UMBC an Honors University in Maryland 1 Information Integration and the Semantic Web Finding knowledge, data and answers Tim Finin University of Maryland, Baltimore County Joint work with Li Ding, Anupam Joshi, Yun Peng, Cynthia Parr, 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 Swoogle Semantic Web search engine Use cases and applications Observations Conclusions

UMBC an Honors University in Maryland 3 Google has made us smarter

UMBC an Honors University in Maryland 4 But what about our agents? tell register Agents still have a very minimal understanding of text and images.

UMBC an Honors University in Maryland 5 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 6 This talk Motivation Swoogle Semantic Web search engine Use cases and applications Observations Conclusions

UMBC an Honors University in Maryland 7 Running since summer M RDF docs, 320M triples, 10K ontologies, 15K namespaces, 1.3M classes, 175K properties, 43M instances, 600 registered users Running since summer M RDF docs, 320M triples, 10K ontologies, 15K namespaces, 1.3M classes, 175K properties, 43M instances, 600 registered users

UMBC an Honors University in Maryland 8 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 9 This talk Motivation Swoogle Semantic Web search engine Use cases and applications Observations Conclusions

UMBC an Honors University in Maryland 10 Applications and use cases Supporting Semantic Web developers –Ontology designers, vocabulary discovery, who’s using my ontologies or data?, use analysis, errors, statistics, etc. Helping scientists publish and find data –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 1 2 3

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UMBC an Honors University in Maryland 12 By default, ontologies are ordered by their ‘popularity’, but they can also be ordered by recency or size. 80 ontologies were found that had these three terms Let’s look at this one

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UMBC an Honors University in Maryland 14 rdfs:range was used 41 times to assert a value. owl:ObjectProperty was instantiated 28 times time:Cal… defined once and used 24 times (e.g., as range)

UMBC an Honors University in Maryland 15 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.

UMBC an Honors University in Maryland 16 Here’s what the agent sees. Note the swoogle and wob (web of belief) ontologies.

UMBC an Honors University in Maryland 17 We can also search for terms (classes, properties) like terms for “person”.

UMBC an Honors University in Maryland 18 10K terms associated with “person”! Ordered by use. Let’s look at foaf:Person’s metadata

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UMBC an Honors University in Maryland 22 87K documents used foaf:gender with a foaf:Person instance as the subject

UMBC an Honors University in Maryland 23 3K documents used dc:creator with a foaf:Person instance as the object

UMBC an Honors University in Maryland 24 2 An NSF ITR collaborative project with University of Maryland, Baltimore County University of Maryland, College Park U. Of California, Davis Rocky Mountain Biological Laboratory An NSF ITR collaborative project with University of Maryland, Baltimore County University of Maryland, College Park U. Of California, Davis Rocky Mountain Biological Laboratory

UMBC an Honors University in Maryland 25 An invasive species scenario Nile Tilapia fish have been found in a California lake. Can this invasive species thrive in this environment? If so, what will be the likely consequences for the ecology? So…we need to understand the effects of introducing this fish into the food web of a typical California lake

UMBC an Honors University in Maryland 26 Food Webs A food web models the trophic (feeding) relationships between organisms in an ecology –Food web simulators are used to explore the consequences of changes in the ecology, such as the introduction or removal of a species –A locations food web is usually constructed from studies of the frequencies of the species found there and the known trophic relations among them. Goal: automatically construct a food web for a new location using existing data and knowledge ELVIS: Ecosystem Location Visualization and Information System

UMBC an Honors University in Maryland 27 East River Valley Trophic Web

UMBC an Honors University in Maryland 28 Species List Constructor Click a county, get a species list

UMBC an Honors University in Maryland 29 The problem We have data on what species are known to be in the location and can further restrict and fill in with other ecological models But we don’t know which of these the Nile Tilapia eats of who might eat it. We can reason from taxonomic data (simlar species) and known natural history data (size, mass, habitat, etc.) to fill in the gaps.

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UMBC an Honors University in Maryland 31 Food Web Constructor Predict food web links using database and taxonomic reasoning. In an new estuary, Nile Tilapia could compete with ostracods (green) to eat algae. Predators (red) and prey (blue) of ostracods may be affected

UMBC an Honors University in Maryland 32 Evidence Provider Examine evidence for predicted links.

UMBC an Honors University in Maryland 33 Status Goal is ELVIS (Ecosystem Location Visualization and Information System) as an integrated set of web services for constructing food webs for a given location. Background ontologies –SpireEcoConcepts: concepts and properties to represent food webs, and ELVIS related tasks, inputs and outputs –ETHAN (Evolutionary Trees and Natural History) Concepts and properties for ‘natural history’ information on species derived from data in the Animal diversity web and other taxonomic sources Under development –Connect to visualization software –Connect to triple shop to discover more data

UMBC an Honors University in Maryland 34 UMBC Triple Shop Online SPARQL RDF query processing with several interesting features Automatically finds SWDs for give queries using Swoogle backend database Datasets, queries and results can be saved, tagged, annotated, shared, searched for, etc. RDF datasets as first class objects –Can be stored on our server or downloaded –Can be materialized in a database or (soon) as a Jena model 3

UMBC an Honors University in Maryland 35 Who knows Anupam Joshi? Show me their names, address and pictures

UMBC an Honors University in Maryland 36 The UMBC ebiquity site publishes lots of RDF data, including FOAF profiles

UMBC an Honors University in Maryland 37 No FROM clause! PREFIX foaf: SELECT DISTINCT ?p2name ?p2mbox ?p2pix FROM ??? WHERE { ?p1 foaf:surname "Joshi". ?p1 foaf:firstName “Anupam". ?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

UMBC an Honors University in Maryland 38 Enter query w/o FROM clause! log in specify dataset

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UMBC an Honors University in Maryland RDF documents were found that might have useful data.

UMBC an Honors University in Maryland 42 We’ll select them all and add them to the current dataset.

UMBC an Honors University in Maryland 43 We’ll run the query against this dataset to see if the results are as expected.

UMBC an Honors University in Maryland 44 The results can be produced in any of several formats

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UMBC an Honors University in Maryland 46 Looks like a useful dataset. Let’s save it and also materialize it the TS triple store.

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UMBC an Honors University in Maryland 48 We can also annotate, save and share queries.

UMBC an Honors University in Maryland 49 Work in Progress There are a host of performance issues We plan on supporting some special datasets, e.g., –FOAF data collected from Swoogle –Definitions of RDF and OWL classes and properties from all ontologies that Swoogle has discovered Expanding constraints to select candidate SWDs to include arbitrary metadata and embedded queries –FROM “documents trusted by a member of the SPIRE project” We will explore two models for making this useful –As a downloadable application for client machines –As an (open source?) downloadable service for servers supporting a community of users.

UMBC an Honors University in Maryland 50 This talk Motivation Swoogle Semantic Web search engine Use cases and applications Observations Conclusions

UMBC an Honors University in Maryland 51 Will Swoogle 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 52 How much reasoning should Swoogle do? 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 53 A RDF Dictionary We hope to develop an RDF dictionary. Given an RDF term, returns a graph of its definiton –Term  definition from “official” ontology –Term+URL  definition from SWD at URL –Term+*  union definition –Optional argument recursively adds definitions of terms in definition excluding RDFS and OWL terms –Optional arguments identifies more namespaces to exclude

UMBC an Honors University in Maryland 54 This talk Motivation Swoogle Semantic Web search engine Use cases and applications Observations Conclusions

UMBC an Honors University in Maryland 55 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 56 Annotated in OWL For more information

UMBC an Honors University in Maryland 57 Backup