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Title:Entity-relation search: context pattern driven relation ranking
Author(s):Zhang, Tianxiao
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Entity Relation Search
Context Pattern
PubMed Data
Abstract:A traditional page link-based search system is not adequate for users intending to query data efficiently. For instance, emergent phenomena reveal that some entity-based search engines, such as EntityRank, directly return answers (target entities) to users instead of web pages. Most of the time, however, compared to searching for interested entities, users more often focus on relationships among entities. To our knowledge, there is only one web search system that automatically extracts relations from massive unstructured corpora. This system is referred to as OpenIE, which indeed brings us one step closer to an entity relation-based system. Nevertheless, its system extracts only direct relations between a pair of entities and ranks simply by occurrence frequency. The monotone pattern extraction, adopted in their relation phrase extraction model, provides high quality entity relations but also fail to return many potential true relations in the corpus, which has been explained in Section 4.2 and affects recall significantly shown in 5.3. In addition, it is difficult for users to retrieve their interested and true relations from massive relation candidate set without a qualified ranking model. Consequently, there still exists a gap between the system and users for retrieving entity relations efficiently by a simple query. To assist users to find their interested relations efficiently, this thesis specifically focuses on the core challenges of the ranking model. Naturally, the quality of each relation candidate is largely relevant to its context. Thus, to evaluate various conditions, a novel idea of context patterns driven ranking has been introduced. After evaluating our online prototype on millions of PubMed medical abstracts, we show that our system performs better than the OpenIE system on both precision and recall. Note that this rich and novel system is the product of a collaborative team effort comprised of the following members: Zequn Zhang, Jiarui Xu, and Varun Berry, and supervised by Professor Kevin Chang.
Issue Date:2016-04-28
Rights Information:Copyright 2016 Tianxiao Zhang
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05

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