Design analysis and implementation for ontology learning model

Ontology plays a very important role in supporting knowledge-based applications. In cloud computing, ontology learning technology is facing new challenges in dealing with heterogeneous data sources from different domains and researchers, which may contain various particular concepts and relations. Traditional ontology learning frameworks usually focus only on the extraction of concepts and taxonomic relations from the multi-structured corpus. However, former researches rarely studied the interactions during ontology learning process among different researchers. Lack of interactions among people who build ontology in different domains may cause inconsistent ontology. Besides, lack of incentive during the ontology building process will also result in low efficiency. To address these challenges, this paper specifies a novel solution to perform ontology learning. The solution includes a service-oriented ontology interaction framework, a service-oriented ontology learning strategy. It shows that it advances ontology learning to a higher level of performance and portability with a number of experiments in demo system.

The essence of all kinds of ontology applications is similarity measuring. In recent years, a variety of learning approaches are introduced to ontology similarity computation and ontology mapping. The purpose of these ontology learning technologies is to get an ontology score function which maps each vertex to a real number, and then the similarity between ontology vertices are judged according to the difference of their scores. However, such learning data should containing all pairs of sample vertices. In this paper, we raise a new ontology learning algorithm in which its training sample set only contains important edges (vertex pair) of which the similarities are to be determined by us here. Our method is based on the Kronecker kernel technologies and the solution is attributed to solving the linear system. Two simulation experiments reveal that our new ontology learning model has higher precision ratio on plant ontology and humanoid robotics ontology for similarity measuring and ontology mapping applications.

Enhancing relevant concepts extraction for ontology learning using domain time relevance

Research on domain ontology in different granulations based on concept lattice

The Semantic Web uses formal ontologies as a key instrument in order to add structure to the data, but building domain specific ontologies is still a difficult, time consuming and error-prone process since most information is currently available as free-text. Therefore the development of fast and cheap solutions for ontology learning from text is a key factor for the success and large scale adoption of the Semantic Web. Ontology development is primarily concerned with the definition of concepts and relations between them, so one of the fundamental research problems related to ontology learning is the extraction of concepts from text. To investigate this research problem we focus on the expert finding problem, i.e, the extraction of expertise topics and their assignment to individuals. The ontological concepts we extract are a person's skills, knowledge, behaviours, and capabilities. For increased efficiency, competitiveness and innovation, every company has to facilitate the identification of experts among its workforce. Even though this can be achieved by using the information gathered during the employment process and through self-assessment, a person's competencies are likely to change over time. Information about people's expertise is contained in documents available inside an organisation such as technical reports but also in publicly available resources, e.g., research articles, wiki pages, blogs, other user-generated content. The human effort required for competency management can be reduced by automatically identifying the experts and expertise topics from text. Our goal is to explore how existing technologies for concept extraction can be advanced and specialised for extracting expertise topics from text in order to build expertise profiles.

Most approaches to ontology learning combine techniques from different areas (hybrid approaches) to increase the efficiency of the ontology learning process. However, the results from the ontology learning process do not fully satisfy the users at present. An important problem is that there is a lack of quantitative and comparative data about the efficiency of techniques and technique combinations applied to ontology learning. In this paper we present a quantitative comparison of technique combinations for concept extraction and a software system (OntoLancs) to support the evaluation of techniques. By applying OntoLancs, users are able to assist the process of building ontologies by semi- automatically acquiring concepts from large-scale domain document collections and experiment with different combinations of knowledge acquisition techniques to refine and organize domain concepts into a taxonomy. Quantitative and comparative studies about the performance of several techniques and user experiences indicate the applicability and usefulness of our approach.

World population in recent decades has significant impacts on the traditional agricultural systems which has resulted in increased demand for food, land use and deforestation, water scarcity, climate changes but not limited to these impacts. In order to overcome all these issues, there is a need for advanced farming technologies for growing the most demand food crops. Smart farming also known as precision agriculture has evolved which uses the advanced technology to optimize the efficiency and productivity of the farming operations. It involves the integration of various technologies such as IoT sensors, drones, robotics and machine learning technologies, big data analytics to gather data on crop growth, environmental conditions and weather patterns. Vertical framing (VF) is one such precision framing efficient crop growth practices which adapts the integration of Internet of Things (IoT) and machine learning (ML) technologies in easier manner. Since, the vertical farming is completely an indoor farming technique, they do not depend on the particular geographical locations and outdoor growth parameters (like soil) for crop cultivation; hence, vertical farming is also known as controlled environment agriculture. This article explores the significance of different indoor vertical farming practices under controlled environment with the comparative analysis, efficiency, productivity, advantages and their potential benefits highlighting the need for sustainable agricultural practices that can meet the growing demand for food while minimizing the negative environmental impacts.

