NDLI: Repairing Inconsistent Taxonomies Using MAP Inference and Rules of Thumb

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Repairing Inconsistent Taxonomies Using MAP Inference and Rules of Thumb

Content Provider	ACM Digital Library
Author	Schockaert, Steven Davis, Jesse Alfarone, Daniele De Cock, Martine Merhej, Elie Blondeel, Marjon
Abstract	Several authors have developed relation extraction methods for automatically learning or refining taxonomies from large text corpora such as the Web. However, without appropriate post-processing, such taxonomies are often inconsistent (e.g. they contain cycles). A standard approach to repairing such inconsistencies is to identify a minimally consistent subset of the extracted facts. For example, we could aim to minimize the sum of the confidence weights of the facts that are removed for restoring consistency. In this paper, we present MAP inference as a base method for this approach, and analyze how it can be improved by taking into account dependencies between the extracted facts. These dependencies correspond to rules of thumb such as "if a given fact is wrong then all facts that have been extracted from the same sentence are also likely to be wrong", which we encode in Markov logic. We present experimental results to demonstrate the potential of this idea.
Starting Page	31
Ending Page	36
Page Count	6
File Format	PDF
ISBN	9781450316064
DOI	10.1145/2663792.2663804
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Inconsistency Taxonomy extraction Map inference Markov logic
Content Type	Text
Resource Type	Article

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