Ontology-based Text Research Articles

An ontology-based text mining dataset for extraction of process-structure-property entities

While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-grained annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained language models to showcase the feasibility of training named entity recognition models. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data.

Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents

Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents

Knowledge-Infused Text Classification for the Biomedical Domain

Extracting knowledge from unstructured text and then classifying it is gaining importance after the data explosion on the web. The traditional text classification approaches are becoming ubiquitous, but the hybrid of semantic knowledge representation with statistical techniques can be more promising. The developed method attempts to fabricate neural networks to expedite and improve the simulation of ontology-based classification. This paper weighs upon the accurate results between the ontology-based text classification and traditional classification based on the artificial neural network (ANN) using distinguished parameters such as accuracy, precision, etc. The experimental analysis shows that the proposed findings are substantially better than the conventional text classification, taking the course of action into account. The authors also ran tests to compare the results of the proposed research model with one of the latest researches, resulting in a cut above accuracy and F1 score of the proposed model for various experiments performed at the different number of hidden layers and neurons.

BioCaster in 2021: automatic disease outbreaks detection from global news media.

SummaryBioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. Following a 6-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here, we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports and a new cloud-based visualization dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community.Availability and implementationBioCaster web-portal is freely accessible at http://biocaster.org.

Design of Chinese Corpus Based on Semantic Mining Algorithm

In order to improve the practical effect of the Chinese corpus, this paper combines the semantic mining algorithm to design the Chinese corpus, proposes an ontology adaptive algorithm based on content learning, and conducts in-depth research on the model of the algorithm. Firstly, in view of the heterogeneity of web information structure and the chaotic nature of information organization, this paper proposes a web content extraction method to effectively remove noise information. Moreover, this paper analyzes an ontology-based text content parsing method, which uses the semantic parsing capability of the ontology to improve the semantic parsing capability of the text. Secondly, this paper proposes the BFA algorithm to optimize the BP neural network. The experimental research shows that the Chinese corpus system based on semantic mining proposed in this paper has a good practical effect and meets the actual needs of Chinese teaching and translation.

Towards a framework to design product service system-based mobile phone waste management: A social media data analysis perspective

ABSTRACT Today, extensive universal concern about sustainable development, strict environmental legislation, and extended producer responsibility has become important incentives to consider end-of-life (EOL) products. Thus, the tremendous volume of mobile phone waste worldwide calls for the design of plans for managing products duringconsumption and the EOL phases to achieve sustainable treatment and betterenvironmental performance. Therefore, a product-service system (PSS) as a sustainable solution has received great attention. To achieve this goal, companies have shifted their focus to continuous improvement of PSS that contributes to better environmental performance and minimizing waste generation during the product life cycle. In this regard, this study suggests a new decision support system (DSS) model that supports the PSS design; that the proposed DSS analyzes the Twitter platform using an ontology-based text mining approach and a data mining-based technique based on the self-organizing map (SOM) to collect mobile phone defects from the Twitter database. Finally, a multi-objective optimization model was used to optimize economic and environmental impacts during the consumption and EOL phase based on mobile phone defects. This study shows that the PSS concepts can prevent mobile phone waste from arising in today’s modern worldby using social media data.

Unveiling a novel model for promoting mobile phone waste management with a social media data analytical approach

With the enormous growth of the population, the intense improvement of the electronic industry, and the ever-incrementing use of mobile phones in today's life, the proper disposal of waste mobile phones has been of paramount importance. Consequently, the tremendous volume of end-of-life (EOL) mobile phones worldwide calls for a sustainable management system to decide on waste mobile phone recovery to minimize the cost and environmental impact. This paper presented a decision problem, namely, EOL option determination, to decrease mobile phone waste. Thus, this study suggests a decision support system (DSS) for the iPhone mobile phone (a subset of the electronics industry) in the EOL phase. Customers’ pervading use of social media has led to these platforms being used as a rich data source to extract information. Thus, the proposed DSS analyzes the Twitter databases, collecting mobile phone defect information from the customers. We suggest an ontology-based text mining and a data mining-based technique through the self-organizing map (SOM) for information discovery from the Twitter data. Finally, a multi-objective mathematical model based on sustainability dimensions is developed to correct EOL decisions based on the defective mobile phone components analyzed from Twitter data. The proposed DSS helps manufacturers when a product is returned and decide for proper EOL processes. This study provides a novel insight and can serve as a valuable reference for solving waste management problems using social media data.

