University of Illinois Urbana-Champaign

Semantic pattern discovery in open information extraction

Chauhan, Aabhas

Content Files
CHAUHAN-THESIS-2020.pdf

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https://hdl.handle.net/2142/108194

Description

Title
Semantic pattern discovery in open information extraction
Author(s)
Chauhan, Aabhas
Issue Date
2020-05-13
Director of Research (if dissertation) or Advisor (if thesis)
Han, Jiawei
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2020-08-26T23:58:49Z
Keyword(s)
  • Information Extraction
  • Pattern Mining
Abstract
Open information extraction (OpenIE) is a novel paradigm that produces structured information from unstructured text with minimum or no supervision. The task involves extracting relevant relation tuples or expressions from a text corpus. Existing methods in the domain tend to produce a large percentage of ill-structured, incomplete or redundant extractions which cannot be directly used in downstream applications, and often fail on sentences with long and complex structures. In this paper, we propose a novel semantic pattern-discovery for OpenIE (SemPatIE) framework which extracts relations in the form of typed textual pattern structures, called meta patterns and groups semantically similar pattern structures. To perform these tasks, the framework uses three techniques: (1) it simplifies complex sentence structures by performing a context-aware sentence segmentation method which splits the dependency graph of sentences at noun or verb level and enables pattern extraction between distantly placed entities; (2) it extracts meta patterns and handles its pattern sparsity problem by introducing a novel idea of iterative frequent pattern mining and nested push-ups; (3) it generates semantic pattern clusters by embedding a multi text-based network between entities, entity types, extracted meta patterns and context words. Experiments show SemPatIE outperforms state-of-the-art OpenIE baselines in handling structurally complex sentences and has a significantly higher recall than existing pattern-based methods. Case studies exhibit the framework's high generalization ability and scalabilty, and effective clustering performance which has direct applications in downstream tasks like knowledge graph construction, evidence mining and truth finding.
Graduation Semester
2020-05
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/108194
Copyright and License Information
Copyright 2020 Aabhas Chauhan

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