LDAP based search mechanisms in Grid environment are syntactic based and do not support semantic information extraction. With the quick development of Semantic Grid, it demands a more flexible and powerful search mechanism for Grid information discovery. We propose a new search mechanism which extends LDAP and can be used in the Semantic Grid environment for semantic searching of relevant information. The mechanism uses an ontology learning model which maps WordNet lexicon into OWL ontologies. Such ontology assistant mechanism can utilize the reasoning services from particular description logic systems and make use of more machine automation in the searching process.

Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called CRCTOL for mining rich semantic knowledge in the form of ontology from domain-specific text documents. By using a full text parsing technique and incorporating both statistical and lexico-syntactic methods, the knowledge extracted by our system is more concise and contains a richer semantics compared with alternative systems. We conduct a case study wherein CRCTOL extracts ontological knowledge, specifically key concepts and semantic relations, from a terrorism domain text collection. Quantitative evaluation, by comparing with a state-of-the-art ontology learning system known as text-to-onto, has shown that CRCTOL produces much better precision and recall for both concept and relation extraction, especially from sentences with complex structures.

Ontology learning from unstructured text has become a critical task for knowledge-driven applications in Big Data and Artificial Intelligence. While significant advances have been made in the automatic extraction of concepts and relations using neural and Transformer-based models, the generation of formal Description Logic axioms required for constructing logically consistent and computationally tractable ontologies remains largely underexplored. This paper puts forward a novel pipeline for automated axiom generation through schema mapping. Our paper introduces three key innovations: a deterministic mapping framework that guarantees logical consistency (unlike stochastic Large Language Models); guaranteed formal consistency verified by OWL reasoners (unaddressed by prior statistical methods); and a transparent, scalable bridge from neural extractions to symbolic logic, eliminating manual post-processing. Technically, the pipeline builds upon the outputs of a Transformer-based fusion model for joint concept and relation extraction. We then map lexical relational phrases to formal ontological properties through a lemmatization-based schema alignment step. Entity typing and hierarchical induction are then employed to infer class structures, as well as domain and range constraints. Using RDFLib and structured data processing, we transform the extracted triples into both assertional (ABox) and terminological (TBox) axioms expressed in Description Logic. Experimental evaluation on benchmark datasets (Conll04 and NYT) demonstrates the efficacy of the approach, with expert validation showing high acceptance rates (>95%) and reasoners confirming zero inconsistencies. The pipeline thus establishes a reliable, scalable foundation for automated ontology learning, advancing the field from extraction to formally verifiable knowledge base construction.

Ontology learning has four main successive mining phases: term, concept, relation and axiom extraction. Since term extraction (TE) is the foundation of the ontology learning process, its inaccuracy will propagate in the following layers. Moreover, for any general-purpose ontology system some domain independent term extraction is needed. To achieve domain independent and precise TE in Farsi the present paper has presented a new hybrid system. It is based on the combination of a linguistic filter on Ezafe construction in Farsi and a statistical filter on C-value algorithm.

Balance deficits are present in a variety of clinical populations and can negatively impact quality of life. The integration of wearable sensors and machine learning technology (ML) provides unique opportunities to quantify biomechanical characteristics related to balance outside of a laboratory setting. This article provides a general overview of recent developments in using wearable sensors and ML to estimate or predict biomechanical characteristics such as center of pressure (CoP) and center of mass (CoM) motion. This systematic review was conducted according to PRISMA guidelines. Databases including Scopus, PubMed, CINHAL, Trip PRO, Cochrane, and Otseeker databases were searched for publications on the use of wearable sensors combined with ML to predict biomechanical characteristics. Fourteen publications met the inclusion criteria and were included in this review. From each publication, information on study characteristics, testing conditions, ML models applied, estimated biomechanical characteristics, and sensor positions were extracted. Additionally, the study type, level of evidence, and Downs and Black scale score were reported to evaluate methodological quality and bias. Most studies tested subjects during walking and utilized some type of neural network (NN) ML model to estimate biomechanical characteristics. Many of the studies focused on minimizing the necessary number of sensors and placed them on areas near or below the waist. Nearly all studies reporting RMSE and correlation coefficients had values <15% and >0.85, respectively, indicating strong ML model estimation accuracy. Overall, this review can help guide the future development of ML algorithms and wearable sensor technologies to estimate postural mechanics.

