A Design Method with Knowledge Modeling for Complex Product Based on Ontology

Innovation design for complex products is a difficult issue in the military manufacturing industry. Ontology may provide a feasible way to rebuild the design process for complex products via sharing and reusing of design knowledge. In this paper, a design method used in the innovation design process of the complex products with knowledge modeling is proposed. Knowledge modeling is realized through ontology construction by combining the cycling evolutional theory of constructing ontology and OWL (Ontology Web Language)-based knowledge representation. As a case study, the satellite is selected as one of the complex products. The application domain of the satellite ontology is analyzed. According to the analysis result, the knowledge structure of satellite ontology is put forward based on OWL. The application of satellite product design shows that the method can effectively organize and reuse the knowledge resources in the innovation design of complex product and help companies to create more competitive products based on the existing knowledge and experience.

Product-design knowledge retrieval based on ontology

In order to improve the utilization ratio of knowledge retrieval, a product-design knowledge retrieval approach based on ontology is proposed. A representation model of product-design knowledge is proposed according to its characteristics. Domain ontology of product-design is estab- lished and the semantic annotation technology is used to connect the design knowledge and ontolo- gy. A new semantic annotation format is developed and semantic information of the design knowl- edge is enriched by making use of ontology. On that basis a retrieval algorithm is designed for semantic retrieval. Finally, this approach is used in a knowledge management system for military-vehi- cle design and its effectiveness and feasibility are validated. Results show that the recall ratio and the precision ratio of knowledge retrieval are improved greatly and users＇ requirements in semantic retrieval are satisfied.

Expression model for multiple relationships in the ontology of traditional Chinese medicine knowledge

Objective:To explore multiple relationships in traditional Chinese medicine(TCM)knowledge by comparing binary and multiple relationships during knowledge organization.Methods:Characteristics of binary and multiple semantic relationships as well as their associations are described.A method to classify multiple relationships based on the involvement of time is proposed and theoretically validated using examples from the ancient TCM classic Important Formulas Worth a Thousand Gold Pieces.The classification includes parallel multiple relationships,restricted multiple relationships,multiple relationships that involve time,and multiple relationships that involve time restriction.Next,construction of multiple semantic relationships for TCM concepts in each classification using Protege,an ontology editing tool is described.Results:Protege is superior to a binary relationship and less than ideal with multiple relationships during the constitution of concept relationships.Conclusion:When applied in TCM,the semantic relationships constructed by Protege are superior than those constructed by correlation and/or attribute relationships,but less ideal than those constructed by the human cognitive process.

Activity Theory Based Ontology Model for Efficient Knowledge Sharing in eHealth

Knowledge sharing has become an important issue that challenges the efficient healthcare delivery in eHealth system. It also rises as one of the most demanding applications with reference to dynamic interactivities among various healthcare actors (e.g. doctors, nurses, patients, relatives of patients). In this paper, we suggest an activity theory based ontology model to represent various healthcare actors. The goal of the suggested model is to enhance interactivities among these healthcare actors for conducting more efficient knowledge sharing, which helps to design eHealth system. To validate the feasibility of suggested ontology model, three typical use cases are further studied. A questionnaire based survey is carried out and the corresponding survey results are reported, together with the detailed discussions.

Generalized Testability Scheme Modeling of Materiel System Based on Information Ontology

A mode of ontology-based information integration and management( OIIM) for testability scheme was proposed through expatiating on the connotation of the system testability scheme.Aiming at the complexity of influencing factors in optimal design procedure of the testability scheme, the information of concept entities,concept attributions and concept relationships was analyzed and extracted,and then the testability scheme information ontology( TSIO) was built and coded via web ontology language( OWL).Based on the information ontology, the generalized model for testability scheme( GMTS) was founded by defining transformation rules. The primary study shows that the mode of OIIM for testability scheme can make up the deficiencies in knowledge representation and reasoning existing in traditional information models,and achieve the information share and reuse. It provides the effectual model basis for the optimal design of the testability scheme.

Knowledge Formalization about Perforated Stomach Ulcer on the Basis of Medical Ontology

The article defines knowledge about pelforated stomach ulcer that is formalized on the basis of an ontology model of medical diagnostics domain. The paper describes a base of observations for the disease and also knowledge base which determines a clinical presentation of the disease. The dependences on courses of the disease and process localization are taken into account during knowledge formalizing. The base of knowledge for the disease has the structure that is conventional for contemporary medicine. These knowledge will be used for building a medical intellectual system of for consulting and diagnostics.

Ontology Based Ocean Knowledge Representation for Semantic Information Retrieval

The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.

