Organizing and searching the world wide web of facts -- step two: harnessing the wisdom of the crowds

ABSTRACT

As part of a large effort to acquire large repositories of facts from unstructured text on the Web, a seed-based framework for textual information extraction allows for weakly supervised extraction of class attributes (e.g., side effects and generic equivalent for drugs) from anonymized query logs. The extraction is guided by a small set of seed attributes, without any need for handcrafted extraction patterns or further domain-specific knowledge. The attributes of classes pertaining to various domains of interest to Web search users have accuracy levels significantly exceeding current state of the art. Inherently noisy search queries are shown to be a highly valuable, albeit unexplored, resource for Web-based information extraction, in particular for the task of class attribute extraction.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Experimentation

Keywords:
class attributes, fact extraction, knowledge acquisition, named entities, unstructured text, web search queries