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With the continued proliferation of location-based services, a growing number of web-accessible data objects are geotagged and have text descriptions. An important query over such web objects is the direction-aware spatial keyword query that aims to retrieve the top-k objects that best match query parameters in terms
Spatial keyword querying has attracted considerable research efforts in the past few years. A prototypical query takes a location and keywords as arguments and returns the k objects that score the highest according to a ranking function. While different scoring functions have been used, how to compare different
This paper proposes a Research paper Similarity system that measures the similarity of an input paper with other papers based on the summarized version of each paper. Currently, This system will take into account 2 different types of summarization for papers based on the different types of keywords,i.e, Normal
analysis of paper abstracts by using Natural Language Processing (NLP) techniques such as Latent Semantic Analysis, Latent Dirichlet Allocation or specific ontology distances, i.e., Word Net. Moreover, the defined mechanisms are enforced on two different sub domains from the corpora generated around the keywords "e-learning
Traditional information retrieval (IR) systems evaluate user queries and retrieve/rank documents based on matching keywords in user queries with words in documents.These exact word-matching and ranking approaches ignore too many relevant documents that do not contain the exact keywords as specified in a user query
Expert finding would be useful for a recommendation system which people want to find experts in a specific area. This paper proposes a model to identify focus research areas of computer science researchers thru their publications. We collect title, abstract and keywords of academic papers published in IEEE Xplore
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