Towards Populating Knowledge Bases Using Test Analysis with Bootstraping: Extended Abstract

A challenge for building the Semantic Web is to capture the knowledge contained in large document collections without having to perform massive manual knowledge engineering. This paper presents Breeder, a system designed to semi-automatically generate lists of “similar” items based on their contexts in a large corpus. To breed a single item list, the system uses a small set of user-provided seed items as the starting point in a bootstrapping operation which can either proceed automatically until a stopping condition is met or be guided by interactive user feedback as the bootstrapping proceeds. The bootstrapping relies on the “duality” of two orthogonal views of the items and their features, as obtained by text analysis. We also present MultiClassBreeder, which further constrains breeding of the classes as they compete for members with one another.

We have experimented using Breeder to build lexical classes of nominal expressions, a result which is useful for populating a knowledge base with instances of ontological classes. We will describe Breeder’s implementation, including the underlying text analysis and measures taken to make real-time, interactive use feasible. We will discuss our preliminary evaluation of Breeder. We also describe our plans to use Breeder to generate pairs of nominal expressions, referring to class instances, which can be used to populate knowledge bases with the extensions of ontological relations.

By: Roy J. Byrd

Published in: RC22967 in 2003


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