Ontology Learning Research Articles

Analysis of Emotional Education Infiltration in College Physical Education Based on Emotional Feature Clustering

Integration of emotional education physical education research in colleges and universities is a topic that has both theoretical and practical implications. Teachers in physical education should pay attention not only to the development of students’ physical knowledge and skills but also to their emotional education, emphasizing the positive role of emotional factors. Emotion is a type of internal experience that people have when deciding whether or not objective things can meet their needs. The diversity of emotional needs, the contradiction of emotional experience, the singleness of emotional orientation, and the universality of negative emotions are all new characteristics of contemporary college students’ emotional development. A teaching feedback strategy generation algorithm based on the emotional learning ontology is designed to obtain the students’ perception and understanding of the learning process without the need for question-and-answer interaction with the students. Based on “emotional triggers,” a better understanding of students’ learning status can be provided, as well as better cognitive and emotional support. The main measure and means to improve teaching quality and efficiency in physical education is to combine cognition, emotion, and education. This paper discusses the connotation and scope of emotional education in colleges and universities and proposes ideas for strengthening emotional education in colleges and universities in light of the new characteristics of college students’ emotional development, based on the clustering of emotional features, in terms of emphasizing the emotional influence of teachers, bolstering students’ aesthetic education, and exploring new avenues for college students’ emotional education.

Arabic ontology extraction model from unstructured text

Ontologies enable the sharing and reusing of knowledge, allowing the interoperation and combination of information systems. Automatic ontological relation extraction from text is an important factor for representing documents and their contents in a useful computerized manner. This paper proposes a model for automatic ontology extraction from Arabic text by parsing sentences and extracting part of speech (POS). Then, the proposed rule-based model is applied to extract the triple attributes of a sentence (subject, predicate, and object) from the parsing tree. Finally, the semantic relations can be used to extract new triples inferred from the initially extracted triples. The results were evaluated using two methods: the first method was based on a comparison with manual extraction because there is no standard method for measuring Arabic triple extraction; the second method is based on translating the dataset into English and comparing the output results using the Stanford dependencies extraction web tool. The proposed model achieved an accuracy of 73.6% for Arabic triple extraction and a 35% increase in overall triples owing to new inferred triples. However, when the dataset was translated into English and tested by the Stanford dependencies extraction web tool, the accuracy was 71.8% without inferred triples.

Applying ontology learning and multi-objective ant colony optimization method for focused crawling to meteorological disasters domain knowledge

The focused crawler based on semantic analysis is a research hotspot in the field of information retrieval. The domain ontology is generally applied to construct the topic model of the focused crawler. In order to overcome the limitations of builders' knowledge reserve and subjective consciousness in the process of constructing artificially ontology, a semi-automatic construction method of domain ontology based on ontology learning technology combining the latent Dirichlet allocation and the Apriori algorithm is proposed in this article. When evaluating the relevance between a hyperlink and a specific topic, the joint evaluation method considering both the web text and the link structure is usually used. However, the traditional weighted sum method is difficult to reasonably determine the optimal weights of these evaluating indicators. To solve this problem, a multi-objective optimization model for link evaluation and a subsequent multi-objective ant colony optimization algorithm (MOACO) are proposed. In the MOACO, a method of the nearest farthest candidate solution (NFCS) is combined with the fast non-dominated sorting to select a set of Pareto-optimal hyperlinks and guide the crawlers’ search directions. The experimental results of the focused crawling on the domain knowledge of typhoon disasters and rainstorm disasters prove that the ability of the proposed focused crawlers to retrieve topic-relevant webpages.

Adaptive Learning System of Ontology using Semantic Web to Mining Data from Distributed Heterogeneous Environment

Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments achieving both semantic web and SNA tools. Its contribution clearly appears on the community productions and skills developments. Python 3.9.0 platform was used for data pre-processing, feature extraction and clustering via Naïve Bayesian and support vector machine. Two case studies were conducted to test the accuracy rate of the proposed system. The accuracy rate for the case studies was (90.771%) and (90.1149 %) respectively, which is considered an affective precision when it is compared with the related scenario with the same data set.

