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Precision assembly process planning technology is one of the major challenges in the field of assembly. Compared with general assembly process planning, precision assembly process planning needs to consider a more complex assembly process, higher precision requirements, and more difficult process control. For this reason, it is of great significance to implement an intelligent assembly process planning and decision-making method for the development of precision assembly technology. This paper realizes the intelligent planning of precision assembly processes through the decision-making route of knowledge modeling and knowledge reasoning. Firstly, a theoretical system of a knowledge-driven precision assembly process decision-making framework is proposed. Secondly, a key precision assembly process decision-making technology is described in detail, which introduces a new approach for knowledge updates based on matching rules. Finally, the effectiveness of the knowledge update method is demonstrated through experimental cases. The knowledge-driven decision-making method combines the accuracy of domain experts' empirical knowledge with the efficiency of intelligent reasoning algorithms. The knowledge update method ensures the currency of knowledge used for decision making. Therefore, the proposed precision assembly decision-making system framework and knowledge updating techniques in this paper are of great significance for knowledge-driven precision assembly process planning. [ABSTRACT FROM AUTHOR]

The assembly process information of wind turbines is usually scattered in process documents consisting of multi-modal information, such as 3D models, natural texts, and images. Therefore, the cost of maintaining data and extracting process knowledge is high while the efficiency is low. To solve this problem, a multi-modal knowledge graph-based modeling method for the assembly process knowledge of wind turbines is proposed with multi-source heterogeneous data. First, the concepts in multi-modal process knowledge graph of wind turbine (MPKG-WT) are defined by analyzing the process characteristics of wind turbines to complete the construction of ontology. Then, based on the characteristics of multi-source heterogeneous data and multi-modal information, data analysis, knowledge extraction, and semantic similarity calculation are leveraged to realize the automatic instantiation of the graph. Finally, taking the process data of a wind turbine enterprise as an example, MPKG-WT is constructed and verified by implementing an auxiliary system for process design. The results show that MPKG-WT is more informative than the single-modal graph, and the data in different modals can complement each other, which leads to significant improvements in the efficiency of process design.

At present, the decision-making of coal mine roof disaster prevention and control measures and the analysis of accident causes mainly rely on manual experience, and the level of intelligence is relatively low. The knowledge graph of roof disaster prevention and control can integrate knowledge and experience of roof disaster prevention and control, assist in analyzing the causes of roof disaster accidents and making decisions on roof disaster prevention and control measures. A method for constructing a knowledge graph of coal mine roof disaster prevention and control has been proposed. The ontology method is used to complete the knowledge modeling of coal mine roof disaster prevention and control. The concepts in the field of roof disaster prevention and control are divided into mine geology, mining technology, prevention and control measures, and accident characterization. The relationships between concepts are defined as usage, triggering, susceptibility, control, prevention, and applicability. The knowledge modeling lays the foundation for the knowledge extraction of coal mine roof disaster prevention and control (entity extraction and relationship extraction). Based on the characteristics of entity overlapping between a large number of nested entities and relationships in the field of coal mine roof disaster prevention and control, a span based entity extraction method and a dependency syntax tree guided entity representation based relationship extraction method are determined. The method constructs a corpus in the field of roof disaster prevention and control, and uses the Neo4j graph database to store data, providing data source support for the application of knowledge graph of roof disaster prevention and control. The partial construction results of the knowledge graph of coal mine roof disaster prevention and control are displayed. It indicates that this knowledge graph can assist in the analysis of roof disaster accident causes and decision-making of prevention and control measures, thereby improving the intelligence level of roof management. It is pointed out that based on this knowledge graph, combined with natural language processing and knowledge reasoning technologies, knowledge Q&A on roof management can be achieved.