Constructing the Ontology for Modeling the Fish Production in Pearl River Basin

This paper puts forward a construction method based on ontology for the Pearl River Basin fish production, to facilitate the domain knowledge analysis and information retrieval. By converting the concepts and terms in domain ordinally, the fish production ontology was constructed with the definition of classes, properties, instances, and relationships. The developed ontology model of the fish production knowledge is proposed and applied in the system of fish diseases diagnosis primarily. The research lays the semantic foundation for the further efficient knowledge management and practical application.

Knowledge Fusion and Synchronization over Ubiquitous Ontology Mapping

With the rapid development and popularization of web services, the available information types and structure are becoming more and more complex and challenging. Actually web services involve the need for dynamic integration and transparent knowledge integration, in light of the urgent information changing track. Under this situation, the traditional search engine and information integration cannot finish this challenge, thereby bringing the opportunity for knowledge fusion and synchronization. This paper proposes a multi-matching strategy ontology mapping method for web information, i.e., ubiquitous ontology mapping method (U-Mapping), which can be viewed as a base collection of information on multiple ontologies made to appear anytime and everywhere. This approach is usually built independently by different information providers, avoiding the grammatical and semantic conflict. Finally, the ontology case information can be utilized under the consolidation of the U-Mapping, concerning language technology and machine learning methods.

Review on the Work of Agriculture Ontology Research Group

This paper introduces efforts and achievements of Agriculture Ontology Service Research Group of Agricultural Information Institute of Chinese Academy of Agriculture Sciences in last 10 years. It summarizes the research on ontology construction methodology, ontology management system, ontology application and etc.

Dictionary,Thesaurus or Ontology? Disentangling Our Choices in the Semantic Web Jungle

The Semantic Web seems finally close to maintaining its promise about a real world-wide graph of interconnected resources. The SPARQL query language and protocols and the Linked Open Data initiative have laid the way for endless data endpoints sparse around the globe. However, for the Semantic Web to really happen, it does not suffice to get billions of triples out there： these must be shareable, interlinked and conform to widely accepted vocabularies. While more and more data are converted from already available large knowledge repositories of companies and organizations, the question whether these should be carefully converted to semantically consistent ontology vocabularies or find other shallow representations for their content naturally arises. The danger is to come up with massive amounts of useless data, a boomerang which could result to be contradictory for the success of the web of data. In this paper, I provide some insights on common problems which may arise when porting huge amount of existing data or conceptual schemes （very common in the agriculture domain） to resource description framwork （RDF）, and will address different modeling choices, by discussing in particular the relationship between the two main modeling vocabularies offered by W3C： OWL and SKOS.

Developing Ontology Systems as a Base of an Environmental Quality Management Model in México

The process of identifying the attributes and relationships considered in an ontology is a complex task because there are many factors involved in the deterioration of environmental quality, the diversity of sources and data dispersion. This work presents an ontology that integrates the data required by an Environmental Quality Synoptic System (EQSS), which to date scatters in different Internet sites and concentrates by different agencies for example INEGI, CONABIO, SEMARNAT, CNA, among others. The methodology process consists of the collection of environmental information in Mexico through the application of computational techniques resulting ontology with environmental knowledge that will be processed by the system EQSS. Among the main advantages is than the selection and structure of information allow the automated generation of results in an environmental statement. The ontology proposal is based on knowledge of EQSS system that is based on the architecture of expert systems and through this important information for decision-making in regard to environmental quality and interaction with Geographic Information System (GIS) is obtained.

Social engineering in cybersecurity:a domain ontology and knowledge graph application examples

Social engineering has posed a serious threat to cyberspace security.To protect against social engineering attacks,a fundamental work is to know what constitutes social engineering.This paper first develops a domain ontology of social engineering in cybersecurity and conducts ontology evaluation by its knowledge graph application.The domain ontology defines 11 concepts of core entities that significantly constitute or affect social engineering domain,together with 22 kinds of relations describing how these entities related to each other.It provides a formal and explicit knowledge schema to understand,analyze,reuse and share domain knowledge of social engineering.Furthermore,this paper builds a knowledge graph based on 15 social engineering attack incidents and scenarios.7 knowledge graph application examples(in 6 analysis patterns)demonstrate that the ontology together with knowledge graph is useful to 1)understand and analyze social engineering attack scenario and incident,2)find the top ranked social engineering threat elements(e.g.the most exploited human vulnerabilities and most used attack mediums),3)find potential social engineering threats to victims,4)find potential targets for social engineering attackers,5)find potential attack paths from specific attacker to specific target,and 6)analyze the same origin attacks.

