Learn More
In this paper, we describe our participation in the Image-CLEF 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)
The explosive growth of image data on the web leads to the research and development of visual information retrieval systems. However , these visual contents do not allow user to query images using semantic meanings. To resolve this problem, automatically annotating images with a list of semantic concepts is an essential and beneficial task. In this paper,(More)
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 Image-CLEF 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)
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)
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)
In this paper, we describe our participation in the CLEF eHealth 2016 task 3: Patient-Centred Information Retrieval focusing on the clinical web documents based on user queries in the health forum. In our participation, we submitted three runs in ad-hoc search and two runs in query variation search subtasks. In ad-hoc search, the main challenge is to(More)
In this paper, we describe our participation in the Query Understanding subtask of the NTCIR-12 IMINE Task. We propose a method that extracts subtopics by leveraging the query suggestions from search engines. The importance of the subtopics with the query is estimated by exploiting multiple query-dependent and query-independent features with supervised(More)