The document discusses ontology-based information extraction (OBIE), which uses an ontology to guide the extraction of information from unstructured text and presents the extracted information in an ontology. It defines OBIE, outlines common OBIE methods like linguistic rules, gazetteers, classification techniques, and discusses technologies, datasets, and evaluation used in OBIE implementations. While OBIE shows potential for creating semantic web content, the document notes there is no agreed-upon OBIE methods yet and areas remain like developing better extractors and cross-lingual OBIE.

1. The DERI Reading Group
Ontology-based information extraction:
An Overview & Survey
(2010, Wimalasuriya and Dou)
Tobias Wunner, UNLP Group
 Copyright 2010 Digital Enterprise Research Institute. All rights reserved, Paul Buitelaar

2. Definition - Motivation
a) Create content for the Semantic Web
 convert existing websites into ontologies
b) Improve quality of existing ontologies
 Test criterion: OBIE task
 OBIE good => ontology good

3. Overview
 Access to information…

4. Overview
 Access to information…
Ontologie-based Information Extraction (OBIE):
“A system that processes unstructured or semi-
structured natural language text guided by an
ontology and presents the output in an ontology.

5. Overview
 ESWC dogfood OBIE-related topics
New!

6. T
 1. Text only:
 Extract conceptualization and instances
County
building with café
and football table
Building
is-a
1. conceptualization
2. instances
Galway DERI building
Problem – two scenarios

7. T
County
building with café
and football table
Building
is-a
1. conceptualization
2. instances
Galway DERI building
Problem – two scenarios
 conceptualization can be
too specific / generic
 wrong conceptualization
 1. Text only:
 Extract conceptualization and instances

8. T
City Buildinglocated
in
Conceptualization
by domain ontology
2. instances
Galway DERI building
Problem – two scenarios
 2. Domain ontology & text:
 extract instances only

9. T
City Buildinglocated
in
Conceptualization
by domain ontology
2. instances
Galway DERI building
Problem – two scenarios
 2. Domain ontology & text:
 extract instances only
less generic but more
semantic stable

10. Definition – key characteristics
a) Process structured / unstructured text
b) “guided” by an ontology
c) Present output in ontology
Text
Source
Information
Extractor
Ontology
guided by

11. Definition – ontology learning or population?
 Ontology population ⊂ OBIE
 “OBIE is Open information extraction” (Etzioni)
 alternative: semantics given by ontology!
 extractors can be inside / outside ontology
Text
Source
Information
Extractor
Ontology
guided by

12. Methods
 Information extractors
1. Linguistic rules
2. Gazetteer lists
3. Classification (classical / structure-aware)
4. Partial parse trees
5. Structured data analyzers
6. Web querying

13. Linguistic Rules - Methods
 Regular expressions
 <COMPANY> .* revenue <Number> <currency>
“Tesco’s revenue in 2009 was 3.4 billion GBP.”
 Extraction ontologies
 combination of ontology and lexicon
(Mädche, Embley, Buitelaar)
 manual construction
 High precision

14.  2. Gazetteer lists
 Phrases / words instead of patterns
 Named-Entity Recognition
 Requirements:
1) Specify what is being extracted
2) Specify sources and avoid manual creation
Gazetteer Methods
Sematic Web
Software
Energy
Supermarket
…
industry
The software giant SAP…
Tesco a UK supermarket …
Siemens energy revenue…
… wind energy company Vestas

15.  3. Classification techniques
 Break down IE task in a set of binary tasks
Classification Methods
pos
semTag
c1
c2
..
cn
Classifier
features

16.  Classical
Classification Methods
Galway Germany DERI Siemens
GEIrelandMunich CITEC
missclassification does
not consider structure!
(equal cost 1/6)
DERI
TescoCladdagh
DERI
CountryCity SW Energy
IndustryLocation

17. W1,6=3
 Structure aware
Classification Methods
Galway Germany Siemens
GEIrelandMunich CITEC
Classifier should
consider taxonomy structure!
TescoCladdagh
DERI

18.  4. Partial parse trees
 TACITUS, SMES, LTAG
 5. Analyze structured data
 Wikpedia Infoboxes
 6. Web querying
 C-PANKOW
 “Towards the self annotating web
Other methods

19. Technologies used in implementation
 Shallow NLP (GATE, sProUT, StanfordNLP)
 POS, sentence splitting, regular expression
 Semantic lexicons (WordNet, GermaNet)
 synonym, meronym, hypernym
 Semantic Annotation (OCAT, iDocument,
PIMO)
Missing
 Terminological tools (UMLS, bio
terminologies)
 Thesauri, translation memory

20. Data sets & evaluation
Data sets (corpora)
1) Message Understanding Conference (MUC-7)
2) Automatic Content Extraction (ACE)
 => more on classical IR, IE, NLP tracks
 => no data set with given semantics (ontology)
Evaluation
 Precision & recall
 Only used for population task

21. Recent Open IE argument
 Con: Weikum, From Information to Knowledge -
Harvest Web Resources for IE
 Disambiguation
 NL relations are not well defined (well defined
arguments)
 Pro: Weld, Using Wiki to Bootrap Open IE
 Relation targeted:
 learn extractor per relation -> lower recall
 Structural targeted:
 general extraction engine -> lower precision

22. Conclusion and Outlook
 No established/ agreed methods yet
 Is OBIE also ontology learning?
 Data sets
 Methods for best extractors
 Semantic Web contribution?
 eg. Gazetteers from DBPedia
 Cross-lingual OBIE -> CLOBIE

23. References
[1] Wimalasuriya, Dou, Ontology-based Information
Extraction: An Introduction and Survey of current
approaches, in Journal of Computer Science, June
2010
[2] Buitelaar et Al., Towards linguistically grounded
ontologies., ESWC, Springer, 200
[3] Weikum et Al, From Information to Knowledge –
Harvesting Entities and Relationships from Web
Sources, Principle Database Systems, 2010
[4] Weld et al., Using Wikipedia to bootstrap open
information extraction, Sigmod Record, 2008