UMBC an Honors University in Maryland 1 Information Integration
and the Semantic Web Finding knowledge, data and answers Tim Finin
1, Anupam Joshi 1, Li Ding 2 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. 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. http://ebiquity.umbc.edu/resource/html/id/327/ Slide 2 UMBC an
Honors University in Maryland 2 Google has made us smarter Slide 3
UMBC an Honors University in Maryland 3 But what about our agents?
tell register Agents still have a very minimal understanding of
text and images. Slide 4 UMBC an Honors University in Maryland 4
But what about our agents? A Google for knowledge on the Semantic
Web is needed by software agents and programs Swoogle tell register
Slide 5 UMBC an Honors University in Maryland 5 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 Slide 6 UMBC an Honors University in Maryland 6
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
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 Slide 7 UMBC an
Honors University in Maryland 7 Applications and use cases
Supporting Semantic Web developers Ontology designers, vocabulary
discovery, who uses what ontologies & 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 Slide 8 UMBC an Honors University in
Maryland 8 1 Slide 9 UMBC 9 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 Lets look at
this one Slide 10 UMBC an Honors University in Maryland 10 All of
this is available in RDF form for the agents among us. Slide 11
UMBC an Honors University in Maryland 11 Heres what the agent sees.
Note the swoogle and wob (web of belief) ontologies. Slide 12 UMBC
an Honors University in Maryland 12 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 An NSF ITR collaborative project
with University of Maryland, Baltimore County University of
Maryland, College Park University of California, Davis Rocky
Mountain Biological Laboratory Slide 13 UMBC an Honors University
in Maryland 13 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 Slide
14 UMBC an Honors University in Maryland 14 East River Valley
Trophic Web http://www.foodwebs.org/ Slide 15 UMBC an Honors
University in Maryland 15 Biologists Gathering data Increase
utility Maximize productivity Foster discovery Broaden
participation Slide 16 UMBC an Honors University in Maryland 16
Representing and sharing data Journal articles Flat files
Spreadsheets Local databases On the Web in HTML or XML Slide 17
UMBC an Honors University in Maryland 17 Bacteria Microprotozoa
Amphithoe longimana Caprella penantis Cymadusa compta Lembos
rectangularis Batea catharinensis Ostracoda Melanitta Tadorna
tadorna ELVIS: Ecosystem Localization, Visualization, and
Integration System Oreochromis niloticus Nile tilapia ? ?...
Species list constructor Food web constructor Slide 18 UMBC an
Honors University in Maryland 18 ELVIS Food Web Constructor
predicts basic network structure Prelude to systems models Slide 19
UMBC an Honors University in Maryland 19 Examine evidence for
predicted links. The Evidence Provider lets users explore evidence
(data, papers, reasoning) for food web links Slide 20 UMBC an
Honors University in Maryland 20 data from ~300 food webs Slide 21
UMBC an Honors University in Maryland 21 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 Slide 22 UMBC an Honors University
in Maryland 22 UMBC Triple Shop http://sparql.cs.umbc.edu/ 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 3 RDF OWL RDF query language
Slide 23 UMBC an Honors University in Maryland 23... leaving out
the FROM clause What are body masses of fishes that eat fishes?
Triple Shop Slide 24 UMBC an Honors University in Maryland 24
specify dataset Slide 25 UMBC an Honors University in Maryland 25
11 RDF documents were found that might have useful data Slide 26
UMBC an Honors University in Maryland 26 Well select them all and
add them to the current dataset. Slide 27 UMBC an Honors University
in Maryland 27 Well run the query against this dataset to see if
the results are as expected. Slide 28 UMBC an Honors University in
Maryland 28 The results can be produced in any of several formats
Slide 29 UMBC an Honors University in Maryland 29 Results
http://sparql.cs.umbc.edu/tripleshop2/ Slide 30 UMBC an Honors
University in Maryland 30 Looks like a useful dataset! Lets
annotate, tag and save it and also materialize it the TS triple
store. Queries can also be annotated, tagged and shared. Looks like
a useful dataset! Lets annotate, tag and save it and also
materialize it the TS triple store. Queries can also be annotated,
tagged and shared. Slide 31 UMBC an Honors University in Maryland
31 Themes revisited The Web contains the worlds 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. Slide 32 UMBC an
Honors University in Maryland 32 http://ebiquity.umbc.edu/
Annotated in OWL For more information