Transcendental Logic-Based Formalism for Semantic Representation of Software Project Requirements Architecture

This article is devoted to the analysis of the situation that has arisen in the practice of using artificial intelligence methods for software development. Nowadays there are many disparate approaches, models, and practices based on the use of narrow intelligence for decision-making at different stages of the life cycle of software products, and an almost complete lack of solutions brought to wide practical use. The article provides a comprehensive overview of the main reasons for the lack of the expected effect from the implementation of Agile and suggests a way to solve this problem based on the use of a self-organizing knowledge model. Based on the heuristic usage of transcendental logic in the terms of "ontological predicates", such a model makes it possible to create a formalism of the semantic representation of the requirements architecture of a software project, which could provide semantic interoperability and an executable semantic framework for automated ontology generation from unstructured informal software requirements text. The main benefit of this model is that it is flexible and ensures the accumulation of knowledge without the need to change the initial infrastructure as well as that the ontology inference engine is the part of the mechanism of collective interaction of active elements of knowledge and not some externally programmed system of rules that imitate the process of thinking.

An artificially intelligent approach for automatic speech processing based on triune ontology and adaptive tribonacci deep neural networks

Automatic Speech Recognition systems have become essential for an independent automation during the present-day era. A hybrid approach for Automatic Speech Recognition, the TriNNOnto has been proposed in this paper which, integrates different approaches like Language Model integrated with dynamic Triune Ontology generation scheme, Acoustic Model and Feature modelling are hybridised based on the Tribonacci based Deep Neural Network, which decides upon the number of layers depending on the size of the samples and their count. The dynamic generation of Ontologies based on the language models and triune ontology for automatic speech recognition is quite novel. The strategies for feature extraction as and the Tribonacci based deep neural network, based the dynamic adjustment of the number of layers using Tribonacci series contributes towards novelty as well as enhances the performance of speech recognition. The proposed strategy has been evaluated for two datasets and an accuracy of 98.15% and 95.18%, have been achieved for the CMUKids and the TIMIT datasets, respectively with low word error rates.

From communities to protein complexes: A local community detection algorithm on PPI networks.

Identifying protein complexes in protein-protein interaction (ppi) networks is often handled as a community detection problem, with algorithms generally relying exclusively on the network topology for discovering a solution. The advancement of experimental techniques on ppi has motivated the generation of many Gene Ontology (go) databases. Incorporating the functionality extracted from go with the topological properties from the underlying ppi network yield a novel approach to identify protein complexes. Additionally, most of the existing algorithms use global measures that operate on the entire network to identify communities. The result of using global metrics are large communities that are often not correlated with the functionality of the proteins. Moreover, ppi network analysis shows that most of the biological functions possibly lie between local neighbours in ppi networks, which are not identifiable with global metrics. In this paper, we propose a local community detection algorithm, (lcda-go), that uniquely exploits information of functionality from go combined with the network topology. lcda-go identifies the community of each protein based on the topological and functional knowledge acquired solely from the local neighbour proteins within the ppi network. Experimental results using the Krogan dataset demonstrate that our algorithm outperforms in most cases state-of-the-art approaches in assessment based on Precision, Sensitivity, and particularly Composite Score. We also deployed lcda, the local-topology based precursor of lcda-go, to compare with a similar state-of-the-art approach that exclusively incorporates topological information of ppi networks for community detection. In addition to the high quality of the results, one main advantage of lcda-go is its low computational time complexity.

Semantic Integration of Heterogeneous Data Sources Using Ontology-Based Domain Knowledge Modeling for Early Detection of COVID-19.

The enormous outbreak of biomedical knowledge, the aim of reducing computation and processing costs and the widespread availability of internet connection have created a profuse amount of electronic data. Such data are stored across the globe in various data sources that are semantically, structurally and syntactically different. This decentralized nature of biomedical data has made it difficult to obtain a unified view of the data. Data integration plays a crucial role in enhancing access to heterogeneous data making the retrieval easier and faster. A variety of ontology, machine learning, deep learning and fuzzy logic-based solutions are being developed for heterogeneous data integration. The proposed model concentrates on the automatic ontology-based data integration method that can be effectively deployed and used in the healthcare domain. The proposed model is divided into three phases. The first phase includes the automatic mapping of data and generation of local ontology across heterogeneous data sources, the second phase combines the local ontology models developed in the first phase to create a root global schema mapping and the third phase queries diverse databases to retrieve semantically analogous records. The model is created based on the medical records, chest X-ray details and COVID-19 symptom questionnaire data of various patients distributed across three data sources (SQL, mongodb and excel). Based on the data, the patients who have moderate/higher risk of developing serious illness from COVID-19 are retrieved.

