Ontology Learning Research Articles

Semi-automated ontology development scheme via text mining of scientific records

PurposeThe purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.Design/methodology/approachThis paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.FindingsThe proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.Originality/valueAccording to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

Ontology learning from object-relational mapping metadata and relational database

Ontologies play an important role in representing the semantics of data sources. Building an ontology as a representation of domain knowledge from available data sources is not a simple process, particularly when dealing with relational data, which remains prevalent in existing knowledge systems. In this study, we create an ontology from a relational database using object-relational mapping (ORM) metadata as additional rules for mapping. Our method comprises two main phases: ontology schema construction using ORM metadata and the generation of ontology instances from the relational database. During the initial phase, we analyzed the ORM metadata to map it to an resource description framework schema (RDF(S))-OWL representation of the ontology. In the subsequent phase, we applied mapping rules to convert the relational database (RDB) data into ontological instances, which are then represented as RDF triples. Using ORM metadata, we enhance the accuracy of the resulting ontology, particularly in terms of extracting concepts and hierarchical relationships. This study contributes to the field of ontology learning by showcasing a novel approach that leverages ORM metadata to create ontologies from relational databases.

A Systematic Review of Stemmers of Indian and Non-Indian Vernacular Languages

The stemming process is crucial and significant in the pre-processing step of natural language processing. The stemmer oversees the stemming process. It facilitates the extraction of morphological variants of a root or base word from the provided word. Over the period, several stemmers for various vernacular languages have been proposed. However, very few research studies have comprehensively investigated these available stemmers. This article makes multifold contributions. First, we discuss the various stemmers of 15 Indian and 17 non-Indian languages describing their key points, benefits, and drawbacks. All the Indian languages for which stemmers have been built are covered in this study. For the non-Indian languages, stemmers of commonly spoken languages have been covered. Second, we present a language-wise comparative analysis of stemmers based on our identified parameters. Third, we discuss the wordnets and dictionaries available for different languages. Fourth, we provide details of the datasets available for various languages. Fifth, we also provide challenges in existing stemmers and future directions for future researchers. The study presented in this article reveals that significant research has been carried out for the stemmers of influential languages such as English, Arabic, and Urdu. On the other hand, languages with d resources, such as Farsi, Polish, Odia, Amharic, and others, have received the least attention for research. Moreover, rigorous analysis reveals that most of the stemmers suffer from over-stemming errors. With a complete catalogue of available stemmers, this study aims at assisting the researchers and professionals working in the areas such as information retrieval, semantic annotation, word meaning disambiguation, and ontology learning.

Generating knowledge graphs through text mining of catalysis research related literature

Ontology learning and named entity recognition are used to automate text data extraction from catalysis research and organizing it into a knowledge graph. Extending the CatalysisIE model practical use of the workflow for researchers is demonstrated.

DarkOnto: An Ontology Construction Approach for Dark Web Community Discussions Through Topic Modeling and Ontology Learning

Social networks on the dark web are rich in data that provides valuable insight into the nature of the activities on the dark web and human behaviors related to these activities. It also encompasses a diversity of ideologies, interests, and thought patterns associated with illicit activities and businesses on the dark web. For this reason, social networks on the dark web constitute a powerful tool and a profuse data source for various investigative work. However, such investigations encounter considerable challenges related to the massive volumes of textual data, analyzing it effectively, and extracting knowledge from it. This knowledge can be used in various investigations and studies when representing it in ontologies as a unified and integrative data source. In this paper, we introduce a novel approach for extracting and representing knowledge hidden in dark web communities through topic modeling and ontology learning methods. We start from the conceptual design of the ontology and employ several stages of text processing and analysis to achieve the desired knowledge graph, DarkOnto. These stages include data cleaning and preprocessing, topic modeling using correlated topic model (CTM), class‐topic similarity estimation, ontology construction, ontology population, and ontology evaluation, where the proposed approach achieved high results. Furthermore, we discuss the results, limitations, challenges, and future work. This paper presents a promising approach for extracting hidden valuable knowledge from dark web communities where investigating and conceptualizing criminal communities can be conducted efficiently.

