Small business is "doomed"
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Why RDF model is different from the XML model
Is Semantic Web Technology Scalable?
"Scalable" is a politician of a word. It has attractiveness to obtain solid backing from diverse factions- it has something to offer both the engineericans and the marketerists. At the same time it has the dexterity to mean different things to different people, so that the sales team can always argue that the competition's product lacks "scalability". The word even supports multiple mental images- you can think of soldiers scaling a wall or climbers scaling a mountain; a more correct image is that of scaling a picture to making it bigger. Even technology cynics can get behind the word "scalable": if a technology is scalable, they would argue, that means it hasn't been scaled.
The fact is that scalability is a complex attribute, more easily done in the abstract than in the concrete. I've long been a cynic about scalability. A significant fraction of engineers who worry about scalability end up with solutions that are too expensive or too late to meet the customer problems at hand, or else they build systems that scale poorly along an axis of unexpected growth. Another fraction of engineers who worry too little about scalability get lucky and avoid problems by the grace of Moore's Law and its analogs in memory storage density, processor power and bandwidth. On the other hand, ignorance of scalability issues in the early phases of a design can have catastrophic effects if a system or service stops working once it grows beyond a certain size.
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Reseller ratings and news
Resellers are firms that are in the business of reselling products to end consumers. These resellers are also known as "Value Added Resellers" (VARs). Implementing a Sales Commission System in a Reseller Industry give rise to unique set of challenges
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Hierarchical Classification of the Web
With the exponential growth of information on the internet and intranets, it is becoming increasingly difficult to find and organize relevant materials. More and more, simple text retrieval systems are being supplemented with structured organizations. Since the 19th century, librarians have used classification systems like Dewey and Library of Congress subject headings to organize vast amounts of information. More recently, web directories such as Yahoo! and LookSmart have been used to classify web pages. Structured directories support browsing and search, but the manual nature of the directory compiling process makes it difficult to keep pace with the ever increasing amount of information. Our work looks at the use of automatic classification methods to supplement human effort in creating structured knowledge hierarchies.
Although many real world classification systems have complex hierarchical structure (e.g., MeSH, U.S. Patents, Yahoo!, LookSmart), few learning methods capitalize on this structure. Most of the approaches mentioned above ignore hierarchical structure and treat each category or class separately, thus in effect “flattening” the class structure. A separate binary classifier is learned to distinguish each class from all other classes. The binary classifiers can be considered independently, so an item may fall into none, one, or more than one category. Or they can be considered as an m-ary problem, where the best matching category is chosen. Such simple approaches work rather well on small problems, but they are likely to be difficult to train when there are a large number of classes and a very large number of features. By utilizing known hierarchical structure, the classification problem can be decomposed into a set of smaller problems corresponding to hierarchical splits in the tree. Roughly speaking, one first learns to distinguish among classes at the top level, then lower level distinctions are learned only within the appropriate top level of the tree. Each of these sub-problems can be solved much more efficiently, and hopefully more accurately as well.
Heterogeneous Web Documents
Currently, the information of the World Wide Web is mainly accessed with search engines. Recent studies showed that the usual keyword-in-context lists are not always the best choice for presenting the results. Additionally, an increasing amount of people uses search engines, without knowing how to formulate good queries. This master thesis therefore describes the design and implementation of a question answering system that generates a summarized answer for open-domain natural language queries. The system aims to increase the quality of existing systems by using heterogeneous documents from Wikipedia, Yahoo! Answers and Frequently Asked Questions. Three main tasks have been identified: The first is passage extraction, which relies on semantic similarity and Hidden Markov Models for identifying irrelevant passages. Passage extraction obtains an average precision of 98% and recall of 81%. The second task calculates different clusterings for assigning a topic to each document. The best results have been found by combining k-means and Newman's community clustering, which results in an average clustering purity of 88%. The final step combines three different rankings and selects the top ranked sentences for composing the summary. Besides the textual summary that is particularly useful for answering definition questions, a list of frequent n-grams and URLs is created to support also factoid and list questions. While working with heterogeneous data, combining different approaches has been observed to be crucial for benefiting from the individual advantages and alleviate differences in format, length, style, focus, relevance as well as problems of ambiguity and redundancy within the documents. An evaluation of the resulting summaries has been done by comparing the system s ROUGE scores with the two systems MEAD and START. User-generated answers from ask.com and Answerbag are used as a reference corpus. The evaluation shows that the system obtains the highest F-measure scores and leads to overall useful summaries. A t-test showed that the system s ROUGE score improvements are significant.