Semantic similarity has become an important tool and widely been used to solve traditional Information Retrieval problems. This study adopts ontology of computer science and proposes an ontology indexing weight based on Wu and Palmer’s edge counting measure and uses the N-grams method for computing a family of word similarity. The study also compares the subsumption weight between Hliaoutakis a...

Most Information Retrieval models compute the relevance score of a document for a given query by summing term weights specific to a document or a query. Heuristic approaches, like TF-IDF, or probabilistic models, like BM25, are used to specify how a term weight is computed. In this paper, we propose to leverage learning-to-rank principles to learn how to compute a term weight for a given docume...

Retrieval accuracy can be improved by considering which document type should be filtered out and which should be ranked higher in the result list. Hence, document type can be used as a key factor for building a re-ranking retrieval model. We take a simple approach for considering document type in the retrieval process. We adapt the BM25 scoring function to weight term frequency based on the doc...

While price and data quality should define the major tradeoff for consumers in data markets, prices are usually prescribed by vendors and data quality is not negotiable. In this paper we study a model where data quality can be traded for a discount. We focus on the case of XML documents and consider completeness as the quality dimension. In our setting, the data provider offers an XML document,...

Let \begin{document}$ \mathcal C $\end{document} be a maximum distance separable (MDS) linear code over finite field id="M2">\begin{document}$ \Bbb F_q $\end{document}. In this paper, we present new formula of its weight distribution, which can seen as another expression the Ma...

Variation in performances of an Information Retrieval system, which merges results from a number of retrieval schemes possessing equal and unequal weights, is studied in this paper. Weight of the retrieval schemes for a particular document is derived from the relevance scores of that corresponding document. Since, the relevance scores are varying from document to document and corpus to corpus, ...

Inverse document frequency (IDF) is one of the most useful and widely used concepts in information retrieval. When it is used in combination with the term frequency (TF), the result is a very effective term weighting scheme (TF-IDF) that has been applied in information retrieval to determine the weight of the terms. Terms with high TF-IDF values imply a strong relationship with the document the...

One of the fundamental problems in coding theory is to find \begin{document}$ n_q(k,d) $\end{document}, minimum length id="M4">\begin{document}$ n $\end{document} for which a linear code id="M5">\begin{document}$ dimension id="M6">\begin{document}$ k and weight id="M7">\begin{d...

Searching information from the Internet via available search engines is often overwhelmed by myriad of resulting documents that are mostly irrelevant. The problem lies in the use of proper keywords arranged in the right order. This paper proposes an effective filtering approach that exploits various existing techniques through a sequence of transformations. The proposed approach employs ontolog...

In recent years we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intra-nets. Automatic text categorization, which is the task of assigning text documents to pre-speci ed classes (topics or themes) of documents, is an important task that can help both in organizing as well as in nding information on thes...