Ontology-based Evaluation Research Articles

An effective ontology-based query response model for risk assessment in urban flood disaster management

The drastic advancements in the field of Information Technology make it possible to analyze, manage and handle large-scale environment data and spatial information acquired from diverse sources. Nevertheless, this process is a more challenging task where the data accessibility has been performed in an unstructured, varied, and incomplete manner. The appropriate extraction of information from diverse data sources is crucial for evaluating natural disaster management. Therefore, an effective framework is required to acquire essential information in a structured and accessible manner. This research concentrates on modeling an efficient ontology-based evaluation framework to facilitate the queries based on the flood disaster location. It offers a reasoning framework with spatial and feature patterns to respond to the generated query. To be specific, the data is acquired from the urban flood disaster environmental condition to perform data analysis hierarchically and semantically. Finally, data evaluation can be accomplished by data visualization and correlation patterns to respond to higher-level queries. The proposed ontology-based evaluation framework has been simulated using the MATLAB environment. The result exposes that the proposed framework obtains superior significance over the existing frameworks with a lesser average query response time of 7 seconds.

An Ontology Framework for Pile Integrity Evaluation Based on Analytical Methodology

Traditional methodology for pile integrity evaluation usually adopts fuzzy qualitative indicators and the engineering experience of technicians to roughly estimate the integrity category of a pile, which includes many uncertainties and heavily subjective factors. Therefore, based on an analytical model for the vibration of pile and an ontology-based approach, this paper describes the development of an integrated evaluation system that can make reasonable evaluations of pile integrity where specific measured reflective wave curves are provided. First, a semi-analytical solution for the velocity response of pile with defects at the pile head was derived by analytical methodology, and then the intrinsic relationships between the quantitative indicators of pile defects and the characteristic parameters of velocity response curves were obtained according to the propagation law of elastic wave and the numerical fitting method. On this basis, a prototypical ontology-based evaluation system, ontology of pile integrity evaluation (OntoPIE), with a new ontology framework of leverage knowledge modelling was developed to create an easy-to-use tool for quantitative identification of pile defects and qualitative evaluation of pile integrity by combining ontology and semantic web rule language (SWRL) rules. A case study was also conducted to show how the developed framework can be used to demonstrate its practicability and scientific feasibility. The accuracy of the framework will be verified by comparing the quantitative indicators of pile defects inferred by OntoPIE with the preset defect indicators through designed examples.

An Ontology-Based Predictive Maintenance Tool for Power Substation Faults in Distribution Grid

Recent advances in Power Grid (PG) technology pose an important problem of measuring the effectiveness of power grid configurations. Current assessment models are not adequate to mitigate the setup issues due to the absence of a high-fidelity evaluation framework that can consider diverse scenarios based on the market interest. Consequently, we develop a highly flexible Ontology-based Evaluation System that can accommodate and assess different scenarios. The use of ontology as middleware is the best approach to produce an efficient, semantically aware, and operationally accurate system environment for managing flexibility in evaluation. The evaluation is made by predicting the failure intensity and subsequently generate a maintenance report of a particular configuration. The selection of the best configuration is made by comparing the maintenance report of different configurations. The developed evaluation system consists of three main components which are Configuration Generator Tool (GCT), Failure Prediction Model (FDM), and Hybrid Simulation Platform (HSP). The GCT is a knowledge-based system that provides a powerful tool for engineers to generate alternative configurations. The GCT data were collected from literature, validated by experts, and modeled using Web Ontology Language (OWL). While the HSP was developed using several modelings and ontology-based tools such as blender 3D modeling, unity 3d, asp.net, my sql, and apache Jena fuseki. Finally, the FDM was developed based on the impact and relationship of odd events to power grid components and the impact of a failed component to other components, the prediction is modeled using two methods Poisson Model and Likelihood Estimation Method.

An Approach for Ontology-Based Information Extraction System Selection and Evaluation

An Approach for Ontology-Based Information Extraction System Selection and Evaluation

Context-Sensitive Spelling Correction of Consumer-Generated Content on Health Care.

