فلاح حسن علي العكايشي

The Web is comprised of a vast quantity of text. Modern search engines struggle to index it independent of the structure of queries and type of Web data, and commonly use indexing based on Web‘s graph structure to identify high-quality relevant pages. However, despite the apparent widespread use of these algorithms, Web indexing based on human feedback and document content is controversial. There are many fundamental questions that need to be addressed, including: How many types of domains/websites are there in the Web? What type of data is in each type of domain? For each type, which segments/HTML fields in the documents are most useful? What are the relationships between the segments? How can web content be indexed efficiently in all forms of document configurations? Our investigation of these questions has led to a novel way to use Wikipedia to find the relationships between the query structures and document configurations throughout the document indexing process and to use them to build an efficient index that allows fast indexing and searching, and optimizes the retrieval of highly relevant results. We consider the top page on the ranked list to be highly important in determining the types of queries. Our aim is to design a powerful search engine with a strong focus on how to make the first page highly relevant to the user, and on how to retrieve other pages based on that first page. Through processing the user query using the Wikipedia index and determining the type of the query, our approach could trace the path of a query in our index, and retrieve specific results for each type. We use two kinds of data to increase the relevancy and efficiency of the ranked results: offline and real-time

In this paper, we present an intelligent web retrieval system that is able to rank webpages by using Wikipedia knowledge to enhance a standard vector space model. Our index contains separate information about the frequency of the terms in Wikpedia articles, in home pages, and in other types of web pages, instead of using a generic term frequency for the whole text collection. We also filter out spam. We present results on the ClueWeb collection, for two sets of queries, for an adhoc retrieval task and for a diversity task (which aims at retrieving not only relevant information, but also information for different aspects of the queries).

Indexing is a crucial technique for dealing with the massive amount of data present on the web. In our third participation in the web track at TREC 2012, we explore the idea of building an efficient query-based indexing system over Web page collection. Our prototype explores the trends in user queries and consequently indexes texts using particular attributes available in the documents. This paper provides an in-depth description of our approach for indexing web documents efficiently; that is, topics available in the web documents are discovered with the assistance of knowledge available in Wikipedia. The well-defined articles in Wikipedia are shown to be valuable as a training set when indexing Webpages. Our complex index structure also records information from titles and urls, and pays attention to web domains. Our approach is designed to close the gaps in our approaches from the previous two years, for some queries. Our framework is able to efficiently index the 50 million pages available in the subset B of the ClueWeb09 collection. Our preliminary experiments on the TREC 2012 testing queries showed that our indexing scheme is robust and efficient for both indexing and retrieving relevant web pages, for both the ad-hoc and diversity task.

Indexing is a crucial technique for dealing with the massive amount of data present on the web. Indexing can be performed based on words or on phrases. Our approach aims to efficiently index web documents by employing a hybrid technique in which web documents are indexed in such a way that knowledge available in the Wikipedia and in meta-content is efficiently used. Our preliminary experiments on the TREC dataset have shown that our indexing scheme is a robust and efficient method for both indexing and for retrieving relevant web pages. We ranked term queries in different ways, depending if they were found in Wikipedia pages or not. This paper presents our preliminary algorithm and experiments for the ad-hoc and diversity tasks of the TREC 2011 Web track. We ran our system on the subset B (50 million web documents) from the ClueWeb09 dataset

This paper describes the details of our participation in the Web track of the TREC 2010. Our approach collects information from the meta data and from some html tags of the web pages. Meta data is often used by the authors to describe the main topic or purpose of the webpage. Usually, the index words are collected from the visible data, but our method is preferable, when the webpage contains only multimedia data, such as images or animations; when the webpage contains only URL links or very little text data (e.g., home pages); when the webpage contains several different topics; when documents are repetitive; when we want to reduce the size of the inverted index; and when the weighting of a webpage depends on specific topics, not on word distribution. We indexed only selected sections of each webpage: "URL", "Title", "Meta", "Header", and "Alt" fields. The "Header" field is the only visible text field that we used

This paper describes the details of our participation in the Web track of the TREC 2010. Our approach collects information from the meta data and from some html tags of the web pages. Meta data is often used by the authors to describe the main topic or purpose of the webpage. Usually, the index words are collected from the visible data, but our method is preferable, when the webpage contains only multimedia data, such as images or animations; when the webpage contains only URL links or very little text data (e.g., home pages); when the webpage contains several different topics; when documents are repetitive; when we want to reduce the size of the inverted index; and when the weighting of a webpage depends on specific topics, not on word distribution. We indexed only selected sections of each webpage: “URL”, “Title”, “Meta”, “Header”, and “Alt” fields. The “Header” field is the only visible text field that we used.