Modeling UIMA type system using web ontology language - Towards interoperability among UIMA-based NLP tools

Hongfang D Liu, Stephen Wu, Cui Tao, Christopher Chute

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

With the recent development and adoption of NLP framework architectures, NLP modules/tools developed independently in the research community can be adopted as integrated applications. Development of wrappers and interfaces required to adopt NLP modules/tools, however, still requires huge amount of efforts. In this paper, we focus on one NLP framework architecture, UIMA (Unstructured Information Management Architecture), which defines annotations as types described in a type system and can achieve direct interoperability if a common type system is used. We explore the use of ontology to model UIMA types and argue existing ontology development or reasoning tools can be utilized to understand types (we use types and annotations interchangeably) from existing NLP systems developed under UIMA, define equivalent annotations in different NLP systems, and apply the practice in the ontology community to draw agreements on the definition of common NLP types, thereby achieving better interoperability among NLP modules/tools.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
Pages31-36
Number of pages6
DOIs
StatePublished - 2012
Event2nd International Workshop on Managing Interoperability and Complexity in Health Systems, MIX-HS 2012, Collocated with the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Oct 29 2012

Other

Other2nd International Workshop on Managing Interoperability and Complexity in Health Systems, MIX-HS 2012, Collocated with the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1210/29/12

Fingerprint

Modeling
Language
Natural language processing
Information management
Ontology
Interoperability
World Wide Web
Annotation
Module
Integrated
Wrapper

Keywords

  • Interoperability
  • NLP
  • OWL
  • UIMA

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Liu, H. D., Wu, S., Tao, C., & Chute, C. (2012). Modeling UIMA type system using web ontology language - Towards interoperability among UIMA-based NLP tools. In International Conference on Information and Knowledge Management, Proceedings (pp. 31-36) https://doi.org/10.1145/2389672.2389679

Modeling UIMA type system using web ontology language - Towards interoperability among UIMA-based NLP tools. / Liu, Hongfang D; Wu, Stephen; Tao, Cui; Chute, Christopher.

International Conference on Information and Knowledge Management, Proceedings. 2012. p. 31-36.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, HD, Wu, S, Tao, C & Chute, C 2012, Modeling UIMA type system using web ontology language - Towards interoperability among UIMA-based NLP tools. in International Conference on Information and Knowledge Management, Proceedings. pp. 31-36, 2nd International Workshop on Managing Interoperability and Complexity in Health Systems, MIX-HS 2012, Collocated with the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, United States, 10/29/12. https://doi.org/10.1145/2389672.2389679
Liu HD, Wu S, Tao C, Chute C. Modeling UIMA type system using web ontology language - Towards interoperability among UIMA-based NLP tools. In International Conference on Information and Knowledge Management, Proceedings. 2012. p. 31-36 https://doi.org/10.1145/2389672.2389679
Liu, Hongfang D ; Wu, Stephen ; Tao, Cui ; Chute, Christopher. / Modeling UIMA type system using web ontology language - Towards interoperability among UIMA-based NLP tools. International Conference on Information and Knowledge Management, Proceedings. 2012. pp. 31-36
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