Abstract
Information Retrieval (IR) is a profound technique to find information that addresses the need of query. Processing of normal text is easier and information can be retrieved efficiently. There are plenty of algorithms in hand to carry out the normal text retrieval. Whereas retrieving geospatial information is very complex and requires additional operations to be performed. Since geospatial data contain complex details than general data such as location, direction. To handle geographical queries, we proposed a Density Probabilistic Document Correlation (DPDC) approach. This approach, initially categorize the geographical features from text that satisfies the given queries. Existing text classification techniques are unsuitable for geospatial text classification due to the exclusivity of the geographical features. Depending on the DPDC approach result we predict overlap of the feature set for a document. Based on overlap and document correlation, the documents are ranked. Highly relevant documents are extracted depending on the score obtained through ranking. Documents with high score are considered the most relevant. The experimental results show that our proposed method efficiently retrieves the list of relevant documents.
Highlights
For the past several years, geographical data has been useful for large spatial data sets
Most relevant documents for the user queries are in the top of the list, whereas the irrelevant documents are not retrieved, which are eliminated by the Density Probabilistic Document Correlation (DPDC) approach
Due to the complex nature of spatial data type and the correlation relationship exists among the spatial data; information retrieval of spatial data becomes laborious
Summary
Spatial data is the progression of discovering interesting patterns, which were formerly unknown, but potentially. The correlation and relationship exist among spatial data are frequently handled by the algorithms of data mining. Based on the features chosen the relevant documents are retrieved User expresses their interest in the form of queries to a component, which performs the search operation. Stop words have the impact on the retrieval process since they have high frequency of appearing in document with less meaning and affect the weighting process, which is carried out in our Density Probabilistic Document Correlation (DPDC) approach. The preprocessed documents or data sets and the user query are given to the DPDC approach component. In order to retrieve relevant document, we use the ranking algorithm, which determines the occurrence trained features in a document. Most relevant documents for the user queries are in the top of the list, whereas the irrelevant documents are not retrieved, which are eliminated by the DPDC approach
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