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Abirami, A. M.
- An Enhanced Method for Efficient Information Retrieval from Resume Documents Using SPARQL
Abstract Views :205 |
PDF Views:2
Authors
Affiliations
1 Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IN
1 Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 1 (2012), Pagination: 5-10Abstract
It is more important to retrieve information from various types of documents like DOC, HTML, etc that contain vital information to be preserved and used in future. Information retrieval from these documents is mostly the manual effort. Though search algorithms do this retrieval, they may not be accurate as expected by the user. Also, some documents like candidates' resumes cannot be stored into the relational database as such because the number of fields is more. Much of manual efforts are put in use to analyze the various resumes to select the candidates who satisfy the specific criteria. To minimize the manual efforts and to get the results faster, this paper proposes the use of Semantic Web Technology like OWL, RDF and SPARQL to retrieve the information from the documents efficiently. This paper proposes to create the Ontology for the required domain as a first step. Based on the fields or tags in the owl file, the user is given a form to provide his personal and academic details. These data is converted into RDF/XML document. RDF files are retrieved and grouped based on some category. Query text is entered and the relevant records are retrieved from RDF documents using SPARQL. SPARQL is an RDF query language that enhances fast and efficient search of data when compared to other XML query languages like XPATH and XQUERY. Comparison between SPARQL and XPATH in terms of time taken to retrieve records is also analyzed in this paper.Keywords
RDF, OWL, SPARQL, Document Filter, Information Retrieval.
- Context-Based Feature Extraction Technique – LSI vs LDA
Abstract Views :247 |
PDF Views:1
Authors
A. M. Abirami
1,
A. Askarunisa
2,
T. S. B. Akshara
1,
G. Prasannashree
1,
K. Priyanga
1,
K. Sarika
1
Affiliations
1 Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IN
2 Department of Computer Science and Engineering, KLN Information Technology, Madurai, Tamil Nadu, IN
1 Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IN
2 Department of Computer Science and Engineering, KLN Information Technology, Madurai, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 5 (2018), Pagination: 85-92Abstract
Internet has enormous amount of documents and they need to be annotated for further processing. Customer reviews or feedback on product is mostly done by using text mining or text analytics techniques. Feature extraction plays the vital role in text analytics methodology by which the most relevant features are extracted and used for text processing. This research article focuses on the use of Latent Dirichlet Allocation (LDA) as the feature extraction technique and it is compared with the prominent technique Latent Semantic Indexing (LSI).
Keywords
Text Analytics, Feature Extraction, Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), Document Categorization.References
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