Iterative knowledge discovery for fault detection in manufacturing systems

An increasing attention is paid to fault detection in manufacturing. Researches are carried out in order to improve the quality and productivity in such systems. Machine Learning (ML) techniques are often used for fault detection tasks. Besides, ontology and semantic web technologies have a great potential to represent, organize and reuse the expert knowledge.In this paper, a Knowledge-based fault detection method for manufacturing processes is proposed, relying on Ontology and Machine Learning techniques. The approach is iterative in the sense that new faults can be detected by ML and added as new knowledge into the ontology periodically. It eases fault detection in industrial contexts, where faults are generally rare. Experiments conducted with seven fault-detection oriented UCI datasets have shown the effectiveness of our proposal. It is composed of a real-time classifier with a reject option, to enable the detection of new defects based on the existing knowledge described in the ontology. When new defect signature is discovered, it is added into the ontology as new knowledge through a semantic mapping. As a result, we have shown the ability of the proposed architecture to detect new faults and to increase the overall accuracy as new faults are included in the ontology. It also conducts to an evolving ontology that will be used in further research to support a generalization process to enable the detection of known defects in new contexts.

Извлечение отношений из NER-размеченных предложений для обучения онтологии

This paper is a continuation of the research on training and using a neural-network language model for ontology learning. The paper deals with the problem of analyzing domain natural language texts and extracting possible relations between concepts from them. A brief overview of existing approaches to solving this problem is presented. A procedure for extracting relations between concepts based on the analysis of the sentence syntax dependency tree is proposed. The results of evaluating the efficiency of extracting relations using this procedure are considered.

Технология обучения нейросетевой модели для пополнения онтологии

The paper considers one of the subtasks of ontology learning - the ontology population, which implies the extension of existing ontology by new instances without changing the structure of its classes and relations. A brief overview of existing ontology learning approaches is presented. A highly automated technology for ontology population based on training and application of the neural-network language model to identify and extract potential instances of ontology classes from domain texts is proposed. The main stages of its application, as well as the results of its experimental evaluation and the main directions of its further improvement are considered.

Review of Tools for Semantics Extraction: Application in Tsunami Research Domain

Immense numbers of textual documents are available in a digital form. Research activities are focused on methods of how to speed up their processing to avoid information overloading or to provide formal structures for the problem solving or decision making of intelligent agents. Ontology learning is one of the directions which contributes to all of these activities. The main aim of the ontology learning is to semi-automatically, or fully automatically, extract ontologies—formal structures able to express information or knowledge. The primary motivation behind this paper is to facilitate the processing of a large collection of papers focused on disaster management, especially on tsunami research, using the ontology learning. Various tools of ontology learning are mentioned in the literature at present. The main aim of the paper is to uncover these tools, i.e., to find out which of these tools can be practically used for ontology learning in the tsunami application domain. Specific criteria are predefined for their evaluation, with respect to the “Ontology learning layer cake”, which introduces the fundamental phases of ontology learning. ScienceDirect and Web of Science scientific databases are explored, and various solutions for semantics extraction are manually “mined” from the journal articles. ProgrammableWeb site is used for exploration of the tools, frameworks, or APIs applied for the same purpose. Statistics answer the question of which tools are mostly mentioned in these journal articles and on the website. These tools are then investigated more thoroughly, and conclusions about their usage are made with respect to the tsunami domain, for which the tools are tested. Results are not satisfactory because only a limited number of tools can be practically used for ontology learning at present.

Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments

Knowledge engineering relies on ontologies, since they provide formal descriptions of real-world knowledge. However, ontology development is still a nontrivial task. From the view of knowledge engineering, ontology learning is helpful in generating ontologies semi-automatically or automatically from scratch. It not only improves the efficiency of the ontology development process but also has been recognized as an interesting approach for extending preexisting ontologies with new knowledge discovered from heterogenous forms of input data. Driven by the great potential of ontology learning, we present an automatic ontology-based model evolution approach to account for highly dynamic environments at runtime. This approach can extend initial models expressed as ontologies to cope with rapid changes encountered in surrounding dynamic environments at runtime. The main contribution of our presented approach is that it analyzes heterogeneous semi-structured input data for learning an ontology, and it makes use of the learned ontology to extend an initial ontology-based model. Within this approach, we aim to automatically evolve an initial ontology-based model through the ontology learning approach. Therefore, this approach is illustrated using a proof-of-concept implementation that demonstrates the ontology-based model evolution at runtime. Finally, a threefold evaluation process of this approach is carried out to assess the quality of the evolved ontology-based models. First, we consider a feature-based evaluation for evaluating the structure and schema of the evolved models. Second, we adopt a criteria-based evaluation to assess the content of the evolved models. Finally, we perform an expert-based evaluation to assess an initial and evolved models’ coverage from an expert’s point of view. The experimental results reveal that the quality of the evolved models is relevant in considering the changes observed in the surrounding dynamic environments at runtime.

Automatic Taxonomy Classification by Pretrained Language Model

In recent years, automatic ontology generation has received significant attention in information science as a means of systemizing vast amounts of online data. As our initial attempt of ontology generation with a neural network, we proposed a recurrent neural network-based method. However, updating the architecture is possible because of the development in natural language processing (NLP). By contrast, the transfer learning of language models trained by a large, unlabeled corpus has yielded a breakthrough in NLP. Inspired by these achievements, we propose a novel workflow for ontology generation comprising two-stage learning. Our results showed that our best method improved accuracy by over 12.5%. As an application example, we applied our model to the Stanford Question Answering Dataset to show ontology generation in a real field. The results showed that our model can generate a good ontology, with some exceptions in the real field, indicating future research directions to improve the quality.

An ontology self-learning approach for CNC machine capability information integration and representation in cloud manufacturing

Manufacturing activities in cloud-based environments strongly rely on the online integration and description of the capability of machining resources. In the authors’ previous work, STEP-NC schema was applied to construct an ontology model to support the integration and reasoning of machine tool information and capability. For the maintenance and update of the model, in this paper, a self-learning method is proposed to explore correlations from STEP-NC process planning documents to obtain machining knowledge to improve the comprehensiveness of the model. In this method, a Map/Reduce-based Apriori algorithm is developed incombination with the built ontological model. First, a dataset is extracted from the document according to the importance analysis results of the model. Then, a mining procedure that combinesApriori algorithm and Map/Reduce framework is developed. Finally, two representation modes are adopted to embed the mined results into the model. According to the outcomes of a preliminary experiment with standard STEP-NC documents, this method effectively enables the ontology learning mechanism from the aspects of time consumption and mined associations, which improves the suitability of the enriched ontology model to handle information integration and industrial applications.

TOWARDS THE AUTOMATIC ONTOLOGY GENERATION AND ALIGNMENT OF BIM AND GIS DATA FORMATS

Abstract. Establishing semantic interoperability between BIM and GIS is vital for geospatial information exchange. Semantic web have a natural ability to provide seamless semantic representation and integration among the heterogeneous domains like BIM and GIS through employing ontology. Ontology models can be defined (or generated) using domain-data representations and further aligned across other ontologies by the semantic similarity of their entities - introducing cross-domain ontologies to achieve interoperability of heterogeneous information. However, due to extensive semantic features and complex alignment (mapping) relations between BIM and GIS data formats, many approaches are far from generating semantically-rich ontologies and perform effective alignment to address geospatial interoperability. This study highlights the fundamental perspectives to be addressed for BIM and GIS interoperability and proposes a comprehensive conceptual framework for automatic ontology generation followed by ontology alignment of open-standards for BIM and GIS data formats. It presents an approach based on transformation patterns to automatically generate ontology models, and semantic-based and structure-based alignment techniques to form cross-domain ontology. Proposed two-phase framework provides ontology model generation for input XML schemas (i.e. of IFC and CityGML formats), and illustrates alignment technique to potentially develop a cross-domain ontology. The study concludes anticipated results of cross-domain ontology can provides future perspectives in knowledge-discovery applications and seamless information exchange for BIM and GIS.

