Natural language descriptors used for categorizations are present from folksonomies to ontologies. While some descriptors are composed of simple expressions, other descriptors have complex compositional patterns (e.g. ‘French Senators Of The Second Empire’, ‘Churches Destroyed In The Great Fire Of London And Not Rebuilt’). As conceptual models get more complex and decentralized, more content is transferred to unstructured natural language descriptors, increasing the terminological variation, reducing the conceptual integration and the structure level of the model. This work describes a formal representation for complex natural language category descriptors (NLCDs). In the representation, complex categories are decomposed into a graph of primitive concepts, supporting their interlinking and semantic interpretation. A category extractor is built and the quality of its extraction under the proposed representation model is evaluated.

1. On the Semantic Representation and
Extraction of Complex Category
Descriptors
André Freitas, Rafael Vieira, Edward Curry, Danilo
Carvalho, João C. Pereira da Silva
Insight Centre for Data Analytics
NLDB 2014
Montpellier, France

2. Outline
 Motivation
 Extracting Natural Language Category Descriptors (NLCDs)
 Evaluation
 Summary
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3. Motivation
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4. Big Data
 Vision: More complete data-based picture of the world for
systems and users.
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5. “Schema” Growth & Complexity
 Fundamental shift in the database landscape
 How to build large ‘schemas’?
10s-100s attributes
1,000s-1,000,000s attributes
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6. Target Motivational Scenario: Wikipedia
 Decentralized content generation
 300,000 editors have edited Wikipedia more than 10 times
 > 280,000 distinct Natural Language Category Descriptors
(NLCDs)
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7. Natural Language Category Descriptors
(NLCDs)
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8. NLCDs
 Natural Language Category Descriptors (NLCDs) are
natural language descriptors for sets
 Simple NLCDs:
- ‘People’
- ‘Countries’
- ‘Films’
 Complex NLCDs:
- ‘French Senators Of The Second Empire’
- ‘United Kingdom Parliamentary Constituencies Represented
By A Sitting Prime Minister’
 Goal:
- Parse NLCDs into an integrated structured graph
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9. Assumptions
N
L
C
D
 NLCDs as a more syntactically tractable subset of natural
language
 NLCDs as a low effort interface for structuring a domain of
discourse
IE
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10. Formality vs. Usability Spectrum
NLCDss NLCD graphss
Information Extraction
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NLCD graphss

11. Applications
 Database Creation
 Semantic Annotation
 Entity/Semantic Search
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12. Other Examples
 IFRS and US GAAP
- ‘Partially owned properties’
- ‘Residential portfolio segment’
- ‘Assets arising from exploration for and evaluation of mineral
resources’
- ‘Key management personnel compensation’
- ‘Other long-term employee benefits’
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13. Extracting Natural Language
Category Descriptors (NLCDs)
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14. Natural Language Category
Descriptors
What is Big Data?
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15. Core Features
 Manual analysis of 10,000 NLCDs.
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16. Features/Core Lexical Categories
Distribution
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17. Number of distinct POS Tag patterns
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18. Graph Representation Model
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19. Focus of the Representation
 Taxonomic Structure
 Context Representation (Open Relation Extraction)
- Reification-based

20. Examples
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21. Examples
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22. Examples
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23. Examples
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24. NLCD Extractor
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25. NLCD Extractor: POS Tagging
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26. NLCD Extractor: Segmentation
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27. NLCD Extractor: Named Entity
Recognition
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28. NLCD Extractor: Core Detection
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29. NLCD Extractor: WSD
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30. NLCD Extractor: Entity Linking
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Dbpedia

31. NLCD Extractor: RDF Representation
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Dbpedia

32. RDF Representation
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33. Evaluation
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34. Evaluation Setup
 Total of 287,957 English Wikipedia categories (Open Domain
scenario)
 Selected random sample of 2,696 categories
 Manual evaluation of the core extraction features
- Entity segmentation
- Relation identification
- Unary operators
- Specialization relations
- Category core identification
- Entity core identification
- Word Sense Disambiguation (WordNet)
- Entity linking (DBpedia)
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35. Results
 Performance:
- (i) graph extraction time: 9.8 ms per graph
- (ii) word sense disambiguation: 121.0 ms per word
- (iii) entity linking: 530.0 ms per link
* i5-3317U (1.70GHz) CPU computer with 4GB RAM (4 core, 2 threads per core).
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36. Summary
 NLCDs can provide a more tractable (from the IE perspective)
natural language interface for structuring large KBs
 We developed an approach for the representation, extraction
and integration of NLCDs
- ~75% extraction accuracy
 Limitations:
- Need for a more principled and formal definition for a NLCD
- Need for a better entity recognition and linking approach
 Future Work: evaluation under a domain-specific scenario
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