Guest Editor's Introduction: Special Issue on Web Content Mining

The added-value of search engines is, apparently, undoubted. Their rapid evolution over the last decade has transformed them into the most important source of information and knowledge. From the end user side, search engine success implies correct results in fast and accurate manner, while also ranking of search results on a given query has to be directly correlated to the user anticipated response. From the content providers' side (i.e. websites), better ranking in a search engine result set implies numerous advantages like visibility, visitability, and profit. This is the main reason for the flourishing of Search Engine Optimization (SEO) techniques, which aim towards restructuring or enriching website content, so that optimal ranking of websites in relation to search engine results is feasible. SEO techniques are becoming more and more sophisticated. Given that internet marketing is extensively applied, prior quality factors prove insufficient, by themselves, to boost ranking and the improvement of the quality of website content is also introduced. Current paper discusses such a SEO mechanism. Having identified that semantic analysis has not been widely applied in the field of SEO, a semantic approach is adopted, which employs Latent Dirichlet Allocation techniques coupled with Gibbs Sampling in order to analyze the results of search engines based on given keywords. Within the context of the paper, the developed SEO mechanism LDArank is presented, which evaluates query results through state-of-the-art SEO metrics, analyzes results' content and extracts new, optimized content.

Web is one of the most widespread platforms for information exchange today, as it is easier to publish documents. As the number of users and providers increases, the number of documents grows, searching for information becomes a difficult and time-consuming process. Web mining uses various data mining techniques to discover useful knowledge from Web hyperlinks, page content and usage log file. The mining tools are used to scan the HTML documents, images, and text, the results is provided for the search engines.It can assist search engines in providing productive results of each search in order of their relevance. In this paper, we brief introduction to the concepts related to data mining, web mining and then an overview of different Web mining tools. We conclude by presenting a comparative table of these tools based on some pertinent criteria.

In the current development, millions of clients are accessing daily the internet and World Wide Web (WWW) to search the information and achieve their necessities. Web mining is a technique to automatic discovers and Extract information from www. Websites are a common stage to discussion the information between users. Web mining is one of the applications of Data mining techniques for extracting information from web data. The area of web mining is web content mining, web usage mining and web structure mining. These three category focus on Knowledge discovery from web. Web content mining involves technique for summarization, classification, clustering and the process of extracting or discovering useful information web pages, it includes image, audio, video and metadata. Web usage mining is the process of extracting information from web server logs. Web structure mining it is the process of using graph theory to analyse the node and connection structure of a website and deals with the hyperlink structure of web. Web mining is a part of data mining which relates to various research communities such as information retrieval, database management systems and Artificial intelligence.

2006

Web is a vast data repository. By mining from this data efficiently, we can gain valuable knowledge. Unfortunately, in addition to useful content there are also many Web documents considered harmful (e.g. pornography, terrorism, illegal drugs). Web mining that includes three main areas -content, structure, and usage mining -may help us detect and eliminate these sites. In this paper, we concentrate on applications of Web content and Web structure mining. First, we introduce a system for detection of pornographic textual Web pages. We discuss its classification methods and depict its architecture. Second, we present analysis of relations among Czech academic computer science Web sites. We give an overview of ranking algorithms and determine importance of the sites we analyzed.

Ijca Proceedings on National Workshop Cum Conference on Recent Trends in Mathematics and Computing 2011, 2012

A Web Search Engine maintains and catalogs the content of Web pages in order to make them easier to find and browse. There are many Search Engines which are similar, differentiates from the other by the methods for scouring, storing, and retrieving information from the Web. Usually Search Engines search through Web pages for specified keywords, in response they return a list of containing specified keywords documents. After finding the list of specified keywords documents, list is sorted by relevance criteria which try to put at the very first positions the documents that best match the user's query. The usefulness of a search engine to most people is based on the relevance of results it retrieves from the web. This paper tries to address some issues regarding some of the major challenges faced by Search Engines, since the size of the Web is rapidly growing.

2019

The article is devoted to solving the set of problems related to natural language texts semantic analysis. The following problems are addressed: automation of generating metadata files describing the semantic representation of a web page; semantic network construction for a given set of texts; semantic search execution for a given set of texts using metadata files; and semantic network export to RDF format. The algorithms for knowledge extraction from text, semantic network construction and query execution on a given semantic network are described. The lexico-syntactic patterns method was used as a basis to approach these problems. A specification for describing lexico-syntactic patterns has been developed and a pattern interpreter based on the morphological dictionary of the Ukrainian language has been created as a part of the software implementation of the method. Experimental studies have been carried out for the «classification of living organisms» subject environment set of pat...

Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) - WIMS '14, 2014

The Web has an enormous influence on our everyday life. Thus, more efficient intelligent approaches and technologies are needed to realize the Web's full potential. Intelligence can be achieved by making the Web aware of the semantics of its own structures and content and by applying intelligent techniques to effectively access web resources. The Semantic Web was one of the significant steps towards bringing Intelligence to the Web. Based on this starting point, the Web Intelligence, Mining, and Semantics (WIMS) community works toward researching and implementing the next generation of the intelligent Web for humans and machines. In this editorial, opening the volume of the proceedings of WIMS'14, we review the topics of interest for the WIMS community, analyze the response of this year's authors to these topics, and present the program of the conference. We hope that this material will be useful for a reader as a key for the structure and content of these proceedings.

THE WEB IS CURRENTLY A DISTRIBUTED MASS OF SIMPLE HYPERTEXT DOCUMENTS. LARGE-SCALE WEB SEARCH ENGINES SUCH AS ALTAVISTA AND INFOSEEK ARE NOT CAPABLE OF RETRIEVING PRECISE INFORMATION RESULTS. THE AUTHORS PRESENT A NEW TOOL, WEBKB, THAT INTERPRETS SEMANTIC STATEMENTS STORED IN WEB-ACCESSIBLE DOCUMENTS.

Semantic Web Engineering in the Knowledge Society, 2009

In this chapter we discuss approaches to find, extract, and structure information from natural language texts on the Web. Such structured information can be expressed and shared using the standard Semantic Web languages and hence be machine interpreted. In this chapter we focus on two tasks in Web information extraction. The first part focuses on mining facts from the Web, while in the second part, we present an approach to collect community-based meta-data. A search engine is used to retrieve potentially relevant texts. From these texts, instances and relations are extracted. The proposed approaches are illustrated using various case-studies, showing that we can reliably extract information from the Web using simple techniques.