GUM: The generalized upper model

2021 ◽  
pp. 1-35
Author(s):  
John A. Bateman
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Design Criteria ◽  
Domain Modeling ◽  
Special Issue ◽  
Paper Briefly ◽  
Mediating Role ◽  
Domain Specific

GUM is a linguistically-motivated ontology originally developed to support natural language processing systems by offering a level of representation intermediate between linguistic forms and domain knowledge. Whereas modeling decisions for individual domains may need to be responsive to domain-specific criteria, a linguistically-motivated ontology offers a characterization that generalizes across domains because its design criteria are derived independently both of domain and of application. With respect to this mediating role, the use of GUM resembles (and partially predates) the adoption of upper ontologies as tools for mediating across domains and for supporting domain modeling. This paper briefly introduces the ontology, setting out its origins, design principles and applications. The example cases for this special issue are then described, illustrating particularly some of the principal differences and similarities of GUM to non-linguistically motivated upper ontologies.

scholarly journals Interactive Cohort Identification of Sleep Disorder Patients Using Natural Language Processing and i2b2

2015 ◽  
Vol 06 (02) ◽  
pp. 345-363 ◽  
Author(s):  
W. Chen ◽  
R. Kowatch ◽  
S. Lin ◽  
M. Splaingard ◽  
Y. Huang
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sleep Disorder ◽  
Language Processing ◽  
Domain Knowledge ◽  
Identification System ◽  
Sleep Study ◽  
Domain Specific ◽  
Cohort Identification ◽  
Document Structures

SummaryNationwide Children’s Hospital established an i2b2 (Informatics for Integrating Biology & the Bedside) application for sleep disorder cohort identification. Discrete data were gleaned from semi-structured sleep study reports. The system showed to work more efficiently than the traditional manual chart review method, and it also enabled searching capabilities that were previously not possible.Objective: We report on the development and implementation of the sleep disorder i2b2 cohort identification system using natural language processing of semi-structured documents.Methods: We developed a natural language processing approach to automatically parse concepts and their values from semi-structured sleep study documents. Two parsers were developed: a regular expression parser for extracting numeric concepts and a NLP based tree parser for extracting textual concepts. Concepts were further organized into i2b2 ontologies based on document structures and in-domain knowledge.Results: 26,550 concepts were extracted with 99% being textual concepts. 1.01 million facts were extracted from sleep study documents such as demographic information, sleep study lab results, medications, procedures, diagnoses, among others. The average accuracy of terminology parsing was over 83% when comparing against those by experts. The system is capable of capturing both standard and non-standard terminologies. The time for cohort identification has been reduced significantly from a few weeks to a few seconds.Conclusion: Natural language processing was shown to be powerful for quickly converting large amount of semi-structured or unstructured clinical data into discrete concepts, which in combination of intuitive domain specific ontologies, allows fast and effective interactive cohort identification through the i2b2 platform for research and clinical use.Citation: Chen W, Kowatch R, Lin S, Splaingard M, Huang Y. Interactive cohort identification of sleep disorder patients using natural language processing and i2b2. Appl Clin Inf 2015; 6: 345–363http://dx.doi.org/10.4338/ACI-2014-11-RA-0106

scholarly journals Introduction to the Integrated Domain Modeling Toolset

2014 ◽  
Vol 16 (1) ◽  
pp. 13-18
Author(s):  
Armands Slihte ◽  
Juan Manuel Cueva Lovelle
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
System Analysis ◽  
Knowledge Engineering ◽  
Domain Modeling ◽  
Model Driven ◽  
Modeling Task

Abstract This paper describes the Integrated Domain Modeling approach and introduces the supporting toolset as a solution to the complex domain-modeling task. This approach integrates artificial intelligence (AI) and system analysis by exploiting ontology, natural language processing (NLP), use cases and model-driven architecture (MDA) for knowledge engineering and domain modeling. The IDM toolset provides the opportunity to automatically generate the initial AS-IS model from the formally defined domain knowledge. In this paper, we describe in detail the scope, architecture and implementation of the toolset.

scholarly journals Natural language processing methods for knowledge management—Applying document clustering for fast search and grouping of engineering documents

2021 ◽  
pp. 1063293X2098297
Author(s):  
Ivar Örn Arnarsson ◽  
Otto Frost ◽  
Emil Gustavsson ◽  
Mats Jirstrand ◽  
Johan Malmqvist
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Clustering Algorithms ◽  
Document Clustering ◽  
Unstructured Data ◽  
Free Text ◽  
Engineering Change ◽  
Engineering Documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested.

scholarly journals Special issue on deep learning for natural language processing

Computing ◽  
2020 ◽  
Vol 102 (3) ◽  
pp. 601-603
Author(s):  
Wei Wei ◽  
Jinsong Wu ◽  
Chunsheng Zhu
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Special Issue

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

Natural Language Processing

1996 ◽  
Vol 16 ◽  
pp. 70-85 ◽  
Author(s):  
Thomas C. Rindflesch
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Domain Knowledge ◽  
The State ◽  
Point Of View ◽  
Computer Applications ◽  
Significant Progress ◽  
Future Directions

Work in computational linguistics began very soon after the development of the first computers (Booth, Brandwood and Cleave 1958), yet in the intervening four decades there has been a pervasive feeling that progress in computer understanding of natural language has not been commensurate with progress in other computer applications. Recently, a number of prominent researchers in natural language processing met to assess the state of the discipline and discuss future directions (Bates and Weischedel 1993). The consensus of this meeting was that increased attention to large amounts of lexical and domain knowledge was essential for significant progress, and current research efforts in the field reflect this point of view.

The Applications of Description Logics in Natural Language Processing

2011 ◽  
Vol 181-182 ◽  
pp. 236-241
Author(s):  
Xian Yi Cheng ◽  
Chen Cheng ◽  
Qian Zhu
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Description Logic ◽  
Description Logics ◽  
Semantic Interpretation ◽  
Natural Language Semantic ◽  
Ontology Language ◽  
Basic Ideas

As a sort of formalizing tool of knowledge representation, Description Logics have been successfully applied in Information System, Software Engineering and Natural Language processing and so on. Description Logics also play a key role in text representation, Natural Language semantic interpretation and language ontology description. Description Logics have been logical basis of OWL which is an ontology language that is recommended by W3C. This paper discusses the description logic basic ideas under vocabulary semantic, context meaning, domain knowledge and background knowledge.

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