Literature review on aftershock and earthquake prediction models aided by NLP summarization and ontology extraction techniques

The field of aftershock prediction has evolved over the years, transitioning from traditional statistical models to more advanced machine learning and deep learning methodologies. The USGS relies on the Reasenberg and Jones (1989, 1994) statistical model which subsequent researchers have refined to adapt to various tectonic regimes globally. Meanwhile, DeVries et al. (2018) explored the potential of Deep Learning in predicting aftershock locations, albeit facing critique for potentially over-complicating the issue. Other researchers have applied machine learning algorithms, such as in predicting aftershock patterns following the Kermanshah Earthquake in Iran, demonstrating the capability of machine learning to outperform traditional Coulomb maps. The dynamic discourse among these varying methodologies highlights the ongoing efforts to improve aftershock prediction, aiming at better preparedness and response strategies in seismic-prone regions.

Forecasting learning in electrical engineering and informatics: An ontological approach

This research explores the vital role of ontology in learning forecasting in electrical engineering and informatics. As formally defined models of knowledge, ontologies are critical in organizing concepts for predictive learning. More than just an inquiry, our research reveals complex interconnections centered on Internet of Things (IoT) design, the semantic web, and knowledge modeling. Applications demonstrate the practical significance of ontologies in fostering intelligent connections, advancing information production, and improving interactions between computers, devices, and humans. This research introduces a comprehensive forecasting learning ontology to highlight the importance of ontologies in education, scientific inquiry, and developing systems for predictive analysis. Ontologies provide a structured framework for understanding the essence of predictive learning, encompassing key elements such as ideas, terminology, methodology, algorithms, data preprocessing, assessment, validation, data sources, application environments, interactions with technology, and learning processes. Emphasizing ontologies as indispensable instruments that drive technological development, our work underscores structured representation, semantic interoperability, and knowledge integration. In summary, this research improves the understanding of ontologies in forecasting by explaining practical applications and revealing new perspectives. Its unique contribution lies in its specific applications and natural consequences, laying the foundation for the future progress of ontology and learning forecasting, especially in educational contexts.

Arabic Opinion Mining System for E-Services Based On Ontology and Machine Learning

With the growing use of E-Services in the Arab world, there is a need for efficient methods to analyze customer feedback and opinions. This research is proposing an Arabic opinion mining system for E-Services based on ontology and machine learning techniques. The system utilizes an Arabic ontology to capture domain-specific vocabulary and relationships between features and a machine learning model to classify customer feedback into positive, negative, or neutral sentiments. By evaluating the performance of the system using several domains datasets for Arabic customer reviews of various e-services, and the results show that the system achieves a high accuracy level. Comparing the performance of the proposed system to traditional polarity and subjectivity approaches.

Формирование лексического модуля прикладной онтологии для её обучения

The complexity of developing ontologies is associated with their learning, which involves the automation of solving problems related to the extraction of ontological concepts and relationships from natural language texts. For a created ontology used in an information system, there will most likely be a need to expand its conceptual system due to the complication or change in data processing. Subsequent application of text analysis to discover new concepts and in-corporate them into the ontology, that is, linking them with existing concepts, will require identifying the latter in sen-tences of natural language texts. This paper considers the problem of automating the formation of the lexical module of applied ontology, which includes formalized representations of ontological concepts in natural language texts. A brief review of existing works devoted to the use of lexical information about ontology components in solving problems related to the analysis of textual data is presented. The ways of using the OntoLex-Lemon model for determining the structure of lexical representations of ontology concepts are considered. A procedure is proposed for the formation of lexical representations based on the analysis of texts in the subject area, taking into account the case when concepts have names consisting of several words. The results of applying the obtained module for the automatic formation of a training set of a neural network language model used in the ontology learning task for discovering new concepts in the corpus of subject texts are presented.

BAYESIAN LEARNING OF COVID-19 VACCINE SAFETY WHILE INCORPORATING ADVERSE EVENTS ONTOLOGY.

