the main challenge of a search engine is ranking web documents to provide the best response to a user`s query. despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. in this paper, a ranking algorithm based on the reinforcement le...

A central issue for making the content of a scientific document quickly accessible to a potential reader is the extraction of keyphrases, which capture the main topic of the document. Keyphrases can be extracted automatically by generating a list of keyphrase candidates, ranking these candidates, and selecting the top-ranked candidates as keyphrases. We present the KeyWE system, which uses an a...

Many information retrieval(IR) systems retrieve relevant documents based on exact matching of keywords between a query and documents. This method degrades precision rate. In order to solve the problem, we collected semantically related words and assigned semantic relationships used in general thesaurus and a special relationship called keyfact term(FT) manually. In addition to the semantic know...

Using several simplifications of the vector-space model for text retrieval queries, the authors seek the optimal balance between processing efficiency and retrieval effectiveness as expressed in relevant document rankings. fficient and effective text retrieval techniques are critical in managing the increasing amount of textual information available in electronic form. Yet text retrieval is a d...

This paper presents a methodology for the ontology based semantic annotation of web pages with annotation weighting scheme that takes advantage of the different relevance of structured document fields. The retrieval model is based on the importance factors of the structural elements, which are used to re-rank the documents retrieval by the ontology based distance measure. The relevance concept ...

Method: Building a document ranking system involves two key decisions: choosing a retrieval model, and choosing a suitable index representation. The former determines the effectiveness of the system, the latter the efficiency; and each of them affects the other. The impact-based document ranking mechanism described by Anh and Moffat [2] was chosen for our system because of its balance between e...

The increased variety of information makes it critical to retrieve documents which are not only relevant but also broad enough to cover as many different aspects of a certain topic as possible. The increased variety of users also makes it critical to retrieve documents that are jargon free and easy-to-understand rather than the specific technical materials. In this paper, we propose a new conce...

This work deals with the implementation of a logical model of Information Retrieval. Specifically, we present algorithms for document ranking within the Belief Revision framework. Therefore, the logical model that stands on the basis of our proposal can be efficiently implemented within realistic systems. Besides the inherent advantages introduced by logic, the expressiveness is extended with r...

In this work, we propose a method for document re-ranking, which exploits negative feedback represented by non-relevant documents. The concept of non-relevance is modelled through the quantum negation operator. The evaluation carried out on a standard collection shows the effectiveness of the proposed method in both the classical Vector Space Model and a Semantic Document Space.

Clustering web search engine results for ambiguous keyword searches poses unique challenges. First, we show that one cannot readily import the frequency based feature ranking to cluster the web search results as in the text document clustering. Next, we present TermRank, a variation of the PageRank algorithm based on a relational graph representation of the content of web document collections. ...