A Domain-Independent Ontology Learning Method Based on Transfer Learning

Ontology plays a critical role in knowledge engineering and knowledge graphs (KGs). However, building ontology is still a nontrivial task. Ontology learning aims at generating domain ontologies from various kinds of resources by natural language processing and machine learning techniques. One major challenge of ontology learning is reducing labeling work for new domains. This paper proposes an ontology learning method based on transfer learning, namely TF-Mnt, which aims at learning knowledge from new domains that have limited labeled data. This paper selects Web data as the learning source and defines various features, which utilizes abundant textual information and heterogeneous semi-structured information. Then, a new transfer learning model TF-Mnt is proposed, and the parameters’ estimation is also addressed. Although there exist distribution differences of features between two domains, TF-Mnt can measure the relevance by calculating the correlation coefficient. Moreover, TF-Mnt can efficiently transfer knowledge from the source domain to the target domain and avoid negative transfer. Experiments in real-world datasets show that TF-Mnt achieves promising learning performance for new domains despite the small number of labels in it, by learning knowledge from a proper existing domain which can be automatically selected.

Generative Unit Assessment: Authenticity in Mathematics Classroom Assessment Practices

In our pursuit to broaden and deepen understandings of what it means to engage in an assessment activity, we explored the designing and implementing of a geometry performance task as an instantiation of authentic assessment to assess elementary school students’ mathematics learning. Using participatory action research, we incorporated a performance task as an end-of-unit assessment with grade 4/5 students. We found that the authenticity within what we are calling a generative unit assessment, is understood as a process that is dynamic in contrast to conventional unit tests. We established an innovative assessment practice that emerged from the student and teacher data and is illustrated through four features applicable to any content area. Through collaborative discussions and the ensuing creation of a generative unit assessment, we found spaces to authentically understand ontological growth and continual learning through assessment.

Completeness based classification algorithm: a novel approach for educational semantic data completeness assessment

PurposeThe purpose of this paper is to reveal the smart collaborative learning service. This concept aims to build teams of learners based on the complementarity of their skills, allowing flexible participation and offering interdisciplinary collaboration opportunities for all the learners. The success of this environment is related to predict efficient collaboration between the different teammates, allowing a smartly sharing knowledge in the Smart University environment.Design/methodology/approachA random forest (RF) approach is proposed, which is based on semantic modelization of the learner and the problem-solving allowing multidisciplinary collaboration, and heuristic completeness processing to build complementary teams. To achieve that, this paper established a Konstanz Information Miner workflow that integrates the main steps for building and evaluating the RF classifier, this workflow is divided into: extracting knowledge from the smart collaborative learning ontology, calculating the completeness using a novel heuristic and building the RF classifier.FindingsThe smart collaborative learning service enables efficient collaboration and democratized sharing of knowledge between learners, by using a semantic support decision support system. This service solves a frequent issue related to the composition of learning groups to serve pedagogical perspectives.Originality/valueThe present study harmonizes the integration of ontology, a new heuristic processing and supervised machine learning algorithm aiming at building an intelligent collaborative learning service that includes a qualified classifier of complementary teams of learners.

Relevance of Educational Research: An Ontological Conceptualization

Educational research is repeatedly confronted with the question of its relevance. Current interpretations of relevance narrowly focus on outcomes and impact of research. In this essay, we propose an alternative, ontological conceptualization of relevance, arguing that more is at stake than outcomes and impact. We characterize the ontology of education and learning in terms of people’s meaningful movements in an always changing world and propose that relevance of educational research resides in what we call “ontological synchronization”—continuous attunement to what is happening and matters at hand, and what future is being generated, including what values and judgments researchers themselves perpetuate in society. Such synchronization, we conclude, hinges on a disciplinary and ethical commitment to principles of actuality and generativity. We discuss what such conceptualization of relevance implies for educational research.