Md Zia Ullah

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Users express their information needs in terms of queries in search engines to find some relevant documents on the Internet. However, search queries are usually short, ambiguous and/or underspecified. To understand user’s search intent, subtopic mining plays an important role and has attracted attention in the recent years. In this paper, we describe our(More)
In this paper, we describe our participation in the ImageCLEF 2014 Scalable Concept Image Annotation task. In this participation, we propose a novel approach of automatic image annotation by using ontology at several steps of supervised learning. In this regard, we construct tree-like ontology for each annotating concept of images using WordNet and(More)
Web search queries are usually short, ambiguous, and contain multiple aspects or subtopics. Different users may have different search intents (or information needs) when submitting the same query. The task of identifying the subtopics underlying a query has received much attention in recent years. In this paper, we propose a method that exploits query(More)
With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies (<i>GWAS</i>) have proved useful as a method for exploring phenotypic associations(More)
With the availability of the huge medical knowledge data on the Internet such as the human disease network, protein-protein interaction (PPI) network, and phenotypegene, gene-disease bipartite networks, it becomes practical to help doctors by suggesting plausible hereditary diseases for a set of clinical phenotypes. However, identifying candidate diseases(More)
In this paper, we describe our participation in the ImageCLEF 2015 Scalable Concept Image Annotation task. In this participation, we propose an approach of image annotation by using ontology at several steps of supervised learning with noisy unlabeled data. In this regard, we construct tree-like ontology for each annotating concept of images using WordNet(More)
Web is gigantic and being constantly update. Everyday lots of users turn into websites for their information needs. As search queries are dynamic in nature, recent research shows that considering temporal aspects underlying a query can improve the retrieval performance significantly. In this paper, we present our approach to address the Temporal Intent(More)
Image annotation has been an important task for visual information retrieval. It usually involves a multi-class multi-label classification problem. To solve this problem, many researches have been conducted during last two decades, although most of the proposed methods rely on the training data with the ground truth. To prepare such a ground truth is an(More)