The recent emergence of data mining as a major application of machine learning has led to increased interest in fast rule induction algorithms. These are able to e ciently process large numbers of examples, under the constraint of still achieving good accuracy. If e is the number of examples, many rule learners have O(e 4 ) asymptotic time complexity in noisy domains, and C4.5RULES has been emp...

P rostate cancer is the most prevalent nonskin cancer and is the third leading cause of cancer death in the United States. It is estimated to affect millions of men worldwide and is a significant cause of morbidity and mortality. The detection and treatment of prostate cancer has changed significantly since the discovery of prostate-specific antigen (PSA) in the 1970s and the development of the...

The fact that birds have feathers and ice is cold seems trivially true. Yet, most machine-readable sources of knowledge either lack such common sense facts entirely or have only limited coverage. Prior work on automated knowledge base construction has largely focused on relations between named entities and on taxonomic knowledge, while disregarding common sense properties. In this paper, we sho...

Many companies search the Web to learn about their competition and understand their potential customers. But how accurate are these search results? For instance, have you ever submitted the query "SAS", only to get results back about "Scandinavian Airline Systems"? This paper presents a SAS-based solution to accessing and clustering Yahoo! search engine results by using SAS Text Miner. We demon...

Learning from past experience allows a problem solver to increase its solvability horizon from simple to complex problems. For planners, learning involves a training phase during which knowledge is extracted from simple problems. But how are these simple problems constructed? All current learning and problem solving systems require the user to provide the training set. However it is rarely easy...

Because the World Wide Web consists primarily of text information extraction is central to any e ort that would use the Web as a resource for knowledge discov ery We show how information extraction can be cast as a standard machine learning problem and argue for the suitability of relational learning in solving it The implementation of a general purpose relational learner for information extrac...

Can a system that “learns from reading” figure out on it’s own the semantic classes of arbitrary noun phrases? This is essential for text understanding, given the limited coverage of proper nouns in lexical resources such as WordNet. Previous methods that use lexical patterns to discover hypernyms suffer from limited precision and recall. We present methods based on lexical patterns that find h...

Many information extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text. A key problem in constructing these knowledge bases from sources like the web is overcoming the erroneous and incomplete information found in millions of candidate extractions. To solve this problem, we turn to semantics – using ontological constraints between candid...

There is much interest in systems that automatically interact with Internet information sites. Such systems are hard to build, partly because they use hand-crafted wrappers to extract a site’s content. We advocate wrapper induction, a technique for automatically learning wrappers. Our wrapper induction e_~nvironment (WIEN) enables users quickly capture a set of example page; our wrapper learnin...

Knowledge bases have the potential to advance artificial intelligence, but often suffer from recall problems, i.e., lack of knowledge of new entities and relations. On the contrary, social media such as Twitter provide abundance of data, in a timely manner: information spreads at an incredible pace and is posted long before it makes it into more commonly used resources for knowledge extraction....