A Framework for Ontology Based Semantic Search System in Ayurvedic Medicine

India is known for its traditional medicinal system such as Ayurveda, Yoga, Unani, Siddha and Homeopathy. Ayurveda plays a significant role in curing the diseases without any side effects. Medicinal plants or herbs are considered as a major resource in meeting the need of people health care. Information about this medicinal knowledge must be preserved and digitized. There have been a massive number of publications and large number of articles on ayurvedic research in the form of unstructured textual data. Text mining approach is used to provide the solution to handle such voluminous of unstructured data. With the exponential growth of text based data, navigating the relevant information needed is the challenging task. Semantic understanding of document content forms the vital requirement for ensuring the quality of content retrieval. However, the current approaches are finding variation in textual classification in bringing the classification accuracy which may fail to understand the data during classification. Hence, an efficient model is required to search, classify and retrieve the most relevant data. The main objective of this research is to develop an effective and efficient framework and algorithm to search and retrieve the most relevant facts by including the application of ontology-based text mining approach. The current status of research is analyzed and reviewed in the area of semantic web retrieval, ontology-based approaches and various classification technique for building the framework. Text mining with the special emphasis on understanding the semantic meaning of content is achieved by using domain ontology called medicinal plant ontology construction. The challenges in finding the semantically related content for the given query are achieved through semantic web and ontology which enriched the data on web for structured representation thereby providing the strong semantics in knowledge representation. The methodology of information extraction is implemented by using medicinal plant ontology with semantic knowledge representation, an algorithm called OCEC (Ontology based Concept Extraction and Classification) was developed where each term is described semantically by mapping the terms and its related terms in the medicinal plant ontology. The web language called Web Ontology Language (OWL) is used for knowledge representation and is considered as richer semantic description language for describing unstructured and semi-structured content on the web thereby extracting the exact and relevant data and to offer a strong semantic search. To evaluate the performance of the proposed method, less relevant and most relevant documents were collected from online sources and digital libraries. Comparative study has been performed with various classification techniques. The experimental results show that the proposed method out performed. To further prove the efficiency of the model, experiments were conducted by giving different queries and the results are compared with other existing methods. The results show that the content retrieved by the proposed model improves precision and recall results. Keyword : Traditional Medicine, Ayurveda, Ontology, Semantic Web, Web Ontology Language

Ontology-based Extractive Text Summarization: The Contribution of Instances

In this paper, we present a text summarization approach focusing on multi-document, extractive and query-focused summarization that relies on an ontology-based semantic similarity measure, that specifically explores ontology instances. We employ the DBpedia Ontology and a theoretical definition of similarity to determine query-sentence and sentence-sentence similarity. Furthermore, we define an instance-linking strategy that builds the most accurate sentence representation possible while achieving a better coverage of sentences that can be represented by ontology instances. Using primarily this instances linking strategy, the semantic similarity measure and the Maximal Marginal Relevance Algorithm- MMR - we propose a summarization model that is capable of avoiding redundancy from a more fine-grained representation of sentences, due to the irrepresentation as ontology instances. We demonstrate that our summarizer is capable of achieving compelling results when compared with relevant DUC systems and recently published related studies using ROUGE metrics. Moreover, our experiments lead us to a better understanding of how ontology instances can be used to represent sentences and what is the role of said instances in this process.

Performance measurement of airports using data envelopment analysis: A review of methods and findings

Data Envelopment Analysis (DEA) is the most commonly used tool for performance measurement of airports. We studied 61 articles and notes published during 2009–2017 that assess the performance of airports across the globe using ontology-based text analysis. Each article aims at achieving objectives beyond the mere calculation of efficiency scores of airports. On the basis of these objectives, four themes emerge – explaining the variation of efficiency through exogenous factors; mapping the change in efficiency over time; decomposition of efficiency using network DEA; and comparing DEA with other tools. We find that there is no systematic approach for selection of inputs and outputs, as they are largely dependent on the availability of data. Most studies computing scale efficiency report that airports exhibit returns to scale. Ownership form of airports is the most commonly studied factor for explaining the variation in efficiency; however, there is no consensus in the findings. Nearly one-third articles study the change in efficiency over time reporting similar findings that can be generalized. The research gap emerging from the review is that there is insufficient thrust on the application of efficiency scores obtained from DEA for improving airport performance. The main contribution of the paper is an analysis of the approaches used for performance measurement and the associations made with the efficiency scores.

PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research

Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/, and the data are freely available through a public SPARQL endpoint.

