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Knowledge discovery is the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data. The complicated computational environment with ultra-large-scale, heterogeneous, highly-dynamic, and semantic-implicit data in the 21st century puts forward new problems and challenges for traditional knowledge discovery. As a solution, Semantic Web and Cloud...
Detecting anomaly nodes from graphs is an important objective in many applications ranging from social networks to World Wide Web. Recently several methods have been proposed to address this problem. A limitation of most of these methods is that they are based on the random walk of the graph, and often fail to be effective. In this paper, we propose a new framework to detect anomaly nodes within a...
In web pages, the reviews are written in natural language and are unstructured-free-texts scheme. Online product reviews is considered as a significant informative resource which is useful for both potential customers and product manufacturers. The task of manually scanning through large amounts of review one by one is computational burden and is not practically implemented with respect to businesses...
Analysis the positive and negative sentiments about each topic of the product are very useful to the customers and manufacturers. In this paper we propose a new topic sentiment mixture model which we call Semi-supervised Co-LDA model to obtain the positive and negative opinions from the reviews about each product. The Semi-supervised Co-LDA can model the topic and sentiment of the product reviews...
Recently, some researchers have found that the abounding search engines cannot support exploratory search effectively. In such case, it requires the search engines know better about the imprecise queries provided by the end users. Actually, it's hard for the users to formulate the queries, not alone understand by the engines. However, in our study, we find that the search logs in the web community...
Most of the previous researches on sentiment analysis concentrate on the binary distinction of positive vs. negative. This paper presents the multi-class sentiment classification problem that attempt to mine the implied rating information from reviews. We use four machine learning methods and two feature selection methods to find out whether or not the multi-class sentiment classification problem...
Along with the rapid popularity of the Internet, crime information on the web is becoming increasingly rampant, and the majority of them are in the form of text. Because a lot of crime information in documents is described through events, event-based semantic technology can be used to study the patterns and trends of web-oriented crimes. In our research project on cyber crime mining, we construct...
The rapid development of Web 2.0 bring the flourish of web reviews. Web reviews are usually released in form of structured records. As the important information source for many popular applications(e.g. monitoring and analysis of public opinion), review records need to be extracted accurately from web pages. To the best of our knowledge, little work in literatures has systemically investigated this...
There are lots of ranking algorithms used in Web information retrieval. However, current algorithms have some problems: these algorithms are based on different calculation formulas to calculate the documents and query similarity or train a lot of training data to get corresponding calculation formula which calculate documents and query similarity. We know that this process is a very complex, and sometimes...
This paper studies the problem of comparing or looking for structured data in DOM trees. The proposed notion of structure descriptor of ordered tree fully represents the structure information of a DOM tree in a serialized style, indicating an efficient method to convert a DOM tree into its node sequence. Based on this notion, this paper produced an algorithm to measure the similarity of two web pages,...
Discovery the association between web pages is an important task as the rapid growth of web data. This article uses the fuzzy method to discover generalized fuzzy association rules among theWeb pages fromWeb logs. In the paper, whether a web page is visited or not and time duration on it are considered two important factors to reflect users' interest and preference. Numerical time duration is fuzzified...
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