Information retrieval with semantic annotation

EasyChair Preprint 1123

Abstract

The processing of information with semantic annotation allows to identify the intention of search of the user and to adjust the result according of the information context . The present research proposes a model for the retrieval of information with semantic annotation that allows to help the user to retrieve the most relevant information among all the information available on the web. In the model, three components (Crawler-Indexing, Processing and Presentation) are developed that allow identifying the need for user information through the processing, selection and subsequent publication of the retrieved information. The crawling and indexing component allows the identification of available websites to extract information and perform semantic annotation by applying different information processing techniques. The processing component analyzes the user's preferences and processes the query performed to calculate the similarity of the indexed information. Subsequently the results are sorted according to the relevance to show in the Presentation component a quantity of information that can be assimilated by the users. For the validation of the proposal we used the metrics of precision and exhaustiveness that allowed to demonstrate the quality, relevance and relevance of the information retrieval with semantic annotation.

Keyphrases: Information Retrieval, Semantic Web, relevance, semantic annotation, similarity

@booklet{EasyChair:1123,
  author    = {Hubert Viltres Sala and Paúl Rodríguez Leyva and Juan Pedro Febles Rodriguez and Vivian Estrada Sentí},
  title     = {Information retrieval with semantic annotation},
  howpublished = {EasyChair Preprint 1123},
  year      = {EasyChair, 2019}}