An Ontology-Based Search Tool in the Semantic Web

2013 ◽  
pp. 221-249 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Adina Lipai ◽  
Maria-Iuliana Dascalu
Keyword(s):  
Semantic Web ◽  
Lexical Database ◽  
Search Results ◽  
Concept Space ◽  
Ontological Approach ◽  
Search Tool ◽  
Clustering Search ◽  
Meta Search ◽  
Specific Interests ◽  
Initial List

The chapter presents a meta-search tool developed in order to deliver search results structured according to the specific interests of users. Meta-search means that for a specific query, several search mechanisms could be simultaneously applied. Using the clustering process, thematically homogenous groups are built up from the initial list provided by the standard search mechanisms. The results are more user-oriented, thanks to the ontological approach of the clustering process. After the initial search made on multiple search engines, the results are pre-processed and transformed into vectors of words. These vectors are mapped into vectors of concepts, by calling an educational ontology and using the WordNet lexical database. The vectors of concepts are refined through concept space graphs and projection mechanisms, before applying the clustering procedure. The chapter describes the proposed solution in the framework of other existent clustering search solutions. Implementation details and early experimentation results are also provided.

Ontology-Based Clustering of the Web Meta-Search Results

2012 ◽  
pp. 332-356
Author(s):  
Constanta-Nicoleta Bodea ◽  
Adina Lipai ◽  
Maria-Iuliana Dascalu
Keyword(s):  
Search Engines ◽  
Lexical Database ◽  
Search Results ◽  
Concept Space ◽  
Ontological Approach ◽  
Search Tool ◽  
Meta Search ◽  
Specific Interests ◽  
The Web ◽  
Initial List

The chapter presents a meta-search tool developed in order to deliver search results structured according to the specific interests of users. Meta-search means that for a specific query, several search mechanisms could be simultaneously applied. Using the clustering process, thematically homogenous groups are built up from the initial list provided by the standard search mechanisms. The results are more user oriented, as a result of the ontological approach of the clustering process. After the initial search made on multiple search engines, the results are pre-processed and transformed into vectors of words. These vectors are mapped into vectors of concepts, by calling an educational ontology and using the WordNet lexical database. The vectors of concepts are refined through concept space graphs and projection mechanisms, before applying the clustering procedure. Implementation details and early experimentation results are also provided.

Clustering of the Web Search Results in Educational Recommender Systems

2012 ◽  
pp. 154-181 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Adina Lipai
Keyword(s):  
Recommender Systems ◽  
Clustering Algorithm ◽  
Web Search ◽  
Web Pages ◽  
Lexical Database ◽  
Assessment Task ◽  
Search Results ◽  
Meta Search ◽  
Search Approach ◽  
The Web

This chapter presents a meta-search approach, meant to deliver bibliography from the internet, according to trainees’ results obtained at an e-assessment task. The bibliography consists of web pages related to the knowledge gaps of the trainees. The meta-search engine is part of an education recommender system, attached to an e-assessment application for project management knowledge. Meta-search means that, for a specific query (or mistake made by the trainee), several search mechanisms for suitable bibliography (further reading) could be applied. The lists of results delivered by the standard search mechanisms are used to build thematically homogenous groups using an ontology-based clustering algorithm. The clustering process uses an educational ontology and WordNet lexical database to create its categories. The research is presented in the context of recommender systems and their various applications to the education domain.

scholarly journals Electronic records management – a state of the art review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Babatunde Oladejo ◽  
Sunčica Hadžidedić
Keyword(s):  
State Of The Art ◽  
Academic Research ◽  
Research Area ◽  
Electronic Records ◽  
Records Management ◽  
Content Type ◽  
Search Results ◽  
Search Tool ◽  
Research And Education ◽  
Electronic Records Management

Purpose This paper aims to examine the state of the art in electronic records management (ERM) with the goal of identifying the prevailing research topics, gaps and issues in the field. Design/methodology/approach First, a wide search was performed on academic research databases, limited to the period between 2008–2018. Second, the search results were reviewed for relevance and duplicates. Finally, the study sources were checked against the list of journals and conferences ranked by computing research and education and JourQual. The final sample of 55 selected studies was analyzed in depth. Findings ERM has lost some research momentum due to being deeply embedded in affiliate information systems areas and the changing records management landscape. Additionally, the requirement models specified by Governmental/National Archives might have constrained technology innovation in ERM. A lack of application was identified for the social media research area. Research limitations/implications Limitations were encountered in available search tool functionality and keyword confusion leading to inflated search results. While effort has been made to obtain optimal search results, some relevant articles may have been omitted. Originality/value The last ERM state-of-the-art review was in 1997. A lot has changed since then. This paper will help researchers understand the current state of ERM research, its understudied areas and identify gaps for future studies.

Search Engine

2011 ◽  
pp. 74-100
Author(s):  
Eliana Campi ◽  
Gianluca Lorenzo
Keyword(s):  
Knowledge Management ◽  
Information Retrieval ◽  
Semantic Web ◽  
Knowledge Base ◽  
Search Engine ◽  
Automatic Classification ◽  
Efficient Approach ◽  
Search Results ◽  
The Creation ◽  
F Measure

This chapter presents technologies and approaches for information retrieval in a knowledge base. We intend to show that the use of ontology for domain representation and knowledge search offers a more efficient approach for knowledge management. This approach focuses on the meaning of the word, thus becoming an important element in the building of the Semantic Web. The search based on both keywords and ontology allows more effective information retrieval exploiting the Semantic of the information in a variety of data. We present a method for taxonomy building, annotating, and searching documents with taxonomy concepts. We also describe our experience in the creation of an informal taxonomy, the automatic classification, and the validation of search results with traditional measures, such as precision, recall and f-measure.

scholarly journals Tool for filtering PubMed search results by sample size

2018 ◽  
Vol 25 (7) ◽  
pp. 774-779
Author(s):  
Carlos Baladrón ◽  
Alejandro Santos-Lozano ◽  
Javier M Aguiar ◽  
Alejandro Lucia ◽  
Juan Martín-Hernández
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Search Engine ◽  
Sample Sizes ◽  
Search Results ◽  
Estimate Sample Size ◽  
Search Tool ◽  
Pubmed Search ◽  
Meta Analyses ◽  
Validation Tests

Abstract Objective The most used search engine for scientific literature, PubMed, provides tools to filter results by several fields. When searching for reports on clinical trials, sample size can be among the most important factors to consider. However, PubMed does not currently provide any means of filtering search results by sample size. Such a filtering tool would be useful in a variety of situations, including meta-analyses or state-of-the-art analyses to support experimental therapies. In this work, a tool was developed to filter articles identified by PubMed based on their reported sample sizes. Materials and Methods A search engine was designed to send queries to PubMed, retrieve results, and compute estimates of reported sample sizes using a combination of syntactical and machine learning methods. The sample size search tool is publicly available for download at http://ihealth.uemc.es. Its accuracy was assessed against a manually annotated database of 750 random clinical trials returned by PubMed. Results Validation tests show that the sample size search tool is able to accurately (1) estimate sample size for 70% of abstracts and (2) classify 85% of abstracts into sample size quartiles. Conclusions The proposed tool was validated as useful for advanced PubMed searches of clinical trials when the user is interested in identifying trials of a given sample size.

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