Turning Text into Research Networks: Information Retrieval and Computational Ontologies in the Creation of Scientific Databases

Abstract

BackgroundWeb-based, free-text documents on science and technology have been increasing growing on the web. However, most of these documents are not immediately processable by computers slowing down the acquisition of useful information. Computational ontologies might represent a possible solution by enabling semantically machine readable data sets. But, the process of ontology creation, instantiation and maintenance is still based on manual methodologies and thus time and cost intensive.MethodWe focused on a large corpus containing information on researchers, research fields, and institutions. We based our strategy on traditional entity recognition, social computing and correlation. We devised a semi automatic approach for the recognition, correlation and extraction of named entities and relations from textual documents which are then used to create, instantiate, and maintain an ontology.ResultsWe present a prototype demonstrating the applicability of the proposed strategy, along with a case study describing how direct and indirect relations can be extracted from academic and professional activities registered in a database of curriculum vitae in free-text format. We present evidence that this system can identify entities to assist in the process of knowledge extraction and representation to support ontology maintenance. We also demonstrate the extraction of relationships among ontology classes and their instances.ConclusionWe have demonstrated that our system can be used for the conversion of research information in free text format into database with a semantic structure. Future studies should test this system using the growing number of free-text information available at the institutional and national levels.

Outcomes research resources in India: current status, need and way forward

“…We intend to pursue this in a subsequent study. Finally, although semi automated methods like natural language processing and computational ontologies could have been utilized to carry out data extraction and reasoning of data extracted from published articles (Lin et al, 2010) (Ceci et al, 2012), we preferred the manual method as 1. The number of relevant articles identified through an initial review was low and 2.…”

The Impacts of the Fourth Industrial Revolution on Smart and Sustainable Cities

“…After selecting articles as shown in Table 1, data mining was conducted for the articles that presented the main concept of smart cities and sustainable cities, corresponding to the second phase of the research. Those articles were organized and submitted to the collocations extraction process [94] in order to identify the terms that presented the highest frequency and their relations in order to show how international scientists have addressed issues about sustainable cities and smart cities.…”

“…As Figure 2 shows, the procedure of entity named recognition (ENR) was applied using the ISNER ®® software on the selected 27 articles. Ceci et al (2012) [94] explained that the ENR procedure has as its main goal the identification and categorization of entities (e.g., words, organizations, or places), expressions of time (e.g., times and dates), and some types of numerical expressions (e.g., percentages and values in money) that can be found in the text. The task of identifying entities entails the establishment of limits (boundaries) considering where they begin and end, a process that is especially important in entities composed of more than one word.…”

“…The task of identifying entities entails the establishment of limits (boundaries) considering where they begin and end, a process that is especially important in entities composed of more than one word. The following step was to evaluate and interpret the values, a process which the authors undertook manually since automatic approaches are subject to failures [94].…”

Systematic reviews: Work that needs to be done and not to be done

“…Although text mining techniques have advanced it remains problematic, currently, for machines to automatically extract data , although it has been tested and found feasible for some types of information . However, the detailed reading of a trial report is still necessary.…”