1. COMP3725
Knowledge Enriched Information
Systems
Lecture 13: Semantic Augmentation
Dhavalkumar Thakker (Dhaval)
School of Computing, University of Leeds
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2. Outline
• Semantic Augmentation
– What
– Why
– How
• Existing systems & services for Semantic
Augmentation
• Challenges
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3. Semantic Augmentation
• From:
(…) Upon their return, Lennon and McCartney
went to New York to announce the formation of
Apple Corps.
• To:
(…) Upon their return, Lennon and McCartney
went to New York to announce the formation of
Apple Corps.
http://dbpedia.org/Ontology/New_York_City
http://dbpedia.org/Ontology/Apple_Corps
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4. Semantic Augmentation
• Semantic augmentation is a process
of attaching semantics to a selected
part of a text to assist automatic
interpretation of the meaning
conveyed by the text.
• Also called semantic annotation,
semantic tagging
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5. It provides additional information
about an existing piece of data.
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6. Why Semantic Augmentation?
• Links to complementary information
– “More about this”
• Show related or similar informatiom
• Reasoning and inferencing offered by
semantics
• Semantic annotation is the glue that ties
ontologies into document spaces –
remember existing web is document web
• Manual metadata production cost is too
high 6

7. GATE for Semantic
Augmentation
• GATE (General Architecture for Text
Engineering) – see gate.ac.uk
• GATE Developer is a development
environment that provides a rich set of
graphical interactive tools for the creation,
measurement and maintenance of software
components for processing human
language.
• See: http://gate.ac.uk/family/developer.html
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8. Overview of Gate Developer
• GATE Developer
• Resources Pane
– applications: groups of processes to run on a
document or corpus
– language resources: corpus, ontologies, schemas
– processing resources: tools that operate on
unstructured text
– datastores: saved documents and resources
• Display Pane: whatever you’re currently working
with.
• See next slide

9. GATE : Interface
Resources
Pane Display
Pane
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10. Processing Resources: ANNIE
• A family of Processing Resources for
language analysis included with GATE
• Stands for A Nearly-New Information
Extraction system.
• Using finite state techniques to implement
various tasks: tokenization, semantic
tagging, verb phrase chunking, and so on.

11. ANNIE IE Modules
http://gate.ac.uk/sale/tao/splitch6.html#chap:annie

12. Some ANNIE Components
• Tokenizer
– word, number, symbol, punctuation, and spaceToken.
• Sentence Splitter
– Segments text into sentences
• Part of Speech Tagger
– produces a part-of-speech tag as an annotation on each word or
symbol – Nouns, verbs etc.
• Gate Morphological Analyser
– detecting morphemes in a piece of text (e.g. car,
caring)
• OntoGazetteer
– Semantic Tagging component – uses ontology

13. Demo:
• From:
(…) Upon their return, Lennon and McCartney
went to New York to announce the formation of
Apple Corps.
• To:
(…) Upon their return, Lennon and McCartney
went to New York to announce the formation of
Apple Corps.
http://dbpedia.org/Ontology/New_York_City
http://dbpedia.org/Ontology/Apple_Corps
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14. Step : Download & Start the
GATE application
• Download GATE from:
http://gate.ac.uk/download/
• Note: the demonstration is using GATE 6.0
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15. Step: From Language Resources
Select
• GATE document-> Make sure that String
content is selected in the last field, see
screenshot below. Name the file “Test”
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16. Paste following text…in the file
• Upon their return, Lennon and McCartney
went to New York to announce the
formation of Apple Corps.
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17. Step: From Processing resources
select following resources
• ANNIE English Tokeniser
• ANNIE Sentence Splitter
• ANNIE POS Tagger
• GATE Morphological Analyser
• Note: For all the above, leave the “Name”
field Empty
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18. Step: From Processing resources
select following resources
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19. Step: From Language Resources
Select
• OWLIM Ontology
– Specify the location of the ontology you would
like to use for semantic augmentation
– For example, we are using dbpedia ontology
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20. OWLIM Ontology window
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21. From Processing Resources
Select
• Select Onto Root Gazetteer
• & specify parameters as follows:
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22. Final steps: Create Corpus
• Go to Language resources and click on GATE Corpus, and
add “Test” document created earlier
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23. Final steps: Create Corpus
Pipeline
• From application
• And add processing resources in order shown below and
press “run this application”
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24. Results: Go to file, Click on Annotation
Set, Annotation List, Lookup
Semantic Augmentation
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25. Other features
• JAPE
– a Java Annotation Patterns Engine, provides
regular-expression based pattern/action rules over
annotations.
– Grammar to detect entities, validate detected
entities, pre & post processing
– Example: “at the Carnegie Stadium”, “at the
Emirates Stadium”, “at the O2 Arena”
– See Tutorial: http://gate.ac.uk/sale/thakker-jape-
tutorial/index.html

26. Some Links
• Home page is http://gate.ac.uk/
• Some good short tutorial videos for getting started:
http://gate.ac.uk/demos/developer-videos/ . These are only
a few minutes each, so they’re fast
• User Guide: http://gate.ac.uk/sale/tao/index.html . This is
apparently for version 7.1, which is a development build,
but again it seems to be fine.
• Lots of documentation :
http://gate.ac.uk/documentation.html
• The wiki: http://gate.ac.uk/wiki/
• JAPE grammar by Dhaval Thakker et al
http://gate.ac.uk/sale/thakker-jape-
tutorial/index.html

27. Challenge: Term Ambiguity
• ...this apple on the palm of my hand...
• ...Apple tried to acquire Palm Inc....
• ...eating an apple sitted by a palm tree...
• What do “apple” and “palm” mean in each case?
• Objective is to recognize entities and disambiguate
their meaning.
DBpedia Spotlight: Shedding Light on the Web of Documents. Pablo Mendes, Max Jakob, Andrés García-Silva,
and Christian Bizer. In: In the Proceedings of the 7th International Conference on Semantic Systems I-Semantics (2011) .
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28. Challenges
• Disambiguation
• Unknown entities
• Ontology learning
• Scale and speed
• Co-referencing

29. Existing Services for Semantic
Augmentation

30. Existing Services for Semantic
Augmentation

31. DBpedia Spotlight
• DBpedia is a collection of entity descriptions
extracted from Wikipedia & shared as linked data
• DBpedia Spotlight uses data from DBpedia and text
from associated Wikipedia pages
• Learns how to recognize that a DBpedia resource
was mentioned
• Given plain text as input, generates annotated text
http://dbpedia-spotlight.github.com/demo/
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32. DBpedia Spotlight
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33. DBpedia Spotlight
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34. References
• DBpedia Spotlight: Shedding Light on the Web of
Documents. Pablo Mendes, Max Jakob, Andrés
García-Silva, and Christian Bizer. In: In the
Proceedings of the 7th International Conference on
Semantic Systems I-Semantics (2011) .
• Introduction to GATE, Dr. Paula Matuszek
• Various resources from gate.ac.uk
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