A decision support system based on ontology and data mining to improve design using warranty data

Analysis of warranty based big data has gained considerable attention due to its potential for improving the quality of products whilst minimizing warranty costs. Similarly, customer feedback information and warranty claims, which are commonly stored in warranty databases might be analyzed to improve quality and reliability and reduce costs in areas, including product development processes, advanced product design, and manufacturing. However, three challenges exist, firstly to accurately identify manufacturing faults from these multiple sources of heterogeneous textual data. Secondly, accurately mapping the identified manufacturing faults with the appropriate design information and thirdly, using these mappings to simultaneously optimize costs, design parameters and tolerances. This paper proposes a Decision Support System (DSS) based on novel integrated stepwise methodologies including ontology-based text mining, self-organizing maps, reliability and cost optimization for identifying manufacturing faults, mapping them to design information and finally optimizing design parameters for maximum reliability and minimum cost respectively. The DSS analyses warranty databases which collect the warranty failure information from the customers in a textual format. To extract the hidden knowledge from this, an ontology-based text mining based approach is adopted. A data mining based approach using Self Organizing Maps (SOM) has been proposed to draw information from the warranty database and to relate it to the manufacturing data. The clusters obtained using SOM are analyzed to identify the critical regions, i.e., sections of the map where maximum defects occur. Finally, to facilitate the correct implementation of design parameter changes, the frequency and type of defects analyzed from warranty data are used to identify areas where improvements have resulted in the greatest reliability for the lowest cost.

Use of the Omaha System for ontology-based text mining to discover meaning within CaringBridge social media journals

ObjectivesThe goals of this study were to examine the feasibility of using ontology-based text mining with CaringBridge social media journal entries in order to understand journal content from a whole-person perspective. Specific aims were to describe Omaha System problem concept frequencies in the journal entries over a four-step process overall, and relative to Omaha System Domains; and to examine the four step method including the use of standardized terms and related words. DesignOntology-based retrospective observational feasibility study using text mining methods. SampleA corpus of social media text consisting of 13,757,900 CaringBridge journal entries from June 2006 to June 2016. MeasuresThe Omaha System terms, including problems and signs/symptoms, were used as the foundational lexicon for this study. Development of an extended lexicon with related words for each problem concept expanded the semantics-powered data analytics approach to reflect consumer word choices. ResultsAll Omaha System problem concepts were identified in the journal entries, with consistent representation across domains. The approach was most successful when common words were used to represent clinical terms. Preliminary validation of journal examples showed appropriate representation of the problem concepts. ConclusionsThis is the first study to evaluate the feasibility of using an interface terminology and ontology (the Omaha System) as a text mining information model. Further research is needed to systematically validate these findings, refine the process as needed to advance the study of CaringBridge content, and extend the use of this method to other consumer-generated journal entries and terminologies.

IDENTIFYING MORPHOLOGICAL PROPERTIES OF RUSSIAN WORDS WITH THE ONTOLOGY-BASED TEXT ANALYSER

This article presents the first stage of an ongoing effort of creating the application Ontology-Based Text Analyser (OTA) aimed at automatically identifying semantics and grammatical properties of widely used Russian words in connected texts. At present this application identifies only morphological properties of Russian words. In this application all morphological properties of content words and grammatical function words are revealed on the basis of a query to the Ontology of Russian Grammatical Forms (OntoRuGrammaForm) that we set up earlier. In OntoRuGrammaForm we used LexInfo which represents morphological properties of words in the ontological format as a scheme for data organising. To set up OntoRuGrammaForm the existing LexInfo ontology was extended with missing and refined grammatical categories. In OntoRuGrammaForm the linkage of semantics with morphological properties is implemented with OntoLex which makes it possible to link grammatical word forms with lemmas and lemmas with concepts in knowledge area ontologies. The automatic process of word morphology identification is illustrated with a connected text of the informative type taken from the open news online-portal. In this news text the system of morphological properties of words is identified with OntoRuGrammaForm. This application also displays lemmas and transcription of separate words in a connected text. The created application (OTA) can be used as an innovative methodical tool in teaching Russian to foreign students to develop skills of identifying morphological characteristics of words in texts. At present this application is available on the Web in open access and can be used for analysing morphological properties of widely used Russian words in a connected text.

Autism research dynamic through ontology-based text mining

Purpose – The increase of prevalence of autism spectrum disorders (ASD) has been accompanied by much new research. The amount and the speed of growth of scientific information available online have strongly influenced the way of work in the research community which calls for new methods and tools to support it. The purpose of this paper is to present ontology-based text mining in the field of autism trend analysis that may help to understand the broader picture of the disorder since its discovery. Design/methodology/approach – The data sets consisted of abstracts of more than 18,000 articles on ASD published from 1943 to the end of 2012 found in MEDLINE and of the documents’ titles for all those articles where the abstracts were not available. Findings – In this way, the authors demonstrated a steeper exponential curve of ASD publications compared with all publications in MEDLINE. In addition, the main research topics over time were identified using the “open discovery” approach. Finally, the relationship between a priori setting up research topics including communication, genetics, environmental risk factors, vaccination and adulthood were revealed. Originality/value – Using ontology-based text mining the authors were able to identify the main research topics in the field of autism during the time, as well as to show the dynamics of some research topics as a priori setting up. The computerised methodology that was used allowed the authors to analyse a much larger quantity of information, saving time and manual work.