Smart health care depends on emerging technologies like electronic health records, along with internet of things, artificial intelligence, mobile communications, cloud computing, etc. This helps to create various systems like health management systems and medical service systems. This chapter includes: (1) development of smart health care using machine learning and blockchain technology; (2) uses of machine learning and blockchain technology with smart healthcare applications; (3) impact of smart healthcare applications on human health; (4) features of smart healthcare applications; and (5) limitations.

After the vision of the Semantic Web was broadcasted at the turn of the millennium, ontology became a synonym for the solution to many problems concerning the fact that computers do not understand human language: if there were an ontology and every document were marked up with it and we had agents that would understand the markup, then computers would finally be able to process our queries in a really sophisticated way. Some years later, the success of Google shows us that the vision has not come true, being hampered by the incredible amount of extra work required for the intellectual encoding of semantic mark-up – as compared to simply uploading an HTML page. To alleviate this acquisition bottleneck, the field of ontology learning has since emerged as an important sub-field of ontology engineering. It is widely accepted that ontologies can facilitate text understanding and automatic processing of textual resources. Moving from words to concepts not only mitigates data sparseness issues, but also promises appealing solutions to polysemy and homonymy by finding non-ambiguous concepts that may map to various realizations in – possibly ambiguous – words. Numerous applications using lexical-semantic databases like WordNet (Miller, 1990) and its non-English counterparts, e.g. EuroWordNet (Vossen, 1997) or CoreNet (Choi and Bae, 2004) demonstrate the utility of semantic resources for natural language processing. Learning semantic resources from text instead of manually creating them might be dangerous in terms of correctness, but has undeniable advantages: Creating resources for text processing from the texts to be processed will fit the semantic component neatly and directly to them, which will never be possible with general-purpose resources. Further, the cost per entry is greatly reduced, giving rise to much larger resources than an advocate of a manual approach could ever afford. On the other hand, none of the methods used today are good enough for creating semantic resources of any kind in a completely unsupervised fashion, albeit automatic methods can facilitate manual construction to a large extent. The term ontology is understood in a variety of ways and has been used in philosophy for many centuries. In contrast, the notion of ontology in the field of computer science is younger – but almost used as inconsistently, when it comes to the details of the definition. The intention of this essay is to give an overview of different methods that learn ontologies or ontology-like structures from unstructured text. Ontology learning from other sources, issues in description languages, ontology editors, ontology merging and ontology evolving transcend the scope of this article. Surveys on ontology learning from text and other sources can be found in Ding and Foo (2002) and Gomez-Perez

Soil, a crucial natural resource and the cornerstone of agriculture, profoundly impacts crop growth, quality, and yield. However, soil degradation affects over one-third of global land, with topsoil loss emerging as a significant form of this degradation, posing a grave threat to agricultural sustainability and socio-economic development. Therefore, accurate monitoring of topsoil-loss distribution is essential for formulating effective soil protection and management strategies. Traditional survey methods are limited by time-consuming and labor-intensive processes, high costs, and complex data processing. These limitations make it particularly challenging to meet the demands of large-scale research and efficient information processing. Therefore, it is imperative to develop a more efficient and accurate extraction method. This study focuses on the Heshan Farm in Heilongjiang Province, China, as the research subject and utilizes remote sensing technology and machine learning methods. It introduces multi-source data, including Sentinel-2 satellite imagery and Digital Elevation Model (DEM) data, to design four extraction schemes. (1) spectral feature extraction; (2) spectral feature + topographic feature extraction; (3) spectral feature + index extraction; (4) spectral feature + topographic feature + index extraction. Models for topsoil loss identification based on Random Forest (RF) and Support Vector Machine (SVM) algorithms are developed, and the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the models. The performance of the models is evaluated using overall accuracy and Kappa coefficient indicators. The results show that Scheme 4, which integrates spectral features, topographic features, and various indices, performs the best in extraction effects. The RF model demonstrates higher classification accuracy than the SVM model. The optimized PSO-RF and PSO-SVM models show significant improvements in extraction accuracy, especially the PSO-RF model, with an overall accuracy of 0.97 and a Kappa coefficient of 0.94. The PSO-RF model using Scheme 4 improves OA by 34.72% and Kappa by 38.81% compared to the RF model in Scheme 1. Topsoil loss has a significant negative impact on crop growth, severely restricting the normal growth and development of crops. This study provides an efficient technical means for monitoring soil degradation in black-soil regions and offers a scientific basis for formulating effective agricultural ecological protection strategies, thereby promoting the sustainable management of soil resources.