1. Information Retrieval and the Semantic Web
Tim Finin, James Mayfield, Anupam Joshi, R. Scott Cost and Clay Fink
University of Maryland, Baltimore County
Johns Hopkins University, Applied Physics Lab
04 January 2004
DARPA contract F and NSF awards ITR-IIS and ITR-IIS provided partial research support for this work

2. Introduction and motivation

3. “XML is Lisp's bastard nephew, with uglier syntax and no semantics
“XML is Lisp's bastard nephew, with uglier syntax and no semantics. Yet XML is poised to enable the creation of a Web of data that dwarfs anything since the Library at Alexandria.”
-- Philip Wadler, Et tu XML? The fall of the relational empire, VLDB, Rome, September 2001.

4. “The web has made people smarter
“The web has made people smarter. We need to understand how to use it to make machines smarter, too.”
-- Michael I. Jordan (UC Berkeley), paraphrased from a talk at AAAI, July
2002

5. “The Semantic Web will globalize KR, just as the WWW globalize hypertext”
-- Tim Berners-Lee

6. “The multi-agent systems paradigm and the web both emerged around 1990
“The multi-agent systems paradigm and the web both emerged around One has succeeded beyond imagination and the other has not yet made it out of the lab.”
-- Anonymous, 2001

7. tell
register
Software agents will need something similar to maximize the use of information on the semantic web.

8. Vision and Model

9. Vision Semantic markup (e.g., OWL) as markup
Web documents are traditional HTML documents, augmented with machine-readable semantic markup that describes their content
Inference and retrieval are tightly bound
Inference over semantic markup improves retrieval and text retrieval facilitates inference
Agents should use the web like humans do
Think of a query, encode to retrieve possibly relevant documents, read some and extract knowledge, repeat until objectives met

10. Why use IR techniques?
We will want to retrieve over structured and unstructured knowledge
We should prepare for the appearance of text documents with embedded SW markup
We may want to get our SWDs into conventional search engines, such as Google.
Mature, scalable, low cost, deployed infrastructure
IR techniques also have some unique characteristics that may be very useful
e.g., ranking matches, document similarity, clustering, relevance feedback, etc.

11. Framework–Semantic Markup
agent
Local KB
Semantic
Web Query
Inference
Engine
Extractor
Encoder
(“swangler”)
Encoded
Markup
Semantic
Markup
Statement
to be proved
Web
Search
Engine
Ranked
Pages
Filters
Semantic
Markup
Semantic
Markup

12. Framework–Incorporating Text
Local KB
Semantic
Web Query
Inference
Engine
Extractor
Encoder
(“swangler”)
Encoded
Markup
Semantic
Markup
Statement
to be proved
Web
Search
Engine
Text
Query
Filters
Text
Text
Ranked
Pages
Filters
Semantic
Markup
Semantic
Markup

13. Harnessing Google
Google started indexing RDF documents some time in late 2003
Can we take advantage of this?
We’ve developed techniques to get some structured data to be indexed by Google
And then later retrieved
Technique: give Google enhanced documents with additional annotations containing Swangle Terms ™

14. Swangle definition swan·gle
Pronunciation: ‘swa[ng]-g&l Function: transitive verb Inflected Forms: swan·gled; swan·gling /-g(&-)li[ng]/ Etymology: Postmodern English, from C++ mangle, Date: 20th century
1: to convert an RDF triple into one or more IR indexing terms
2: to process a document or query so that its content bearing markup will be indexed by an IR system
Synonym: see tblify
- swan·gler /-g(&-)l&r/ noun

15. Swangling Swangling turns a SW triple into 7 word like terms
One for each non-empty subset of the three components with the missing elements replaced by the special “don’t care” URI
Terms generated by a hashing function (e.g., SHA1)
Swangling an RDF document means adding in triples with swangle terms.
This can be indexed and retrieved via conventional search engines like Google
Allows one to search for a SWD with a triple that claims “Ossama bin Laden is located at X”