The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, their significance; scenarios of ethical import, and initial ethical guidelines. A focus area, potential case study, and lens through which to see the ethical significance at hand, is ontologies, with emphasis on the most abstract ontologies (so-called generic, foundational, top-level or upper ontologies). This would aim to show the ethical import of highly abstract (often philosophical) ontological choices, assumptions, and their effects on data, developers, users, and society. In particular, I stress the need for critical examination of such models, their content, their intended meanings, assumptions, ideologies, as well as the development process. Both the ethics of, and ethics in, these activities are, therefore, of interest. Topics in the ethics of AI that are relevant include: transparency, bias, data-sharing, privacy, control over data, etc. This paper thereby contributes to the ethics of AI, as well as the ethics of information, of knowledge managment and relevant topics by developing and discussing the ethics of ontology (and similar systems). Future development will refine and add to the ideas expressed in this paper. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a result of alkali ASP flooding in oil and gas fields, strata and pipelines become seriously scaled, which poses a threat to the normal operation of crude oil production. We propose an intelligent knowledge reasoning model for dynamic scaling prediction in order to address the problems of high directivity, poor generalization ability, and poor application effect of existing scaling prediction methods. The model framework includes the knowledge acquisition layer which mainly relates to the manual acquisition of scaling prediction knowledge and the intelligent training of the knowledge base, and it includes the knowledge modeling layer that provides a set of standard domain common ontology and knowledge organization system using the ontology modeling technology, it also includes the knowledge inference layer which is the application layer of the model. The three layers collaborate and finally complete the scaling prediction through inference and expression. A total of 238 wells were selected for experimentation in the northern development area of the Xingshugang Oilfield. Experimental results indicate that the model has the highest accuracy of 91.87%. Additionally, the time series prediction trend for the six ions matches the trend of change in ion concentration in the scaling state, verifying the accuracy of the model's predictions. [ABSTRACT FROM AUTHOR]

Current network representation learning models mainly use matrix factorization-based and neural network-based approaches, and most models still focus only on local neighbor features of nodes. Knowledge representation learning aims to learn low-dimensional dense representations of entities and relations from structured knowledge graphs, and most models use the triplets to capture semantic, logical, and topological features between entities and relations. In order to extend the generalization capability of the network representation learning models, this paper proposes a network representation learning algorithm based on multiple remodeling of node attributes named MRNR. The model constructs the knowledge triplets through the textual association relationships between nodes. Meanwhile, a novel co-occurrence word training method has been proposed. Multiple remodeling of node attributes can significantly improve the effectiveness of network representation learning. At the same time, MRNR introduces the attention mechanism to achieve the weight information for key co-occurrence words and triplets, which further models the semantic and topological features between entities and relations, and it makes the network embedding more accurate and has better generalization ability. [ABSTRACT FROM AUTHOR]

Copyright of Lexicographica is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aquaponics, one of the vertical farming methods, is a combination of aquaculture and hydroponics. To enhance the production capabilities of the aquaponics system and maximize crop yield on a commercial level, integration of Industry 4.0 technologies is needed. Industry 4.0 is a strategic initiative characterized by the fusion of emerging technologies such as big data and analytics, internet of things, robotics, cloud computing, and artificial intelligence. The realization of aquaponics 4.0, however, requires an efficient flow and integration of data due to the presence of complex biological processes. A key challenge in this essence is to deal with the semantic heterogeneity of multiple data resources. An ontology that is regarded as one of the normative tools solves the semantic interoperation problem by describing, extracting, and sharing the domains’ knowledge. In the field of agriculture, several ontologies are developed for the soil-based farming methods, but so far, no attempt has been made to represent the knowledge of the aquaponics 4.0 system in the form of an ontology model. Therefore, this study proposes a unified ontology model, AquaONT, to represent and store the essential knowledge of an aquaponics 4.0 system. This ontology provides a mechanism for sharing and reusing the aquaponics 4.0 system’s knowledge to solve the semantic interoperation problem. AquaONT is built from indoor vertical farming terminologies and is validated and implemented by considering experimental test cases related to environmental parameters, design configuration, and product quality. The proposed ontology model will help vertical farm practitioners with more transparent decision-making regarding crop production, product quality, and facility layout of the aquaponics farm. For future work, a decision support system will be developed using this ontology model and artificial intelligence techniques for autonomous data-driven decisions.

