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. 2018 Winter;104(4):31-78.

Generating Domain Terminologies using Root- and Rule-Based Terms

Jacob Collard  1 , T N Bhat  2 , Eswaran Subrahmanian  3   4 , Ram D Sriram  3 , John T Elliot  2 , Ursula R Kattner  2 , Carelyn E Campbell  2 , Ira Monarch  4   5
Affiliations

Generating Domain Terminologies using Root- and Rule-Based Terms

Jacob Collard et al. J Wash Acad Sci. 2018 Winter.

Abstract

Motivated by the need for flexible, intuitive, reusable, and normalized terminology for guiding search and building ontologies, we present a general approach for generating sets of such terminologies from natural language documents. The terms that this approach generates are root- and rule-based terms, generated by a series of rules designed to be flexible, to evolve, and, perhaps most important, to protect against ambiguity and standardize semantically similar but syntactically distinct phrases to a normal form. This approach combines several linguistic and computational methods that can be automated with the help of training sets to quickly and consistently extract normalized terms. We discuss how this can be extended as natural language technologies improve and how the strategy applies to common use-cases such as search, document entry and archiving, and identifying, tracking, and predicting scientific and technological trends.

Keywords: dependency parsing; natural language processing; ontology generation; search; terminology generation; unsupervised learning.

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Figures

Figure 1.
Figure 1.
Terminology Generation
Figure 2:
Figure 2:
Dependency representation of the tree’s red leaves
Figure 3:
Figure 3:
Collapsed representation of the leaves of the tree that are red
Figure 4:
Figure 4:
Collapsed representation of the red leaves of the tree
Figure 5:
Figure 5:
Normalized tree for RED-TREE_LEAF
Figure 6:
Figure 6:
Dependency representation of temperature variations of the elastic constants
Figure 7:
Figure 7:
Normalized representation of temperature variations of the elastic constants
Figure 8.
Figure 8.
A strategy for use-cases to create and manage root- and rule-based terminologies
Figure 9.
Figure 9.
Visualizing Term realtionships
Figure 10:
Figure 10:
Dependency representation of “the leaves of the tree that are red”
Figure 11:
Figure 11:
Dependency representation of “the read leaves of the tree”
Figure 12:
Figure 12:
Nomralized dependency representation of “the tree’s red leaf”
Figure 13:
Figure 13:
Normalized dependency representation of “the leaves of the tree that are red”
Figure 14:
Figure 14:
Collapsed representation of the tree’s red leaves
Figure 15:
Figure 15:
Collapsed representation of the tree’s leaves that are red
Figure 16:
Figure 16:
Collapsed representation of the leaves of the tree that are red
Figure 17:
Figure 17:
Collapsed representation of the red leaves of the tree
Figure 18:
Figure 18:
Universal representation for sentences 1 through 4)
See this image and copyright information in PMC

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References

    1. Berners-Lee Tim, Hendler James, and Lassila Ora. 2001. “The Semantic Web.” Scientific American.
    1. Bhat Talapady N. 2010. “Building Chemical Ontology for Semantic Web Using Substructures Created by Chem-BLAST.” International Journal on Semantic Web and Information Systems 6 (3): 22–37.
    1. Bhat Talapady N., Bartolo Laura M., Kattner Ursula R., Campbell Carelyn E., and Elliott John T.. 2015. “Strategy for Extensible, Evolving Terminology for the Materials Genome Initiative Efforts.” Journal of Materials, no. 8: 1866–75.
    1. Clark Herbert H., and Deanna Wilkes-Gibbs. 1986. “Referring as a Collaborative Process.” Cognition, no. 22: 1–39. - PubMed
    1. Coulter Neal, Monarch Ira, and Konda Suresh. 1998. “Software Engineering as Seen Through Its Research Literature: A Study in Co-Word Analysis.” Journal of the Association for Information Science and Technology 49 (13). New York, NY: John Wiley & Sons, Inc.: 1206–23.

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