BackgroundConsumer-generated content, such as postings on social media websites, can serve as an ideal source of information for studying health care from a consumer’s perspective. However, consumer-generated content on health care topics often contains spelling errors, which, if not corrected, will be obstacles for downstream computer-based text analysis.ObjectiveIn this study, we proposed a framework with a spelling correction system designed for consumer-generated content and a novel ontology-based evaluation system which was used to efficiently assess the correction quality. Additionally, we emphasized the importance of context sensitivity in the correction process, and demonstrated why correction methods designed for electronic medical records (EMRs) failed to perform well with consumer-generated content.MethodsFirst, we developed our spelling correction system based on Google Spell Checker. The system processed postings acquired from MedHelp, a biomedical bulletin board system (BBS), and saved misspelled words (eg, sertaline) and corresponding corrected words (eg, sertraline) into two separate sets. Second, to reduce the number of words needing manual examination in the evaluation process, we respectively matched the words in the two sets with terms in two biomedical ontologies: RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT). The ratio of words which could be matched and appropriately corrected was used to evaluate the correction system’s overall performance. Third, we categorized the misspelled words according to the types of spelling errors. Finally, we calculated the ratio of abbreviations in the postings, which remarkably differed between EMRs and consumer-generated content and could largely influence the overall performance of spelling checkers.ResultsAn uncorrected word and the corresponding corrected word was called a spelling pair, and the two words in the spelling pair were its members. In our study, there were 271 spelling pairs detected, among which 58 (21.4%) pairs had one or two members matched in the selected ontologies. The ratio of appropriate correction in the 271 overall spelling errors was 85.2% (231/271). The ratio of that in the 58 spelling pairs was 86% (50/58), close to the overall ratio. We also found that linguistic errors took up 31.4% (85/271) of all errors detected, and only 0.98% (210/21,358) of words in the postings were abbreviations, which was much lower than the ratio in the EMRs (33.6%).ConclusionsWe conclude that our system can accurately correct spelling errors in consumer-generated content. Context sensitivity is indispensable in the correction process. Additionally, it can be confirmed that consumer-generated content differs from EMRs in that consumers seldom use abbreviations. Also, the evaluation method, taking advantage of biomedical ontology, can effectively estimate the accuracy of the correction system and reduce manual examination time.

Ontology-based evaluation of natural disaster management websites

In recent years, the world has witnessed a number of severe natural disasters, causing heavy losses to families, communities, and even nations. Natural disaster management (NDM) websites play an im...

Ontology-Based Evaluation of Natural Disaster Management Websites: A Multistakeholder Perspective

In recent years, the world has witnessed a number of severe natural disasters, causing heavy losses to families, communities, and even nations. Natural disaster management (NDM) websites play an important role in assisting people through various disaster stages. However, such websites are complex and there is little research on standards and guidelines for their development and evaluation. In this paper, we develop an ontology-based evaluation tool to assess the utility of NDM websites. Two main groups of stakeholders-- experts who are in charge of NDM websites and potential users of such websites--contributed to the process. A total of 73 experts validated the ontology developed for NDM web elements through a Delphi study. These experts also provided importance ratings for web elements in the ontology. In a survey of the second major group of stakeholders--potential users--818 participants provided another set of importance ratings for web elements in the ontology. The design theory in this work is based on utility theory. The metrics for the evaluation of websites are relative utility and absolute utility. Using the evaluation tool, we evaluated the NDM websites of the 50 U.S. states from the perspectives of the two groups of stakeholders. The results indicate a lack of readiness in most of these websites.

Multi-document summarization based on the Yago ontology

Sentence-based multi-document summarization is the task of generating a succinct summary of a document collection, which consists of the most salient document sentences. In recent years, the increasing availability of semantics-based models (e.g., ontologies and taxonomies) has prompted researchers to investigate their usefulness for improving summarizer performance. However, semantics-based document analysis is often applied as a preprocessing step, rather than integrating the discovered knowledge into the summarization process.This paper proposes a novel summarizer, namely Yago-based Summarizer, that relies on an ontology-based evaluation and selection of the document sentences. To capture the actual meaning and context of the document sentences and generate sound document summaries, an established entity recognition and disambiguation step based on the Yago ontology is integrated into the summarization process.The experimental results, which were achieved on the DUC’04 benchmark collections, demonstrate the effectiveness of the proposed approach compared to a large number of competitors as well as the qualitative soundness of the generated summaries.

Current research in conceptual modelling of agent mobility: an ontology-based evaluation

There is increasing recognition of the potential benefits of mobile agent technology for distributed and mobile applications. However, there has been only limited consideration of how to model agent mobility at the conceptual level to support application analysis and design. Furthermore, the fragmented nature and inconsistent terminology characterising such research to date complicates the effort to understand and compare the work. This paper presents an ontology intended to serve as a necessary first step to providing effective conceptual level support for mobile agent applications and then uses the ontology to evaluate the conceptual models of current mobile agent methodologies.