Improving surgical training by means of technology assistance is an important challenge that aims to directly impact surgical quality. Surgical training includes the acquisition of two categories of knowledge: declarative knowledge (i.e. ‘knowing what’) and procedural knowledge (i.e. ‘knowing how’). It is essential to acquire both before performing any particular surgery. There are currently many tools for acquiring procedural knowledge, such as simulators. However, few approaches or tools allow a trainer to formalize and record surgical declarative knowledge, and a trainee to have easy access to it. In this paper, we propose an approach for structuring surgical declarative knowledge according to procedural knowledge and based on surgical process modeling. A dedicated software application has been implemented. We evaluated the concept and the software usability on two procedures with different medical populations: endoscopic third ventriculostomy involving 6 neurosurgeons and preparation of a surgical table for craniotomy involving 4 scrub nurses. The results of both studies show that surgical process models could be a well-adapted approach for structuring and visualizing surgical declarative knowledge. The software application was perceived by neurosurgeons and scrub nurses as an innovative tool for managing and presenting surgical knowledge. The preliminary results show that the feasibility of the proposed approach and the acceptability and usability of the corresponding software. Future experiments will study impact of such an approach on knowledge acquisition.

The long-term maintenance of good condition for equipment is the basis of carrying out combat missions under the high technology and fast pace of modern war. However, the knowledge in the health management field at present has the characteristics of distribution, multi-source, heterogeneity and uncertainty, which seriously affects the efficiency of knowledge sharing and reuse. In order to improve the utilization of health management knowledge, an ontology-based knowledge representation method is proposed to describe knowledge in a unified and standardized way, and the classical ontology is extended to express the uncertain knowledge in the field of health management. In addition, to improve the maintenance and knowledge updating efficiency, a global ontology model and a hierarchy, time and activity (HTA) ontology model are constructed. This paper takes the guidance subsystem of a missile as an example to illustrate the process of knowledge modeling. The results show this method realizes knowledge sharing in the health management field and can provide decision support for health management of equipment. [ABSTRACT FROM AUTHOR]

The National Science Foundation’s Arctic Data Center is the primary data repository for NSF-funded research conducted in the Arctic. There are major challenges in discovering and interpreting resources in a repository containing data as heterogeneous and interdisciplinary as those in the Arctic Data Center. This paper reports on advances in cyberinfrastructure at the Arctic Data Center that help address these issues by leveraging semantic technologies that enhance the repository’s adherence to the FAIR data principles and improve the Findability, Accessibility, Interoperability, and Reusability of digital resources in the repository. We describe the Arctic Data Center’s improvements. We use semantic annotation to bind metadata about Arctic data sets with concepts in web-accessible ontologies. The Arctic Data Center’s implementation of a semantic annotation mechanism is accompanied by the development of an extended search interface that increases the findability of data by allowing users to search for specific, broader, and narrower meanings of measurement descriptions, as well as through their potential synonyms. Based on research carried out by the DataONE project, we evaluated the potential impact of this approach, regarding the accessibility, interoperability, and reusability of measurement data. Arctic research often benefits from having additional data, typically from multiple, heterogeneous sources, that complement and extend the bases – spatially, temporally, or thematically – for understanding Arctic phenomena. These relevant data resources must be ‘found’, and ‘harmonized’ prior to integration and analysis. The findings of a case study indicated that the semantic annotation of measurement data enhances the capabilities of researchers to accomplish these tasks.

Community portraits can deeply explore the characteristics of community structures and describe the personalized knowledge needs of community users, which is of great practical significance for improving community recommendation services, as well as the accuracy of resource push. The current community portraits generally have the problems of weak perception of interest characteristics and low degree of integration of topic information. To resolve this problem, the reader community portrait method based on the thematic and timeliness characteristics of interest labels (UIT) is proposed. First, community opinion leaders are identified based on multi-feature calculations, and then the topic features of their texts are identified based on the LDA topic model. On this basis, a semantic mapping including "reader community-opinion leader-text content" was established. Second, the readers' interest similarity of the labels was dynamically updated, and two kinds of tag parameters were integrated, namely, the intensity of interest labels and the stability of interest labels. Finally, the similarity distance between the opinion leader and the topic of interest was calculated to obtain the dynamic interest set of the opinion leaders. Experimental analysis was conducted on real data from the Douban reading community. The experimental results show that the UIT has the highest average F value (0.551) compared to the state-of-the-art approaches, which indicates that the UIT has better performance in the smooth time dimension. [ABSTRACT FROM AUTHOR]