While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, none of these methods considered the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia, and reflux are all related to an abnormal digestive system. Explicitly bringing AE relationships into the model can aid in the detection of true AE signals amid the noise while reducing false positives. We propose a Bayesian graph-assisted signal selection (BGrass) model to simultaneously estimate all AEs while incorporating the network of dependence between AEs. Under a fully Bayesian inference framework, we also propose a negative control approach to mitigate the reporting bias and an enrichment approach to detecting AE groups of concern. For posterior computation we construct an equivalent model representation and develop an efficient Gibbs sampler. We evaluate the performance of BGrass via extensive simulations. To study the safety of COVID-19 vaccines, we apply BGrass to analyze approximately one million VAERS reports (01/01/2016-12/24/2021) involving more than 800 AEs. In particular, we found that blood clots (including deep vein thrombosis, thrombosis, and pulmonary embolism) are more likely to be reported after COVID-19 vaccination, compared to influenza vaccines. They are also reported more often for Johnson & Johnson-Janssen vaccine, compared to mRNA-based COVID-19 vaccines. A user-friendly R package BGrass that implements the proposed methods to assess vaccine safety is included in the Supplementary Material and is publicly available at https://github.com/BangyaoZhao/BGrass.

Semi-Automatic Building and Learning of a Multilingual Ontology

Most online platforms, applications, and Websites use a massive amount of heterogeneous evolving data. These data must be structured and normalized before integration to improve the search and increase the relevance of results. An ontology can address this critical task by efficiently managing data and providing structured formats through techniques such as the Web Ontology Language (OWL). However, building an ontology can be costly, primarily if conducted manually. In this context, we propose a new methodology for automatically building and learning a multilingual ontology using Arabic as the base language via a corpus collected from Wikipedia. Our proposed methodology relies on Finite-state transducers (FSTs). FSTs are regrouped into a cascade to reduce errors and minimize ambiguity. The produced ontology is extended to English and French and independent language images via a translator we developed using APIs. The rationale for starting with the Arabic corpus to extract terms is that entity linking is more convenient from Arabic to other languages. In addition, many Wikipedia articles in English and French (for instance) do not have associated Arabic articles, but the opposite is true. In addition, dealing with Arabic terms permits us to enrich the Arabic module of the free linguistic platform we use in dictionaries and graphs. To assess the efficiency of our proposed methodology, we conducted performance metrics. The reported results are encouraging and promising.

Business English Learning Path Based on Knowledge Mapping in the Context of Educational Informatisation

Abstract In this paper, we first construct ontology and entity learning to construct a knowledge graph by matching the knowledge to the schema layer. Then, we obtain users’ learning styles, extract interest keywords from user behavioral data, map them into the vector space encoded based on the KgTransH algorithm, and compare them with the original algorithm on multiple datasets. Then, the business English learning path is planned based on learning path length, learning time and achievement ranking. Finally, the effect of the knowledge graph-based business English learning path planning method is analyzed through comparative experiments. Through 50 iterations, the satisfaction of the knowledge graph method, GA method and ACO method are between 4.3-4.8, 4-4.7 and 4.05-4.7, respectively, which indicates that the method of this paper is better than the other methods. This study is important for improving the learning effect of Business English.

Semantic analysis based on ontology and deep learning for a chatbot to assist persons with personality disorders on Twitter

ABSTRACT This paper presents a chatbot taking advantage of semantic analysis based on ontology and deep learning techniques for ensuring the monitoring of Twitter users with personality disorders during the period of COVID-19. The monitoring provided by our work consists of (i) removing inappropriate tweets from the newsfeed of the sick person according to their state, (ii) providing via a chatbot an answer to the sick person in the form of another tweet that can help him to overcome their concerns about a problem related to the epidemic. Our approach was started by detecting people having personality disorders on Twitter, followed by detecting their behaviour towards COVID-19 expressed in tweets posted in relation to this epidemic. After that, moving to perform the filtration and the recommendation tasks of tweets based on a semantic analysis. Our semantic analysis is achieved at first by querying an ontology based on a comparison taking into account concepts and behaviour expressed. Then, via a deep learning approach in order to resolve untreated cases by the ontology. For the evaluation part, we obtained an F-measure value equals to 72% for the task of filtering inappropriate tweets and 75% for the task of recommended tweet.

Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review.

