This paper presents an approach for extending the vector space model (VSM) to perform XML retrieval. The model is extended to support important aspects of XML structural and semantic information such as element nesting level, matching tag names in the query and the collection and the relation between tag names and content of an element. Potential use of the model for heterogeneous as well as fo...

The vector space model is one of the classical and widely applied information retrieval models to rank the web page based on similarity values. The retrieval operations consist of cosine similarity function to compute the similarity values between a given query and the set of documents retrieved and then rank the documents according to the relevance. In this paper, we are presenting different a...

In this contribution, we propose a method that uses a multilingual framework to validate the relevance of the notion of vector based semantic similarity between texts. The goal is to verify that vector based semantic similarities can be reliably transfered from one language to another. More precisely, the idea is to test whether the relative positions of documents in a vector space associated w...

In conventional vector space model for information retrieval, query vector generation is imperfect for retrieval of precise documents which are desired by user. In this paper, we present a stochastic based approach for optimizing query vector without user involvement. We explore the document search space using particle swarm optimization and exploit the search space of possible relevant and non...

we show that the character space of the vector-valued lipschitz algebra $lip^{alpha}(x, e)$ of order $alpha$ is homeomorphic to the cartesian product $xtimes m_e$ in the product topology, where $x$ is a compact metric space and $e$ is a unital commutative banach algebra. we also characterize the form of each character on $lip^{alpha}(x, e)$. by appealing to the injective tensor product, we then...