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A Semantic Best-Effort Approach for
Extracting Structured Discourse
Graphs from Wikipedia
André Freitas, Danilo Carvalho, J. C. P. da Silva,
Sean O’Riain, Edward Curry
© Copyright 2009 Digital Enterprise Research Institute. All rights reserved.

2. Outline
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 Motivation
 Representation
 Requirements
 Semantic Best-effort Representation
 Extraction
 Graphia Extractor
 Preliminary Evaluation
 Extraction Examples
 Conclusion

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Motivation

4. Motivation
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5. Motivation
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6. Motivation
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7. Motivation
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Natural language texts Linked Data
 No terminological or structural
regularity
 Terminological and
structural regularity
 Highly contextualized
 Shared semantic agreement
 Complex semantic dependency between data consumers
relations
 Ambiguity
Information selection/normalization
- vocabulary constraints
+ entity-centric
+ pay-as-you-go data semantics
= semantic best-effort

8. Motivation
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 Vocabulary-independent (schema-free queries)
 How to abstract users from knowing the data
representation?
 Semantic matching
 Schemaless databases in the limit demands
vocabulary-independency
 How information extraction is reshaped in this
scenario?

9. Motivational Scenario
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What is the relationship between Barack Obama and Indonesia?
Semantic Best-
effort Extraction
Sentence: From age six
to ten, Obama attended
local schools in Jakarta, Entity-centric text
including Besuki Public representation
School and St Francis
Assisi School.

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Representation

11. Computational Linguistics Perspective
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 What is already there to represent NL?
 Discourse Representation Theory (DRT)
 Semantic Role Labeling (SRL)

12. Discourse Representation Theory (DRT)
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 “The key idea behind (...) Discourse Representation Theory is
that each new sentence of a discourse is interpreted in the
context provided by the sentences preceding it.”
van Eijck and Kamp
 Models propositions in discourse (multiple sentences).
 Discourse representation structures (DRS).
John enters a card. Every card is green.

13. Semantic Role Labeling (SRL)
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 Shallow semantic parsing.
 Detection of arguments associated with a predicate.
 Associated semantic types to arguments.
Bill cut his hair with a razor
[Agent Bill] cut [Patient his hair] [Instrument with a razor.]

14. Semantic Best-Effort
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 Objectives:
 Entity-centric & Standardized: easier to integrate with
other resources
 Remove the formal constraints and the ‘baggage’ from
existing approaches
 Representation robust to extraction limitations/errors

15. Semantic Best-Effort Requirements
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 Text segmentation into (s,p,o)s
 Context representation
 Conceptual model independency
 Resolve co-references (pay-as-you-go)
 Represent recurrent discourse structures
 Standardized representation (RDF(S))
 Principled interpretation (compositionality)

16. Examples
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- Text segmentation into (s,p,o)s
- Context representation
- Resolve co-references (pay-as-you-go)
- Conceptual model independency

17. Examples
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- Context representation

18. Examples
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19. Examples
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- Represent recurrent discourse structures

20. Examples
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- Represent recurrent discourse structures
- Resolve co-references (pay-as-you-go)

21. Examples
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- Represent recurrent discourse structures

22. SDG Elements
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 Named, non-named entities and properties
 Quantifiers & operators
 Triple Trees
 Context elements
 Co-Referential elements
 Resolved & normalized entities

23. Graph Patterns
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24. [[Interpretation]]
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 Graph traversal – deref sequence

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Extraction

26. SBE Graph Extraction Tool
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27. Extraction Pipeline Architecture
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 Subject
 Predicate
 Object
 Prepositional phrase & Noun complement
 Reification
 Time

28. Preliminary Evaluation
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 1033 relations (triples) from 150 sentences from 5
randomly selected Wikipedia articles
 Manually classified the graphs: error categories
and accuracy.

29. Preliminary Evaluation
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30. Other Extraction Examples
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31. Other Extraction Examples
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32. Other Extraction Examples
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33. Other Extraction Examples
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34. Other Extraction Examples
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35. Other Extraction Examples
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36. Other Extraction Examples
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37. Other Extraction Examples
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38. Other Extraction Examples
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39. Other Extraction Examples
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40. Other Extraction Examples
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41. Other Extraction Examples
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42. Conclusion
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 Main direction for improvement is completeness
 Aligned with the pay-as-you-go scenario
 Still need to define clear criteria for what you can’t extract
 There is still a long way to go (e.g. complex subordination)
 Investigation using existing n-ary relations patterns
 Context (reification) should be a first-class citizen in the
representation of natural language
 Focus on getting the semantic pivots (rigid designators) right
 Worth putting effort on enumerable patterns (timestamps,
operators)