supported by DARPA contract F , NSF grants CCR and IIS and grants from IBM, Fujitsu and HP.

UMBC an Honors University in Maryland 2 This talk Motivation Semantic web 101 Swoogle Semantic Web search engine Use cases and applications Conclusions

UMBC an Honors University in Maryland 3 Once there were only a few large computers

UMBC an Honors University in Maryland 4 Then there were many,

UMBC an Honors University in Maryland 5 All connected 24x7, Internet Cellular telephony IRDA Bluetooth Ultra Wide Band RFID and more to come

UMBC an Honors University in Maryland 6 Interoperating; tcp/ip ftp smtp rpc corba ssh http html xml gif jpg mpg mp3 pdf …

UMBC an Honors University in Maryland 7 Access to the world’s knowledge del.icio.us

UMBC an Honors University in Maryland 8 Google has made us smarter

UMBC an Honors University in Maryland 9 But what about our agents? tell register Agents still have a very minimal understanding of text and images.

UMBC an Honors University in Maryland 10 This talk Motivation Semantic web 101 Swoogle Semantic Web search engine Use cases and applications Conclusions

UMBC an Honors University in Maryland 11 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.

UMBC an Honors University in Maryland 12 “The Semantic Web will globalize KR, just as the WWW globalize hypertext” -- Tim Berners-Lee Semantic Web adds semantics

UMBC an Honors University in Maryland 13 Semantic Web 101 <rdf:RDF xmlns:rdf=" xmlns:foaf= xmlns:uni=http//ebiquity.umbc.edu/ontologies/uni/> Li Ding RDF/XML rdf:RDF tag namespaces  ontologies Semantic graph, URIs as nodes & links triples Li Ding foaf:name uni:Student rdf:type

UMBC an Honors University in Maryland 14 Where’s the semantics? URIs as “rigid designators” Conventions for URIs denoting things in the “real world” Namespaces and URIs provide an unambiguous shared vocabulary RDF, RDFS and OWL have semantics defined using model theory and also axioms Ontologies allow agents to draw inferences –uni:Student is a subclass of foaf:Person –Every uni:Student has at least one uni:school, which must be an instance of uni:School –A foaf:Person with a uni:school is necessarily a uni:Student

UMBC an Honors University in Maryland 15

UMBC an Honors University in Maryland 16

UMBC an Honors University in Maryland 17

UMBC an Honors University in Maryland 18 RDF/a RDF/a is a W3C proposal for embedding RDF in XHTML documents Jo Lambda's Home Page Hello. This is Jo Lambda 's home page. Work If you want to contact me at work, you can either me, or call <> foaf:name "Jo Lambda"^^rdf:XMLLiteral ; foaf:mbox ; foaf:phone " "^^rdf:XMLLiteral. An HTML Document with RDF embedded The triples in ntriple format.

UMBC an Honors University in Maryland 19 But what about our agents? A Google for knowledge on the Semantic Web is needed by software agents and programs Swoogle tell register

UMBC an Honors University in Maryland 20 This talk Motivation Semantic web 101 Swoogle Semantic Web search engine Use cases and applications Conclusions

UMBC an Honors University in Maryland 21

UMBC an Honors University in Maryland 22 Running since summer M RDF documents, 250M RDF triples, 10K ontologies Semantic Web archive: many dynamic RDF documents

UMBC an Honors University in Maryland 23 Analysis Index Discovery IR Indexer Search Services Semantic Web metadata Web Service Web Server Candidate URLs Bounded Web Crawler Google Crawler SwoogleBot SWD Indexer Ranking document cache SWD classifier human machine htmlrdf/xml … the Web Semantic Web Information flowSwoogle‘s web interface Legends Swoogle Architecture

UMBC an Honors University in Maryland 24 A Hybrid Harvesting Framework Manual submission RDF crawlingBounded HTML crawlingMeta crawling Seeds MSeeds H Seeds R Swoogle Sample Dataset Inductive learner the Web Google API call crawl true would google

UMBC an Honors University in Maryland 25 Performance – Site Coverage SW06MAR - Basic statistics (Mar 31, 2006) – 1.3M SWDs from 157K websites – 268M triples – 61K SWOs including >10K in high quality –1.4M SWTs using 12K namespaces Significance –Compare with existing works ( DAML crawler, scutter ) –Compare SW06MAR with Google ’ s estimated SWDs SWDs per website Website

UMBC an Honors University in Maryland 26 Performance – crawlers’ contribution High SWD ratio: 42% URLs are confirmed as SWD Consistent growth rate: 3000 SWDs per day RDF crawler: best harvesting method HTML crawler: best accuracy Meta crawler: best in detecting websites # of documents

UMBC an Honors University in Maryland 27 This talk Motivation Swoogle overview Bots navigate the Semantic Web Ranking Semantic Web content Use cases and applications Conclusions

UMBC an Honors University in Maryland 28 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 –InderenceWeb: searching over and enhancing proofs –SemNews: Text Meaning of news stories Supporting SW tools –Triple shop: finding data for SPARQL queries

UMBC an Honors University in Maryland 29

UMBC an Honors University in Maryland 30

UMBC an Honors University in Maryland 31

UMBC an Honors University in Maryland 32 Web-scale semantic web data access agent data access servicethe Web ask (“person”) Search vocabulary ask (“?x rdf:type foaf:Person”) inform (“foaf:Person”) Fetch docs Populate RDF database Query local RDF database inform (doc URLs) Search URIrefs in SW vocabulary Search URLs in SWD index Compose query Index RDF data

UMBC an Honors University in Maryland 33 UMBC Triple Shop Online SPARQL RDF query processing based on HP’s Joseki with two features Selectable reasoning level of inference Automatically finds SWDs for give queries using Swoogle backend database –Provide dataset creation wizard and server-side dataset storage –Tag and share saved datasets SPARQL: a query language for getting information from RDF graphs (dataset)

UMBC an Honors University in Maryland 34 UMBC Triple Shop Querying the Semantic Web is as easy as shopping (1)Go to (2)You provide a SPARQL query and constraints on what sources to use (3)Swoogle finds and suggests documents with relevant data, producing a dataset (4)You specify the amount of reasoning to do, possibly resulting in an enhanced dataset (5)We run the query and give you the results (6)You can also download the dataset or save it on the server and give it tags

UMBC an Honors University in Maryland 35

UMBC an Honors University in Maryland 36

UMBC an Honors University in Maryland 37

UMBC an Honors University in Maryland 38 This talk Motivation Swoogle overview Bots navigate the Semantic Web Ranking Semantic Web content Use cases and applications Conclusions

UMBC an Honors University in Maryland 39 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/dateTermsDocumentsIndividualsTriplesBytes Swoogle21.5x x10 5 7x10 6 5x10 7 7x10 9 Swoogle32x10 5 7x x x10 7 1x x10 6 5x10 7 5x10 9 5x x10 6 5x10 9 5x x10 13 We think Swoogle’s centralized approach can be made to work for the next few years if not longer.

UMBC an Honors University in Maryland 40 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

UMBC an Honors University in Maryland 41 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

UMBC an Honors University in Maryland 42 Annotated in OWL For more information