Speech intelligibility and speech comprehension for dysarthric speech has attracted much attention recently. Dysarthria is characterized by irregularities in the speed, strength, pitch, breath control, range, steadiness, and accuracy of muscle movements required for articulatory aspects of speech production. This study examined the contributions made by other studies involved in dysarthric speech comprehension. We focused on the modes of meaning extraction used in generalizing speaker-listener underpinnings in light of semantic ontology extraction as a desired technique, applied method types, speech representations used, and databases sourced from. This study involved a systematic literature review using 7 electronic databases: Cochrane Database of Systematic Reviews, Web of Science Core Collection, Scopus, PubMed, ACM, IEEE Xplore, and Google Scholar. The main eligibility criterion was the extraction of meaning from dysarthric speech using natural language processing or understanding approaches to improve on dysarthric speech comprehension. In total, out of 834 search results, 30 studies that matched the eligibility requirements were acquired following screening by 2 independent reviewers, with a lack of consensus being resolved through joint discussion or consultation with a third party. In order to evaluate the studies' methodological quality, the risk of bias assessment was based on the Cochrane risk-of-bias tool version 2 (RoB2) with 23 of the studies (77%) registering low risk of bias and 7 studies (33%) raising some concern over the risk of bias. The overall quality assessment of the study was done using TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis). Following a review of 30 primary studies, this study revealed that the reviewed studies focused on natural language understanding or clinical approaches, with an increase in proposed solutions from 2020 onwards. Most studies relied on speaker-dependent speech features, while others used speech patterns, semantic knowledge, or hybrid approaches. The prevalent use of vector representation aligned with natural language understanding models, while Mel-frequency cepstral coefficient representation and no representation approaches were applied in neural networks. Hybrid representation studies aimed to reconstruct dysarthric speech or improve comprehension. Comprehensive databases, like TORGO and UA-Speech, were commonly used in combination with other curated databases, while primary data was preferred for specific or unique research objectives. We found significant gaps in dysarthric speech comprehension characterized by the lack of inclusion of important listener or speech-independent features in the speech representations, mode of extraction, and data sources used. Further research is therefore proposed regarding the formulation of models that accommodate listener and speech-independent features through semantic ontologies that will be useful in the inclusion of key features of listener and speech-independent features for meaning extraction of dysarthric speech.

An ontology for defining and characterizing demonstration environments

Demonstration Environments (DEs) are essential tools for testing and demonstrating new technologies, products, and services, and reducing uncertainties and risks in the innovation process. However, the terminology used to describe these environments is inconsistent, leading to heterogeneity in defining and characterizing them. This makes it difficult to establish a universal understanding of DEs and to differentiate between the different types of DEs, including testbeds, pilot-plants, and living labs. Moreover, existing literature lacks a holistic view of DEs, with studies focusing on specific types of DEs and not offering an integrated perspective on their characteristics and applicability in different contexts. This study proposes an ontology for knowledge representation related to DEs to address this gap. Using an ontology learning approach analyzing 3621 peer-reviewed journal articles, we develop a standardized framework for defining and characterizing DEs, providing a holistic view of these environments. The resulting ontology allows innovation managers and practitioners to select appropriate DEs for achieving their innovation goals, based on the characteristics and capabilities of the specific type of DE. The contributions of this study are significant in advancing the understanding and application of DEs in innovation processes. The proposed ontology provides a standardized approach for defining and characterizing DEs, reducing inconsistencies in terminology and establishing a common understanding of these environments. This enables innovation managers and practitioners to select appropriate DEs for their specific innovation goals, facilitating more efficient and effective innovation processes. Overall, this study provides a valuable resource for researchers, practitioners, and policymakers interested in the effective use of DEs in innovation.

포스트-휴머니즘적 접근과 회집체로서의 학습체계, 그리고 포스트-평생학습론 사유

Lifelong learning research is continuously influenced by new scientific trends. The posthumanist approach that has recently moved into the mainstream is also increasingly influencing the understanding of lifelong learning. The posthumanist approach's 'more-than-human' perspective aligns with the attempt to understand human learning from an object-oriented and flat ontological standpoint. Posthumanism redefines ontology, offering a different perspective from the previous anthropocentric worldview, that is, the modernist worldview. In this transitional phase, we aim to address three meaningful questions:. 1. Why is it important to understand lifelong learning from the perspective of the posthumanist approach? 2. How does this shift in perspective capture the phenomenological reality of lifelong learning? 3. What new dimension does this approach uncover? The posthumanist approach has liberated lifelong learning from the constraints of educational 'essentialism'. In early models of lifelong learning research, humans were depicted as subjective beings, yet they were often confined to reproducing a predetermined world and essence. Although humans have been portrayed as self-directed learners, the actual learning process has always been tied to curriculums, competencies, and fixed outcomes. A truly Copernican shift in learning starts with relocating lifelong learning to an object-oriented perspective. Post-humanism rejects human pretentious subjectivity and reduces the burden of knowledge imposed upon them by the world. The traditional subject-centered learning theory was reinterpreted as object-oriented in this paper. The essence of the post-lifelong learning concept we are discussing is to move away from understanding learning as the accumulation of individual knowledge by Cartesian individuals. Instead, it is about viewing learning in terms of 'extension performed by activity systems'. In this paper, the researchers, first, redefined the concept of learning within the context of the 'more-than-human' dimension. Second, the learning process was redefined as an activity within a learning system that transcends the object-centeredness of learning and encompasses a systematic level including human-material-desire-movement. Third, the learning system was conceptualized based on the idea of assemblage or assemblage ontology. The combination and movement within the system are described as a process of 'becoming' that emerges from and is produced within the system's elements. Fourth, this lifelong learning phenomenon centered around the 'learning system' was differentiated from the previous learning ontology, which was based on isomorphism. We define this new framework as 3.0, that is, post-lifelong learning, marking it as distinct from the previous lifelong learning concepts of 1.0 or 2.0.