An ontology model to represent aquaponics 4.0 system’s knowledge

Aquaponics,one of the vertical farming methods,is a combination of aquaculture and hydroponics.To enhance the production capabilities of the aquaponics system and maxi-mize 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 inte-gration 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 rep-resent and store the essential knowledge of an aquaponics 4.0 system.This ontology pro-vides 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 termi-nologies 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.

Enhancing Domain Knowledge with Semantic Models of Web Documents

The paper considers the problem of semantic processing of web documents by designing an approach, which combines extracted semantic document model and domain- related knowledge base. The knowledge base is populated with learnt classification rules categorizing documents into topics. Classification provides for the reduction of the dimensio0ality of the document feature space. The semantic model of retrieved web documents is semantically labeled by querying domain ontology and processed with content-based classification method. The model obtained is mapped to the existing knowledge base by implementing inference algorithm. It enables models of the same semantic type to be recognized and integrated into the knowledge base. The approach provides for the domain knowledge integration and assists the extraction and modeling web documents semantics. Implementation results of the proposed approach are presented.

A Knowledge Management Framework in Software Requirements Engineering Based on the SECI Model

Software requirements engineering deals with: elicitation, specification, and validation of software requirements. Furthermore there is a need to facilitate collaboration amongst stakeholders and analysts. Fewer efforts were deployed to support them in performing their job on a day to day basis. To solve this problem we use knowledge management for software requirements engineering. This paper proposes a knowledge management framework, based on the SECI model of knowledge creation, aimed at exploiting tacit and explicit knowledge related to software requirements within a given software project. The core part of the proposed framework is a set of four sub systems “Socializer”;“Externalizer”;“Combiner”;and “Internalizer”, attached to a couple of domain ontologies and a set of knowledge assets. Indeed we aim to facilitate a semantic based interpretation of knowledge assets related to software requirements by restricting their interpretation through the application domain and software requirements ontologies. We anticipate that this framework would be very helpful for stakeholders as well as analysts to exchange and manage their knowledge within a given software project. We show in the case study, through a virtual payroll project using the two-step approach: domain level requirements plus design level requirements, how the key elicitation SRE techniques are used during the first phase of domain requirements elicitation through the four subsystems of our framework.

Dynamic knowledge graph approach for modelling the decarbonisation of power

This paper presents a dynamic knowledge graph approach that offers a reusable,interoperable,and extensible framework for modelling power systems.Domain ontologies have been developed to support a linked data representation of infrastructure data,socio-demographic data,areal attributes like demand,and models describing power systems.The knowledge graph links the data with a hierarchical representation of administrative regions,supporting geospatial queries to retrieve information about the population within the vicinity of a power plant,the number of power plants,total generation capacity,and demand within specific areas.Computational agents were developed to operate on the knowledge graph.The agents performed tasks including data uploading,updating,retrieval,processing,model construction and scenario analysis.A derived information framework was used to track the provenance of information calculated by agents involved in each scenario.The knowledge graph was populated with data describing the UK power system.Two alternative models of the transmission grid with different levels of structural resolution were instantiated,providing the foundation for the power system simulation and optimisation tasks performed by the agents.The application of the dynamic knowledge graph was demonstrated via a case study that investigates clean energy transition trajectories based on the deployment of Small Modular Reactors in the UK.

Knowledge Model for Electric Power Big Data Based on Ontology and Semantic Web

It is very important for the development of electric power big data technology to use the electric power knowledge.A new electric power knowledge theory model is proposed here to solve the problem of normalized modeled electric power knowledge for the management and analysis of electric power big data.Current modeling techniques of electric power knowledge are viewed as inadequate because of the complexity and variety of the relationships among electric power system data.Ontology theory and semantic web technologies used in electric power systems and in many other industry domains provide a new kind of knowledge modeling method.Based on this,this paper proposes the structure,elements,basic calculations and multidimensional reasoning method of the new knowledge model.A modeling example of the regulations defined in electric power system operation standard is demonstrated.Different forms of the model and related technologies are also introduced,including electric power system standard modeling,multi-type data management,unstructured data searching,knowledge display and data analysis based on semantic expansion and reduction.Research shows that the new model developed here is powerful and can adapt to various knowledge expression requirements of electric power big data.With the development of electric power big data technology,it is expected that the knowledge model will be improved and will be used in more applications.

A Practical Approach to Constructing a Geological Knowledge Graph:A Case Study of Mineral Exploration Data

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for reuse.Research of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new knowledge.Geological knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or processes.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.In this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling study.Based on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic relations.Finally,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph representation.The research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.