Mining topic-specific concepts and definitions on the web

Abstract

Traditionally, when one wants to learn about a particular topic, one reads a book or a survey paper. With the rapid expansion of the Web, learning in-depth knowledge about a topic from the Web is becoming increasingly important and popular. This is also due to the Web's convenience and its richness of information. In many cases, learning from the Web may even be essential because in our fast changing world, emerging topics appear constantly and rapidly. There is often not enough time for someone to write a book on such topics. To learn such emerging topics, one can resort to research papers. However, research papers are often hard to understand by non-researchers, and few research papers cover every aspect of the topic. In contrast, many Web pages often contain intuitive descriptions of the topic. To find such Web pages, one typically uses a search engine. However, current search techniques are not designed for in-depth learning. Top ranking pages from a search engine may not contain any description of the topic. Even if they do, the description is usually incomplete since it is unlikely that the owner of the page has good knowledge of every aspect of the topic. In this paper, we attempt a novel and challenging task, mining topic-specific knowledge on the Web. Our goal is to help people learn in-depth knowledge of a topic systematically on the Web. The proposed techniques first identify those sub-topics or salient concepts of the topic, and then find and organize those informative pages, containing definitions and descriptions of the topic and sub-topics, just like those in a book. Experimental results using 28 topics show that the proposed techniques are highly effective.