Improving surgical training by means of technology assistance is an important challenge that aims to directly impact surgical quality. Surgical training includes the acquisition of two categories of knowledge: declarative knowledge (i.e. 'knowing what') and procedural knowledge (i.e. 'knowing how'). It is essential to acquire both before performing any particular surgery. There are currently many tools for acquiring procedural knowledge, such as simulators. However, few approaches or tools allow a trainer to formalize and record surgical declarative knowledge, and a trainee to have easy access to it. In this paper, we propose an approach for structuring surgical declarative knowledge according to procedural knowledge and based on surgical process modeling. A dedicated software application has been implemented. We evaluated the concept and the software usability on two procedures with different medical populations: endoscopic third ventriculostomy involving 6 neurosurgeons and preparation of a surgical table for craniotomy involving 4 scrub nurses. The results of both studies show that surgical process models could be a well-adapted approach for structuring and visualizing surgical declarative knowledge. The software application was perceived by neurosurgeons and scrub nurses as an innovative tool for managing and presenting surgical knowledge. The preliminary results show that the feasibility of the proposed approach and the acceptability and usability of the corresponding software. Future experiments will study impact of such an approach on knowledge acquisition. [ABSTRACT FROM AUTHOR]

Current network representation learning models mainly use matrix factorization-based and neural network-based approaches, and most models still focus only on local neighbor features of nodes. Knowledge representation learning aims to learn low-dimensional dense representations of entities and relations from structured knowledge graphs, and most models use the triplets to capture semantic, logical, and topological features between entities and relations. In order to extend the generalization capability of the network representation learning models, this paper proposes a network representation learning algorithm based on multiple remodeling of node attributes named MRNR. The model constructs the knowledge triplets through the textual association relationships between nodes. Meanwhile, a novel co-occurrence word training method has been proposed. Multiple remodeling of node attributes can significantly improve the effectiveness of network representation learning. At the same time, MRNR introduces the attention mechanism to achieve the weight information for key co-occurrence words and triplets, which further models the semantic and topological features between entities and relations, and it makes the network embedding more accurate and has better generalization ability.

Operational knowledge is one of the most valuable assets in a company, as it provides a strategic advantage over competitors and ensures steady and optimal operation in machines. An (interactive) assessment system on the shop floor can optimize the process and reduce stopovers because it can provide constant valuable information regarding the machine condition to the operators. However, formalizing operational (tacit) knowledge to explicit knowledge is not an easy task. This transformation considers modeling expert knowledge, quantification of knowledge uncertainty, and validation of the acquired knowledge. This study proposes a novel approach for production assessment using a knowledge transfer framework and evidence theory to address the aforementioned challenges. The main contribution of this paper is a methodology for the formalization of tacit knowledge based on an extended failure mode and effect analysis for knowledge extraction, as well as the use of evidence theory for the uncertainty definition of knowledge. Moreover, this approach uses primitive recursive functions for knowledge modeling and proposes a validation strategy of the knowledge using machine data. These elements are integrated into an interactive recommendation system hosted on a backend that uses HoloLens as a visual interface. We demonstrate this approach using an industrial setup: a laboratory bulk good system. The results yield interesting insights, including the knowledge validation, uncertainty behavior of knowledge, and interactive troubleshooting for the machine operator.

