Methods for Domain-Independent Information Extraction from the Web: An Experimental Comparison

Our KNOWITALL system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an autonomous, domain-independent, and scalable manner. In its first major run, KNOWITALL extracted over 50,000 facts with high precision, but suggested a challenge: How can we improve KNOWITALL's recall and extraction rate without sacrificing precision? This paper presents three distinct ways to address this challenge and evaluates their performance. Rule Learning learns domain-specific extraction rules. Subclass Extraction automatically identifies sub-classes in order to boost recall. List Extraction locates lists of class instances, learns a "wrapper" for each list, and extracts elements of each list. Since each method bootstraps from KNOWITALL's domain-independent methods, no hand-labeled training examples are required. Experiments show the relative coverage of each method and demonstrate their synergy. In...

• Type: Conference
• Year: 2004
• Where: AAAI
• Authors: Oren Etzioni, Michael J. Cafarella, Doug Downey, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, Alexander Yates
• Keywords: AAAI 2004, Domain-specific Extraction Rules, Extraction Locates Lists, Intelligent Agents, Subclass Extraction