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Capturing Knowledge of Emerging Entities from the Extended Search Snippets

Ngwobia, Sunday C.
http://orcid.org/0000-0002-6908-480X
http://rave.ohiolink.edu/etdc/view?acc_num=dayton157309507473671

Abstract Details

2019, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
Google and other search engines feature the entity search by representing a knowledge card summarizing related facts about the user-supplied entity. However, the knowledge card is limited to certain entities which have a Wiki page or an entry in encyclopedias such as Freebase. The current encyclopedias are limited to highly popular entities which are far fewer compared with the emerging entities. Despite the availability of knowledge about the emerging entities on the search results, yet there are no approaches to capture, abstract, summarize, fuse, and validate fragmented pieces of knowledge about them. Thus, in this paper, we develop approaches to capture two types of knowledge about the emerging entities from a corpus extended from top-n search snippets of a given emerging entity. The first kind of knowledge identifies the role(s) of the emerging entity as, e.g., who is s/he? The second kind captures the entities closely associated with the emerging entity. As the testbed, we considered a collection of 20 emerging entities and 20 popular entities as the ground truth. Our approach is an unsupervised approach based on text analysis and entity embeddings. Our experimental studies show promising results as the accuracy of more than 87% for recognizing entities and 75% for ranking them. Beside 87% of the entailed types were recognizable. Our testbed and source codes are available on Github (https://github.com/sunnyUD/research_source_code).
Saeedeh Shekarpour, Ph.D (Committee Chair)
Ju Shen, Ph.D (Committee Member)
Zhongmei Yao, Ph.D (Committee Member)
Tam Nguyen, Ph.D (Committee Member)
James Buckley, Ph.D (Advisor)
59 p.
Computer Science; Information Systems
Emerging entities; Capturing Knowledge; Knowledge Graph; search snippets; Entity embedding; Enhanced corpus, entity types entailment

Recommended Citations

Citations

  • Ngwobia, S. C. (2019). Capturing Knowledge of Emerging Entities from the Extended Search Snippets [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton157309507473671

    APA Style (7th edition)

  • Ngwobia, Sunday. Capturing Knowledge of Emerging Entities from the Extended Search Snippets. 2019. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton157309507473671.

    MLA Style (8th edition)

  • Ngwobia, Sunday. "Capturing Knowledge of Emerging Entities from the Extended Search Snippets." Master's thesis, University of Dayton, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton157309507473671

    Chicago Manual of Style (17th edition)

dayton157309507473671
307
© 2019, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.