The use of external libraries in today's software projects allows developers to take advantage of features provided by such application programming interfaces (APIs) without having to reinvent the wheel. However, APIs have also introduced new challenges to the software engineering community (e.g. API incompatibilities, software vulnerabilities, and license violations) that extend beyond traditional project boundaries and often involve different software artifacts. One potential solution to these challenges is to provide a technology-independent representation of software dependency semantics and its integration with knowledge from other software artifacts. In our research, we take advantage of the semantic web (SW) and its technology stack to establish a unified knowledge representation of build and dependency repositories. Given this knowledge base, we can now extend and integrate other (heterogeneous) resources to allow for a flexible and comprehensive global impact analysis approach. To illustrate the applicability of our SW-enabled modeling approach, we discuss two different applications. These applications illustrate how our modeling approach can not only integrate and reuse knowledge from dependency management systems and other software artifacts, but also take advantage of inference services provided by the SW to support novel software analytics services across artifact and project boundaries. [ABSTRACT FROM AUTHOR]

In the clothing industry, garment pattern design serves as a significant middle link between fashion design and manufacturing. With the advent of advanced multimedia techniques, like virtual reality, 3D modeling, and interactive design, this work has become more intuitive. However, it is still a tremendous knowledge-based work that relied on the experienced patternmakers' know-how. For enterprises, it will take a long time to cultivate a patternmaker from an abecedarian to an expert. Moreover, while facing fierce competition in the market, enterprises still have to endure the pressures and risks led by the turnover of experienced patternmakers. In this context, we put forward a knowledge-supported garment pattern design approach by learning the experienced patternmakers' knowledge based on fuzzy logic and artificial neural networks. Based on this approach, we created a knowledge-supported pattern design model, consisting of several sub-models following the garment styles, considering the properties of fabrics and fitting degree. The inputs of the model are the feature body dimensions, while the outputs, namely the pattern parameters, can be generated following the fabric and fitting degree selected. Through performance comparison with other models and the actual fitting test, the results revealed that the present approach was applicable. Our proposed approach can not only support the non-expert patternmakers or abecedarians to make decisions when developing the patterns by reducing the difficulties of patternmaking but help the enterprises to reduce the dependencies on the experts, hence promoting the efficiency and reducing risks. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of dispersed accident-related information on construction sites, which hinders consensus among employers, workers, supervisors, and society. A robust NLP-based framework is presented to analyze and structure accident-related textual data into a comprehensive knowledge base that reveals accident patterns and risk information. Accident scenarios, including frequency and severity scores, are structured into a graph database through knowledge modeling, establishing an ontology to elucidate keyword relationships. Network analysis identifies accident patterns, quantifies scenario likelihood and severity, and predicts criticality, forming an accident hazard ontology. This vectorized ontology supports accident tracking, prediction, and learning with potential applications. The framework ensures reliable data integration, real-time hazard assessment, and proactive safety measures. • Propose a groundbreaking intelligent ontology to evaluate accident risks on construction sites, setting a new standard. • Use NLP to analyze accident-related data systematically, identifying patterns and attributes linked to accidents. • Capture accident probability, severity, and comprehensive risk assessments, consolidating them into a graph database. • Convert accident information into a graphical format to improve decision-making and risk management in construction. • Establish a human- and machine-readable knowledge system for better safety management and decision support in construction. [ABSTRACT FROM AUTHOR]

Using big data management system to manage coal mine equipment maintenance information lacks the ability to express coal mine equipment maintenance knowledge, has not formed a relatively complete coal mine equipment maintenance knowledge management system, and cannot realize knowledge mining and inter-knowledge relationship links, resulting in a large number of information with in-depthmining value cannot be usedeffectively.In order to solve the above problems, a coal mine equipment maintenance knowledge graph is constructed. Firstly, ontology-based knowledge modeling is carried out by defining key concepts, relationships and attributes of coal mine equipment maintenance. Secondly, obtain knowledge from structured, semi-structured and unstructured data sources, and coal mine equipment maintenance knowledge extraction is completed through named entity identification, relationship extraction and event extraction.Finally, the graph databaseNeo4jis used to store coal mine equipment maintenance knowledge and form a coal mine equipment maintenance knowledge graph.The application of coal mine equipment maintenance knowledge graph in intelligent semantic search, intelligent question and answer and visualization decision support can improve the efficiency of coal mine equipment maintenance knowledge management and provide strong support for the realization of intelligent dynamic management of coal mine equipment.