A novel algorithm for the construction of fast English sentence retrieval model using a combination of ontology and advanced machine learning techniques

A novel algorithm for the construction of fast English sentence retrieval model using a combination of ontology and advanced machine learning techniques

Ontology learning from relational database: a review

A relational database (RDB) is a digital database that uses components (such as constraints, tables, keys, etc.) to manage data in a structured manner. Because of these components, RDBs are considered ’poor’ from a semantic point of view, precisely because of the structure-oriented nature of the components used. One way to eliminate this limitation is to transform the RDB into an ontology. The purpose of this article is to review the different approaches existing in the literature to extract data from an RDB and convert it into ontology instances. Two approaches are used to integrate the mapping between RDBs and ontologies. The first allows ontologies to be extracted from an RDB, the second consists of a mapping of the relational database to an existing ontology. Our proposed review focuses on methods for creating a specific ontology from an RDB. The proposed review examines this field, classifying the methods that will be analyzed according to their inputs and outputs. Such classification may be useful for understanding the usability of methods. The aim is to critically review existing studies to help outline this research topic’s progress and identify methods’ gaps and functionalities.

A hybrid ontology‐based semantic and machine learning model for the prediction of spring breakup

AbstractRiver ice breakups carry the potential for high flows and flooding and are of great interest to accurately predict. A challenge in forecasting these events is the management of the massive amounts of data associated with an ice season. This study couples ontological and machine learning models in a new hybrid modeling framework to predict spring breakup on a national scale. The Ice Season Ontology sorts the data and allows for a user‐friendly means of analyzing any ice season, providing insight on which variables are most and least central. With this, a refined variable selection is able to be made for machine learning models. The most successful developed model, a random forest, produced highly accurate forecasts when applied to a national scale case study, with a mean absolute error of 10.85 days and an R2 of .884. This new modeling framework provides a means for decision‐making support for river bound communities and a new methodology for modeling applications in other fields.

SCMD suspicious cryptographic message detection

The proposed framework aims to prevent global crimes, specifically terrorist attacks, bomb blasts, and drone attacks, by using the Suspicious Cryptographic Message Detection (SCMD) system to detect and decrypt suspicious messages. Terrorists often use encrypted messages to communicate with their teammates in different parts of the world, making it difficult for authorities to monitor their activities. The framework addresses this issue by detecting with gradient boosting algorithm and cryptanalyzing by using Simple Substitution Cipher techniques to decrypt suspicious messages and predict potential criminal activity. The framework utilizes various emerging trends in information technology, including machine learning techniques, pre-defined decision rules, wordnet, and semantic web ontology, to predict the type of crime, the criminal's name and location, and other criminal details. It comprises multiple components, including Semantic web Ontology, Suspicious Database, and machine learning techniques, to facilitate the detection and reporting of criminal activity. When the server receives a suspicious encrypted message, the framework detect and decrypts it and predicts the type of crime based on microblogs before it can be executed by the criminals. The details of the criminals are then alerted to the cybercrime department, reducing the burden on security departments and enhancing public safety. In summary, the framework provides a comprehensive solution to the challenges posed by encrypted messages in criminal activities. It uses advanced technologies to identify and predict potential criminal activities, enabling law enforcement agencies to take preemptive actions to prevent major and minor attacks, including terrorist attacks.