16. A Swangled Triple <rdf:RDF
xmlns:s="
</rdf>
<s:SwangledTriple> <s:swangledText>N656WNTZ36KQ5PX6RFUGVKQ63A</s:swangledText> <rdfs:comment>Swangled text for [ </rdfs:comment> <s:swangledText>M6IMWPWIH4YQI4IMGZYBGPYKEI</s:swangledText> <s:swangledText>HO2H3FOPAEM53AQIZ6YVPFQ2XI</s:swangledText> <s:swangledText>2AQEUJOYPMXWKHZTENIJS6PQ6M</s:swangledText> <s:swangledText>IIVQRXOAYRH6GGRZDFXKEEB4PY</s:swangledText> <s:swangledText>75Q5Z3BYAKRPLZDLFNS5KKMTOY</s:swangledText> <s:swangledText>2FQ2YI7SNJ7OMXOXIDEEE2WOZU</s:swangledText> </s:SwangledTriple>

17. What’s the point? We’d like to get our documents into Google
Swangle terms look like words to Google and other search engines.
Cloaking obviates modifying document
Add rules to the web server so that, when a search spider asks for document X the document swangled(X) is returned. Caching makes this efficient
A swangle term length of 7 may be an acceptable length for a Semantic Web of 1010 triples -- collision prob for a triple ~ 2*10-6.
We could also use Swanglish – hashing each triple into N of the 50K most common English words

18. OWLIR

19. Student Event Scenario
UMBC sends out descriptions of ~50 events a week to students.
Each student has a “standing query” used to route event messages.
A student only receives announcements of events matching his/her interests and schedule.
Use LMCO’s AeroText system to automatically add DAML+OIL markup to event descriptions.
Categorize text announcements into event types
Identify key elements and add DAML markup
Use JESS to reason over the markup, drawing ontology-supported inferences

20. Event Ontology A simple ontology for University events
Includes classes, subclasses, properties, etc.
Can include instance data, e.g., UMBC, NEC, Fairleigh Dickenson, etc.

21. OWLIR Architecture Jess Jess Jess Expand Event Description Agents
Classification
Extract triples & reason
Info Extraction
Event Categories
Movie
Sport
Talk
. . .
Trip
LMCO AeroText
+ Java
Jess
Event Descriptions
Text
Jess
Text+ DAML
Text+ DAML
Text + triples
Text + triples
Convert triples to index terms
Extract triples & reason
Convert triples to index terms
Must
Text
Index
Query
User
Interface
Text OK
Jess
SIRE
Must not
Retrieve
Text + triples
Results User Interface
Final Results
Inference on results

22. Swoogle

23. http://swoogle.umbc.edu/ SWD = SWO + SWI SWD Rank SWD IR Engine
Swoogle Search
SWOs
SWIs
HTML
documents
Images
CGI scripts
Audio files
Video files
SWD = SWO + SWI
SWOOGLE 2
Ontology Dictionary
Swoogle Search
Ontology Dictionary
Swoogle Statistics
Web Server
Human users
The web, like Gaul, is divided into three parts: the regular web (e.g. HTML), Semantic Web Ontologies (SWOs), and Semantic Web Instance files (SWIs)
Web Service
Intelligent Agents
service
IR analyzer
SWD analyzer
analysis
SWD Cache
SWD Metadata
digest
SWD Reader
Candidate
URLs
The Web
SWD Rank
Swoogle Statistics
Web Crawler
discovery
A SWD’s rank is a function of its type (SWO/SWI) and the rank and types of the documents to which it’s related.
Swoogle uses four kinds of crawlers to discover semantic web documents and several analysis agents to compute metadata and relations among documents and ontologies. Metadata is stored in a relational DBMS. Services are provided to people and agents.
Statistics as of November 2004
SWDs
336,000
Classes
95,000
Triples
47,000,000
Properties
53,000
Ontologies
4,200
Individuals
7,200,000
SWD IR Engine
Swoogle provides services to people via a web interface and to agents as web services.
Swoogle puts documents into a character n-gram based IR engine to compute document similarity and do retrieval from queries
Contributors include Tim Finin, Anupam Joshi, Yun Peng, R. Scott Cost, Jim Mayfield, Joel Sachs, Pavan Reddivari, Vishal Doshi, Rong Pan, Li Ding, and Drew Ogle. Partial research support was provided by DARPA contract F and by NSF by awards NSF-ITR-IIS and NSF-ITR-IDM November 2004.