Railway alignment optimization is a complex process in which human knowledge and experience are extensively used. However, the related knowledge is usually unstructured, which is difficult for computers to recognize. Moreover, related knowledge is applied in fragmented ways in existing alignment optimization methods, which are thus difficult to update with actual advancements of human experience and knowledge. To solve the above problems, the first-known knowledge graph modeling method for railway alignment optimization is proposed in this paper. First, a hierarchical and categorized semantic network modeling approach for railway alignment design knowledge is devised. Based on this, a railway alignment design knowledge graph (RAD-KG) is constructed. Then, a rapid knowledge retrieval method is proposed for improving the querying efficiency from the RAD-KG during alignment optimization. Finally, the RAD-KG integrating multiple alignment design principles is successfully applied to a real-world case. It is verified that the alignment generated by the proposed method reduces costs by 9.2% compared with conventional manual work by experienced engineers. Moreover, the RAD-KG-assisted method can rapidly update alignment design guidelines during optimization and, hence, produce several alignment alternatives satisfying various complicated requirements, which confirms the flexibility of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Concept maps are useful in many disciplines for the representation and communication of structured knowledge. This article contains a description of concept mapping and knowledge modeling based upon concept maps that are used for a variety of purposes. It describes applications of concept mapping and knowledge modeling for education, for knowledge preservation and sharing, for knowledge creation, as an efficient means of creating documentation, and for the creation of knowledge formalisms from informal knowledge representations. Examples are drawn from several different knowledge domains.

Like chemistry's table of elements, the knowledge spectrum organizes a large amount of information about knowledge, dividing intelligent behavior into its essential cognitive elements. This paper describes a way to catalog, measure, and analyze organization knowledge requirements and knowledge resources through inheritance of knowledge spectrum properties. Business process knowledge requirements trees and employee knowledge profiles would be created to permit knowledge requirements fulfillment analyses. [ABSTRACT FROM AUTHOR]

Ontology in software development is explained as presenting the properties of things within a domain knowledge and how they are interrelated to each other by defining a set of notions and taxonomies that exemplify the knowledge. It is used to determine the ambiguity in the software requirements specification. Though averred to be useful, the software engineering communities are still unfamiliar with the role of Ontology in modeling knowledge in software development processes. Moreover, not much has been known about the role of Ontology in software engineering processes. The objective is to map and explain the substantiation about the role of Ontology in Modelling Knowledge and the challenge faced by the software engineering team to understand how far ontology can help them determine the ambiguity in modeling and software development processes. We have carried out a methodical review of the literature issued between 2012 and 2021 and recognized 150 publications that talk over the role of ontology in modeling knowledge in software development processes. This study conveyed and employed particular inclusion and exclusion criteria in bi-rounds to establish the utmost pertinent publications for our research objective. The review acknowledged 22 applications that explain ontologies' primary role in software development processes. However, our findings suggest ontology's role in software engineering as a investigation background requires extra consideration. A further experimental result I needed to better understand the role of ontology in modeling knowledge in software development with quality requirements as well as self-organizing groups. [ABSTRACT FROM AUTHOR]

Emergency response is an important task in geologic hazard emergency management in China. Rapid emergency response actions can effectively reduce the losses caused by geologic hazards. Emergency plans and emergency command systems play an important role in emergency response. The emergency response still comes with challenges, such as the difficulty of integrating emergency plans with real-time geologic hazard information and the operability of emergency disposal in emergency plans. One possible approach to solving this problem is to model and represent knowledge and to enable the emergency command system to acquire and intelligently provide the required emergency response knowledge. In this paper, we propose a conceptual model of knowledge for geologic hazard emergency response (GHER). The geospatial characteristics of GHER knowledge are represented by the geo-ontology in the modeling process. The ontology enables semantic interoperability of GHER knowledge. A case study is used as an application test to evaluate the framework of GHER knowledge acquisition. In the framework, the GHER knowledge model enables the emergency command system to provide imperative knowledge intelligently for emergency decision-making; knowledge inference enables the emergency command system to provide important implicit knowledge for emergency response; and spatial analysis of geographic information system is applied to visualize and analyze spatial data in the process of emergency response. [ABSTRACT FROM AUTHOR]