1. Finding knowledge, data and answers on the Semantic Web
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.

2. This talk Motivation Swoogle Semantic Web search engine
Use cases and applications
Conclusions

3. Google has made us smarter
Software agents will need something similar to maximize the use of information on the semantic web.

4. But what about our agents?
Software agents will need something similar to maximize the use of information on the semantic web.
tell
register
Agents still have a very minimal understanding of text and images.

5. But what about our agents?
Swoogle
Swoogle
Swoogle
tell
register
Swoogle
Swoogle
Swoogle
Swoogle
Swoogle
Swoogle
Swoogle
Swoogle
Swoogle
Swoogle
Swoogle
Software agents will need something similar to maximize the use of information on the semantic web.
Swoogle
A Google for knowledge on the Semantic Web is needed by software agents and programs

6. This talk Motivation Swoogle Semantic Web search engine
Use cases and applications
Conclusions

7.
Running since summer 2004
1.6M RDF docs, 300M triples, 10K ontologies, 15K namespaces, 1.3M classes, 175K properties, 43M instances, 420 registered users

8. Swoogle Architecture Analysis Index Discovery Search Services …
IR Indexer
Search Services
Semantic Web
metadata
Web
Service
Server
Candidate
URLs
Bounded Web Crawler
Google Crawler
SwoogleBot
SWD Indexer
Ranking
document cache
SWD classifier
human
machine
html
rdf/xml …
the Web
Information flow
Swoogle‘s web interface
Legends

9. This talk Motivation Swoogle Semantic Web search engine
Use cases and applications
Conclusions

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.
Searching specialized collections
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

11. 1

12. 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

13. Basic Metadata
hasDateDiscovered:  
hasDatePing:  
hasPingState:  PingModified
type:  SemanticWebDocument
isEmbedded:  false
hasGrammar:  RDFXML
hasParseState:  ParseSuccess
hasDateLastmodified:  
hasDateCache:  
hasEncoding:  ISO
hasLength:  18K
hasCntTriple:  311.00
hasOntoRatio:  0.98
hasCntSwt:  94.00
hasCntSwtDef:  72.00
hasCntInstance:  8.00

15. 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)

16. All of this is available in RDF form for the agents among us.
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.

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

18. We can also search for terms (classes, properties) like terms for “person”.

19. 10K terms associated with “person”! Ordered by use.
Let’s look at foaf:Person’s metadata

23. 87K documents used foaf:gender with a foaf:Person instance as the subject

24. 3K documents used dc:creator with a foaf:Person instance as the object

25. Swoogle’s archive saves every version of a SWD it’s seen.

27. 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

28. 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

29. 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

30. East River Valley Trophic Web
The web structure in the image is organized vertically, with node color representing trophic level. Red nodes represent basal species, such as plants and detritus, orange nodes represent intermediate species, and yellow nodes represent top species or primary predators. Links characterize the interaction between two nodes, and the width of the link attenuates down the trophic cascade (i.e. a link is thicker at the predator end and thinner at the prey end).

31. Species List Constructor
Click a county, get a species list

32. 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.

34. Predict food web links using database and taxonomic reasoning.
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

35. Examine evidence for predicted links.
Evidence Provider
Examine evidence for predicted links.

36. 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

37. 3 UMBC Triple Shop http://sparql.cs.umbc.edu/
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

38. Web-scale semantic web data access
agent
data access service
the Web
Index RDF data
ask (“person”)
Search vocabulary
Search URIrefs
in SW vocabulary
inform (“foaf:Person”)
Compose query
ask (“?x rdf:type foaf:Person”)
Populate
RDF database
Search URLs
in SWD index
inform (doc URLs)
Fetch docs
Query local
RDF database

39. Who knows Anupam Joshi?
Show me their names, address and pictures

40. The UMBC ebiquity site publishes lots of RDF data, including FOAF profiles

41. PREFIX foaf: <http://xmlns.com/foaf/0.1/>
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
No FROM clause!

42. log in
specify dataset
Enter query w/o FROM clause!

45. 302 RDF documents were found that might have useful data.

46. We’ll select them all and add them to the current dataset.

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

48. The results can be produced in any of several formats

50. Looks like a useful dataset
Looks like a useful dataset. Let’s save it and also materialize it the TS triple store.

52. We can also annotate, save and share queries.

53. 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.

54. This talk Motivation Swoogle Semantic Web search engine
Use cases and applications
State of the Semantic Web
Conclusions

55. 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/date
Terms
Documents
Individuals
Triples
Bytes
Swoogle2
1.5x105
3.5x105
7x106
5x107
7x109
Swoogle3
2x105
7x105
1.5x107
7.5x107
1x1010
2006
1x106
5x109
5x1011
2008
5x106
5x1013
We think Swoogle’s centralized approach can be made to work for the next few years if not longer.

56. 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

57. A RDF Dictionary We’d 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

58. 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

59. For more information
Annotated in OWL