Capturing Knowledge of Emerging Entities from the Extended Search Snippets

Date of Award

2019

Degree Name

Master of Computer Science (M.C.S.)

Department

Department of Computer Science

Advisor/Chair

Advisor: Saeedeh Shekarpour

Second Advisor

Advisor: James Buckley

Abstract

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).

Keywords

Computer Science, Information Systems, Emerging entities, Capturing Knowledge, Knowledge Graph, search snippets, Entity embedding, Enhanced corpus, entity types entailment

Rights Statement

Copyright © 2019, author

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