An Agent-based Meta-Search Engine Architecture for Open Government Datasets Search

Abstract

Recently, Most of the countries are publishing their government data in the Web as datasets. A large number of datasets (HTML, CSV, RDF, XML, JSON, Excel, and PDF etc), catalogs and portals (data.gov, data.gov.uk, data.gov.au etc) are emerging in the Science and Government sector. Open Government Data drives in the US, UK and elsewhere have created hung amounts of government data available to the public on the web. A large number of datasets are published on government data portals, the question arises how to get datasets satisfying from government data portals with the least effort. However in many cases, a single search result is not sufficient to meet the user's query and it is necessary to create a new service platform by combining existing government catalogs search. In this paper we proposed a meta-search architecture that will integrate different dataset catalogs search interface and collected datasets from different open government data catalogs and display higher results. In this proposed architecture have search interface provides a scalable and recognizable solution for finding Open Government datasets from open government data catalogs. Thus, the propose meta-search architecture improves the usability and effectively of open government datasets search.

Agent-Based Document Expansion for Information Retrieval Based on Topic Modeling of Local Information

“…For example, [14] use nine modules (agents) for a system using intelligent evolution based on user queries to improve accuracy of the results. [15] survey various approaches that use cognitive agents to support information retrieval in the form of meta-search [16], semantic search or by taking users emotions into account [17]. In the context of this paper, we take the former view and understand agents as actors that operate on local information.…”