Improving ontology-based text classification: An occupational health and security application

Information retrieval has been widely studied due to the growing amounts of textual information available electronically. Nowadays organizations and industries are facing the challenge of organizing, analyzing and extracting knowledge from masses of unstructured information for decision making process. The development of automatic methods to produce usable structured information from unstructured text sources is extremely valuable to them. Opposed to the traditional text classification methods that need a set of well-classified trained corpus to perform efficient classification; the ontology-based classifier benefits from the domain knowledge and provides more accuracy. In a previous work we proposed and evaluated an ontology-based heuristic algorithm [28] for occupational health control process, particularly, for the case of automatic detection of accidents from unstructured texts. Our extended proposal is more domain dependent because it uses technical terms and contrast the relevance of these technical terms into the text, so the heuristic is more accurate. It divides the problem in subtasks such as: (i) text analysis, (ii) recognition and (iii) classification of failed occupational health control, resolving accidents as text analysis, recognition and classification of failed occupational health control, resolving accidents.

Intellectual Information Support for Operation of Technical Systems Based on Ontological Engineering

The software, information, methodical and intellectual support of the equipment operation provision is a critical issue to guarantee the success of enterprises. This paper devoted to the more efficient use of expert knowledge in the field of maintenance and repair of equipment, through the development of ontologies of different technical systems and algorithms to work with them. Developed ontology of road construction machinery, office equipment, maintenance services and contractors shown. Described the process of combining classifiers for industrial equipment. Showed generating circuit files of ontology-based text analysis classifier of fixed assets. The algorithms use methods and derived models. After construction and filling of the respective ontologies were formulated a set of tasks to work with them, and we get algorithms work with them. This paper brings a contribution to increasing of performance of equipment maintenance.

An Ontology-Based Text Mining Method to Develop D-Matrix From Unstructured Text

Fault dependency (D)-matrix is a systematic diagnostic model [7] to capture the hierarchical system-level fault diagnostic information consisting of dependencies between observable symptoms and failure modes associated with a system. Constructing a D-matrix from first principles and updating it using the domain knowledge is a labor intensive and time consuming task. Further, in-time augmentation of D-matrix through the discovery of new symptoms and failure modes observed for the first time is a challenging task. Here, we describe an ontology-based text mining method for automatically constructing and updating a D-matrix by mining hundreds of thousands of repair verbatim (typically written in unstructured text) collected during the diagnosis episodes. In our approach, we first construct the fault diagnosis ontology consisting of concepts and relationships commonly observed in the fault diagnosis domain. Next, we employ the text mining algorithms that make use of this ontology to identify the necessary artifacts, such as parts, symptoms, failure modes, and their dependencies from the unstructured repair verbatim text. The proposed method is implemented as a prototype tool and validated by using real-life data collected from the automobile domain.

Using ontology-based text classification to assist Job Hazard Analysis

The dangers of the construction industry due to the risk of fatal hazards, such as falling from extreme heights, being struck by heavy equipment or materials, and the possibility of electrocution, are well known. The concept of Job Hazard Analysis is commonly used to mitigate and control these occupational hazards. This technique analyzes the major tasks in a construction activity, identifies all potential task-related hazards, and suggests safe approaches to reduce or avoid each of these hazards. In this paper, the authors explore the possibility of leveraging existing construction safety resources to assist JHA, aiming to reduce the level of human effort required. Specifically, the authors apply ontology-based text classification (TC) to match safe approaches identified in existing resources with unsafe scenarios. These safe approaches can serve as initial references and enrich the solution space when performing JHA. Various document modification strategies are applied to existing resources in order to achieve superior TC effectiveness. The end result of this research is a construction safety domain ontology and its underlying knowledge base. A user scenario is also discussed to demonstrate how the ontology supports JHA in practice.

Ontology-based text summarization. The case of Texminer

Purpose – The purpose of this paper is to look into the latest advances in ontology-based text summarization systems, with emphasis on the methodologies of a socio-cognitive approach, the structural discourse models and the ontology-based text summarization systems. Design/methodology/approach – The paper analyzes the main literature in this field and presents the structure and features of Texminer, a software that facilitates summarization of texts on Port and Coastal Engineering. Texminer entails a combination of several techniques, including: socio-cognitive user models, Natural Language Processing, disambiguation and ontologies. After processing a corpus, the system was evaluated using as a reference various clustering evaluation experiments conducted by Arco (2008) and Hennig et al. (2008). The results were checked with a support vector machine, Rouge metrics, the F-measure and calculation of precision and recall. Findings – The experiment illustrates the superiority of abstracts obtained through the assistance of ontology-based techniques. Originality/value – The authors were able to corroborate that the summaries obtained using Texminer are more efficient than those derived through other systems whose summarization models do not use ontologies to summarize texts. Thanks to ontologies, main sentences can be selected with a broad rhetorical structure, especially for a specific knowledge domain.