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Information Integration and the Semantic Web Finding knowledge, data and answers Tim Finin1, Anupam Joshi1, Li Ding2 1 University of Maryland, Baltimore County 2 Stanford University, Knowledge Systems Lab Joint work with Yun Peng, Cynthia Parr, Andriy Parafinyk, Lushan Han, Pranam Kolari, Pavan Reddivari, Rong Pan, Akshay Java, Joel Sachs and others. UMBC an Honors University in Maryland http://ebiquity.umbc.edu/resource/html/id/327/ 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. 1 Google has made us smarter UMBC an Honors University in Maryland 2 But what about our agents? tell register UMBC an Honors University in Maryland Agents still have a very minimal understanding of text and images. 3 But what about our agents? Swoogle Swoogle Swoogle Swoogle tell Swoogle Swoogle Swoogle register Swoogle Swoogle Swoogle Swoogle Swoogle Swoogle Swoogle Swoogle A Google for knowledge on the Semantic Web is needed by software agents and programs UMBC an Honors University in Maryland 4 Information Integration and the Semantic Web • The Semantic Web enables information integration with standards supporting shared semantic models, ontology mapping, common tools, etc. • A Google-like global index can help people and programs to – Find Semantic Web ontologies and data – Understand how these are being used – Build trust and provenance models – Assemble ontology maps – Create new integration tools UMBC an Honors University in Maryland 5 • http://swoogle.umbc.edu/ • Running since summer 2004 • 1.8M RDF docs, 320M triples, 10K ontologies, 15K namespaces, 1.3M classes, 175K properties, 43M instances, 600 registered users UMBC an Honors University in Maryland 6 Applications and use cases 1 Supporting Semantic Web developers – Ontology designers, vocabulary discovery, who uses what ontologies & data, use analysis, errors, statistics, etc. 2 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 3 Supporting SW tools – Triple shop: finding data for SPARQL queries UMBC an Honors University in Maryland 7 1 UMBC an Honors University in Maryland 8 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 UMBC an Honors University in Maryland 9 All of this is available in RDF form for the agents among us. UMBC an Honors University in Maryland 10 Here’s what the agent sees. Note the swoogle and wob (web of belief) ontologies. UMBC an Honors University in Maryland 11 2 An NSF ITR collaborative project with • University of Maryland, Baltimore County • University of Maryland, College Park • University of California, Davis • Rocky Mountain Biological Laboratory UMBC an Honors University in Maryland 12 Invasive Species • Invasive species cost the U.S. economy over $138 billion per year • By various estimates, these species contribute to the decline of 35% - 46% of U.S. endangered and threatened species • The invasive species problem is growing, as the number of pathways of invasion increases. Pimental et al. 2000 Environmental and economic costs associated with non-indigenous species in the United States. Bioscience 50:53-65. Charles Groat, Director U.S. Geological Survey, http://www.usgs.gov/invasive_species/plw/usgsdirector01.html UMBC an Honors University in Maryland 13 East River Valley Trophic Web UMBC http://www.foodwebs.org/ an Honors University in Maryland 14 Biologists Gathering data UMBC an Honors University in Maryland • Increase utility • Maximize productivity • Foster discovery • Broaden participation 15 Representing and sharing data Journal articles Flat files Spreadsheets Local databases On the Web in HTML or XML UMBC an Honors University in Maryland 16 ELVIS: Ecosystem Localization, Visualization, and Integration System Oreochromis niloticus Nile tilapia Bacteria Microprotozoa Food web Species list Amphithoe longimana constructor constructor Caprella penantis Cymadusa compta Lembos rectangularis Batea catharinensis Ostracoda Melanitta Tadorna tadorna . . . ? UMBC an Honors University in Maryland 17 ELVIS Food Web Constructor predicts basic network structure WeightAB 1 1 ( DistanceXA PenaltyXA ) ( DistanceYB PenaltyYB ) N CertaintyIdxXY i 1 UMBC an Honors University in Maryland weighti ( LinkValuei ) discount Prelude to systems models 18 Examine evidence for predicted links. The Evidence Provider lets users explore evidence (data, papers, reasoning) for food web links UMBC an Honors University in Maryland 19 data from ~300 food webs UMBC an Honors University in Maryland 20 Supporting ontologies and their use • SpireEcoConcepts, for – confirmed and potential food web links – bibliographic information of food web studies – ecosystem terms – taxonomic ranks • California Wildlife Habitat Relationships Ontology – life history – geographic range – management information • ETHAN (Evolutionary Trees and Natural History) – Natural history information on species derived from data in the Animal Diversity Web and other taxonomic sources UMBC an Honors University in Maryland 21 UMBC Triple Shop • http://sparql.cs.umbc.edu/ 3 • Online SPARQL RDF query processing with several interesting features • Automatically finds data for queries using Swoogle • 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 UMBC an Honors University in Maryland RDF query language 22 Triple Shop What are body masses of fishes that eat fishes? . . . leaving out the FROM clause UMBC an Honors University in Maryland 23 specify dataset UMBC an Honors University in Maryland 24 11 RDF documents were found that might have useful data UMBC an Honors University in Maryland 25 We’ll select them all and add them to the current dataset. UMBC an Honors University in Maryland 26 We’ll run the query against this dataset to see if the results are as expected. UMBC an Honors University in Maryland 27 The results can be produced in any of several formats UMBC an Honors University in Maryland 28 Results http://sparql.cs.umbc.edu/tripleshop2/ UMBC an Honors University in Maryland 29 • Looks like a useful dataset! • Let’s annotate, tag and save it and also materialize it the TS triple store. • Queries can also be annotated, tagged and shared. UMBC an Honors University in Maryland 30 Themes revisited • The Web contains the world’s knowledge in forms accessible to people and computers • The Semantic Web enables information integration with standards supporting shared semantic models, ontology mapping, common tools, etc. • We need better ways to discover, index, search and reason over knowledge on the Semantic Web • Swoogle-like systems help create consensus ontologies, foster best practices, find data and support tools. UMBC an Honors University in Maryland 31 For more information http://ebiquity.umbc.edu/ Annotated in OWL UMBC an Honors University in Maryland 32