24. Concepts Document Term Individual
A Semantic Web Document (SWD) is an online document written in semantic web languages (i.e. RDF and OWL).
An ontology document (SWO) is a SWD that contains mostly term definition (i.e. classes and properties). It corresponds to T-Box in Description Logic.
An instance document (SWI or SWDB) is a SWD that contains mostly class individuals. It corresponds to A-Box in Description Logic.
Term
A term is a non-anonymous RDF resource which is the URI reference of either a class or a property.
Individual
An individual refers to a non-anonymous RDF resource which is the URI reference of a class member.
In swoogle, a document D is a valid SWD iff. JENA* correctly parses D and produces at least one triple.
*JENA is a Java framework for writing Semantic Web applications.
rdf:type
foaf:Person
rdfs:Class
rdf:type
foaf:Person

25. Demo 1 2 3 4 5 Find “Time” Ontology (Swoogle Search)
Digest “Time” Ontology
Document view
Term view 2 3
Find Term “Person”
(Ontology Dictionary)
Digest Term “Person”
Class properties
(Instance) properties 4 5
Swoogle Statistics

26. Demo 1
Find “Time” Ontology
We can use a set of keywords to search ontology. For example, “time, before, after” are basic concepts for a “Time” ontology.

27. Usage of Terms in SWD http://www.cs.umbc.edu/~finin/foaf.rdf
rdf:type
foaf:Person
rdf:type
foaf:Person
foaf:mbox
foaf:mbox
populated Class
rdfs:subClassOf
wordNet:Agent
populated Property
foaf:Person
rdf:type
rdfs:Class
rdfs:domain
foaf:mbox
defined Class
rdf:type
defined Property
rdf:Property
defined Individual

28. Digest “Time” Ontology (term view)
Demo 2(a)
Digest “Time” Ontology (term view)
TimeZone
before
………….
intAfter

29. Digest “Time” Ontology (document view)
Demo 2(b)
Digest “Time” Ontology (document view)

30. Demo 3
Find Term “Person”
Not capitalized! URIref is case sensitive!

31. 167 different properties 562 different properties
Demo 4
Digest Term “Person”
167 different properties
562 different properties

32. Demo 5
Swoogle Statistics

33. Swoogle IR Search
This is work in progress, not yet fully integrated into Swoogle
Documents are put into an ngram IR engine (after processing by Jena) in canonical XML form
Each contiguous sequence of N characters is used as an index term (e.g., N=5)
Queries processed the same way
Character ngrams work almost as well as words but have some advantages
No tokenization, so works well with artificial languages and agglutinative languages
=> good for RDF!

34. Why character n-grams? Suppose we want to find ontologies for time
We might use the following query
“time temporal interval point before after during day month year eventually calendar clock duration end begin zone”
And have matches for documents with URIs like

35. Another approach: URIs as words
Remember: ontologies define vocabularies
In OWL, URIs of classes and properties are the words
So, take a SWD, reduce to triples, extract the URIs (with duplicates), discard URIs for blank nodes, hash each URI to a token (use MD5Hash), and index the document.
Process queries in the same way
Variation: include literal data (e.g., strings) too.

36. Conclusion

37. What we have done
Developed Swoogle – a crawler based retrieval system for SWDs
Developed and implemented a technique to get Google to index and retrieve SWDs
Prototyped (twice) an ngram based IR engine for SWDs
Explored the integration of inference and retrieval
Used these in several demonstration systems