RIDLUR: Relationship Identification Leveraging Userdefined Rules

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RIDLUR: Relationship Identification Leveraging Userdefined Rules

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Title: RIDLUR: Relationship Identification Leveraging Userdefined Rules

1
RIDLURRelationship Identification Leveraging
User-defined Rules

Meta-data Extraction Group
CSCI6350
Zixin Wu
Vincent Howard
Michael Moore

2
Background Reading

How to Create Topic Maps
Identifies all associations between two items in
an index (topic map)
Uses GATE/JAPE
Classifying Semantic Relations in Bioscience
Texts
Identifies relations between two fixed entities
S-CREAM - Semi-automatic Creation of Metadata
Annotation and Creation of metadata-enriched
documents
Uses machine learning techniques to create rules
for extraction
Semantic Enhancement Engine (SEE)
Does not use natural language processing
techniques
Automatically determines target ontology
Keyword Extraction from the Web for FOAF Metadata
Uses context to identify relationships (two
entities occur in multiple web pages)
Instance-Based Learning and Information
Extraction for the Generation of Metadata
Uses simple (Perl) based regular expressions to
identify entities and relationships
Doesnt work well for dirty data or a large
variety of knowledge types

3
Research Problems

Information Extraction of structured,
semi-structured and unstructured data
Entity Identification
Entity Disambiguation
Relationship Identification
Mapping to ontology
Classification
Identifying importance to target domain (one and
many)

4
Problems to be addressed

Relationship Identification
Identify relationships in unstructured text
Applicability to target domain
Only concerned with relationships defined in the
target ontology
Allows relationship extraction rules to be
defined in the context of the relationship

5
System Architecture
6
System Architecture

GATE (Natural language processing tool)
English Tokeniser
Separate words and punctuations as "Tokens".
Sentence Splitter
Separate every sentence.
POS Tagger
Annotate the grammar function to every token.
Morphological analyzer
Annotate the root of every verb.
Gazetteer
Annotate the type of every token, by looking up
lists.
OrthoMatcher
For example, "IBM" and "Big Blue" is the same
thing.
Verb Group Chunker
For example, "is going to investigate" is a verb
chunk.
NE Transducer
Find the named entities. For example, "Mr. Smith"
is a person.

7
System Architecture

User perspective
Define regular expressions based on relationships
in target ontology
Translated into JAPE Rules (complex regular
expressions)
Generate automata on-the-fly.
Matching text returned to user
Simple text
RDF

8
Abilities and Limitations

Abilities
Present user with text that matched the regular
expressions defined
Identifies instances of relationships defined in
the target ontology
Associate each regular expression with one
relationship in the target ontology
Limitations
Does no machine learning
Does not handle coreference and subordinate
clause
Does not create instance data
Simple user interface
Only allows specifying one schema/knowledgebase

9
Example
Mr. Smith lived in Athens.
lived in Athens
subj
in
Verb2Subject subj Verb2Object in
Mr. Smith (Person)
Athens (Location)
Mr. Smith and Bob lived in Athens.
lived in Athens
subj
in
Verb2Subject subjset_member Verb2Object in
set
Athens (Location)
set_member
Mr. Smith (Person)
Bob (Person)
Verb2Subject subj(set_member)
10
References

How to Create Topic Maps, Kerk, R. (University
Halle-Wittenberg), Groschupf, S. (Media Style
Labs).
Classifying Semantic Relations in Bioscience
Texts, Rosario, B, Hearst, M. UC Berkeley.
Proceedings of ACL. 2004.
S-CREAM - Semi-automatic Creation of Metadata,
Handschuh, S., Stabb, S., Ciravenga, F. Institute
AIFB, Department of CS, University of Sheffield.
Semantic Enhancement Engine A Modular Document
Enhancement Platform for Semantic Applications
over Heterogeneous Content, Hammond, B.,
Sheth, A., Kochut, K. Semagix and Department of
Computer Science, University of Georgia.Real
World Semantic Web Applications, 2002.
Keyword Extraction from the Web for FOAF
Metadata, Mori, J. et. all. University of Tokyo.
Instance-Based Learning and Information
Extraction for the Generation of Metadata,
Lattner, A., Herzog, O. Center for Computing
Technologies - TZI, University of Bremen,
Germany. 3rd International Conference on
Knowledge Management, 2003.

11
References

Extracting Personal Names from Emails Applying
Named Entity Recognition To Informal Text,
Minkov, E., Cohen, W., Wang, R. Carnegie Mellon
University. Computational Linguistics, 2004.
Computational Linguistics Meets Metadata, or the
Automatic Extraction of Keywords From Full Text
Content, Deegan, M, et. all. 2004.
Automatic Document Metadata Extraction using
Support Vector Machines, Han, H. , et. all.
JCDL, 2003.
Exploiting Dictionaries in Named Entity
Extraction Combining Semi-Markov Extraction
Processes and Data Integration Methods, Cohen,
W. (Carnegie Mellon University), Sarawagi, S.
(IIT Bombay). KD, 2004.
A Comparison of String Metrics for Matching
Names and Records, Cohen, W., Ravikumar, P.,
Flenberg, S. Carnegie Mellon University. 2003.
From Unstructured Data to Actionable
Intelligence, Rao, R. IEEE IT Pro, 2003.
SemTag and Seeker Bootstrapping the Semantic
Web via Automated Semantic Annotation, Dill, S.,
et. all. IBM Almaden Research Center. 2003.
Web Directories as Training Data for Automated
Metadata Extraction, Kavalec, M., Svatek, V.,
Strossa, P. University of Economics, Prague,
Czech Republic. 2004.
Adaptive Name Matching in Information
Integration, Bilenkio, M., Mooney, R.
(University of Texas at Austin), Cohen, W.,
Ravikumar, P., Flenberg, S. (Carnegie Mellon
University). IEEE Intelligent Systems, 2003.
A Generalized Framework for an Ontology-Based
Data-Extraction System, Wessman, A., Liddle, S.,
Embley, D. ISTA. 2005.
Automatic Discovery of WordNet Relations,
Hearst, M. WordNet An Electronic Lexical
Database. MIT Press. 1998.