Information Integration and the Semantic Web Finding knowledge, data and answers

UMBCUMBCan Honors University in an Honors University in

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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 County2 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/


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Google has made us smarter


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But what about our agents?

tell

register

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


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But what about our agents?

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

SwoogleSwoogle

Swoogle

Swoogle

SwoogleSwoogle

SwoogleSwoogle

Swoogle SwoogleSwoogle

SwoogleSwoogle

SwoogleSwoogle

tell

register


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Information Integrationand 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


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•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


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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

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2

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1


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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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All of this is available in RDF form for the

agents among us.


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Here’s what the agent sees. Note the swoogle and wob (web of belief) ontologies.


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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


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Invasive Species

• Invasive species cost the U.S.economy over $138 billion per year

• By various estimates, these speciescontribute 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


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East River Valley Trophic Web

http://www.foodwebs.org/


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Biologists Gathering data

• Increase utility• Maximize productivity• Foster discovery• Broaden participation


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Representing and sharing data

Journal articles

Flat files

Spreadsheets

Local databases

On the Web in HTML or XML


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Bacteria

Microprotozoa

Amphithoe longimana

Caprella penantis

Cymadusa compta

Lembos rectangularis

Batea catharinensis

Ostracoda

Melanitta

Tadorna tadorna

ELVIS: Ecosystem Localization, Visualization, and Integration

SystemOreochromis niloticus

Nile tilapia

??

. . .

Species list constructor

Food web constructor


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ELVIS Food Web Constructor

predicts basic network structure

Prelude to systems models

XY

1

( )i

Ni

i

weightCertaintyIdx LinkValue

discount

AB

XA XA YB

1

1 ( ) ( )YB

WeightDistance Penalty Distance Penalty


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Examine evidence for predicted links.

The Evidence Provider lets users explore evidence (data, papers, reasoning) for food web links

The Evidence Provider lets users explore evidence (data, papers, reasoning) for food web links


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data from ~300 food webs

data from ~300 food webs


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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


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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

3RDFOWL

RDF query language


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. . . leaving out the FROM clause

What are body masses of fishes that eat fishes?

Triple Shop


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specify dataset


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11 RDF documents were found that might

have useful data


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We’ll select them all and add them to the

current dataset.


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We’ll run the query against this dataset to see if the results are as expected.

We’ll run the query against this dataset to see if the results are as expected.


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The results can be produced in any of several formats

The results can be produced in any of several formats


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Results

http://sparql.cs.umbc.edu/tripleshop2/


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• 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.

• 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.


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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.


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http://ebiquity.umbc.edu/

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Information Integration and the Semantic Web Finding knowledge, data and answers