Thesaurus-based approach for building domain ontology with a case study of military aircraft prototype ontology construction

To alleviate the amount of work involved in constructing a domain ontology, starting with the base of an existing terminological-rich thesaurus is better than starting from scratch. With a case study of reengineering the Defense Science and Technology Thesaurus into a prototype military aircraft ontology, a four-phase thesaurus-based methodology is introduced and investigated, which consists of identifying the application purpose, overall design, designing in detail and evaluation. Designing in detail is the core step, converting the terms and semantic relationships of the thesaurus into an ontology and supplementing richer semantic relationships. The resulting prototype ontology includes 87 concepts and 34 relationships, and can be extended and scaled up to a full-fledged domain ontology in the future. Eight universal genres of relationships of this ontology are preliminarily summarized and analyzed, including equivalent relationships, approximate relationships, generic/abstract relationships, part/whole relationships, cause/effect relationships, entity/location relationships etc., and the normalization of semantic relationships is critical to the merging and reusing of follow-up multiple ontologies.

Heuristic web service composition method based on domain ontology

In order to realize automatic web service composition, a heuristic web service composition method based on domain ontology is proposed. First, this method integrates the domain ontology and the artificial intelligence （AI） planning algorithm. Then, it uses the domain ontology and its reasoning capability to infer the semantic relationship among parameters. Finally, it transforms the web service composition problem into the planning problem based on the AI planning heuristic algorithm. The preliminary experimental results show that the above method compensates for the lack of semantics in the previous AI planning method and it can satisfy the requirements of quality and efficiency of composition, thus generating composite web services according to customer requirements automatically and efficiently.

Traditional Chinese Medicine(TCM)Domain Ontology:Current Status and Rethinking for the Future Development

The past twenty years have seen the increasingly important role of ontology in traditional Chinese medicine(TCM).However,the development of TCM ontology faces many challenges.Since the epistemologies dramatically differ between TCM and contemporary biomedicine,it is hard to apply the existing top-level ontology mechanically.“Data silos”are widely present in the currently available terminology standards,term sets,and ontologies.The formal representation of ontology needs to be further improved in TCM.Therefore,we propose a unified basic semantic framework of TCM based on in-depth theoretical research on the existing top・level ontology and a re-study of important concepts in TCM.Under such a framework,ontologies in TCM subdomains should be built collaboratively and be represented formally in a common format.Besides,extensive cooperation should be encouraged by establishing ontology research communities to promote ontology peer review and reuse.

Domain ontology driven composable simulation framework

The simulation composability is one of important development directions in simulation domain. The key issue is the semantic composability. Currently there is no feasible approach to realize the effective semantic representation and composition of simulation components. Based on domain knowledge and Web Ontology Language (OWL), this paper proposes a composable simulation framework, which includes conceptual model semantics, model components semantics, model framework semantics, and simulation scenario semantics. Additionally, all the semantics are utilized in the model components development process, the simulation system development process, and the simulation system execution process respectively. The consistency checking among those semantics is also proposed. The detailed mapping processes between different semantic models can help to build the domain ontology driven composable simulation system.

A comprehensive construction of the domain ontology for stratigraphy

Stratigraphic knowledge,the cornerstone of geoscience,needs to be represented by the Knowledge Graph based upon ontology,in order to apply the state-of-the-art big-data techniques.This study aims to comprehensively construct the ontologies for the stratigraphic domain.This has been achieved by a federated,crowd intelligence-based collaboration among domain experts of major stratigraphic subdisciplines.The initial step is to enumerate key terms from authoritative references and incorporate them into the Geoscience Professional Knowledge Graphs(GPKGs)of Deep-time Digital Earth Project.During this process,semantic heterogeneities were meticulously addressed by professional judgement aided by an automatic detection of Homonyms at the GPKGs platform.Afterwards,these terms were further differentiated as either classes or properties and arranged in a hierarchical framework in a top-down process.Consequently,seven ontologies are constructed for major stratigraphic branches,i.e.,Lithostratigraphy,Biostratigraphy,Chronostratigraphy,Chemostratigraphy,Magnetostratigraphy,Cyclostratigraphy and Sequence Stratigraphy.The ontology of Biostratigraphy,among them,is elaborated here,as no biostratigraphic ontology has been attempted before to our knowledge.The constructed biostratigraphic ontology comprises following major root classes:Fossil,Biostratigraphic unit,Biostratigraphic horizon.Altogether,they contribute to the eventual dating and correlating of strata in another root class:Biostratigraphic correlation.In summary,the achievements of this study are probably heretofore the most comprehensive ontologies for the stratigraphic domain.Moreover,a proto model of semantic search engine was conceived to discuss potential application of our work for better querying stratigraphic references,utilizing the semantic liaison of the classes in the constructed ontologies.

Domain ontology development of knowledge base in cardiovascular personalized health management

In China,cardiovascular disease has become the leading killer in recent years,and mortality from cardiovascular disease is continuing to rapidly increase.Extant medical research has proven that personal health management(prevention,intervention,and recuperation)of chronic diseases,such as cardiovascular diseases,is the best strategy for their prevention and treatment.Currently,the public can obtain health management knowledge through the Internet,newspapers,books,and other channels.However,with the explosive growth of available information,the public is limited to obtain effective health management guidance due to the characteristics of multiple sources,uneven accuracy(even some contradictory knowledge)and a major paucity of personalization,especially for the general public who lack professional medical knowledge.To address these problems,this paper proposes a knowledge base framework(i.e.domain ontology library)of health management programs based on the cardiovascular disease domain,which can standardize knowledge of health management programs both logically and structurally.In order to satisfy the needs of personalized health management,the core ontology of the domain ontology library is health-management-program ontology.In addition to common ontologies(e.g.disease ontology,drug ontology,etc.),basic ontologies include the ontology of individual health characteristics(e.g.individual-healthcharacteristics and environmental-characteristics ontology),and ontologies comprising diet and sport(e.g.ingredients,recipes,physical exercise,etc.).We then construct the ontology library through the professional ontology tool,Protégé.With a case study,we translate a piece of text health management knowledge into instances of an ontology library.At the same time,we present a personalized health management program recommendation algorithm based on the ontology library,and a recommendation case is realized according to this algorithm.As a basic research,the results of this paper can also support other health management applications in the future.

DOF:a generic approach of domain ontology fuzzification

Although recent studies on the Semantic Web have focused on crisp ontologies and knowledge representation,they have paid less attention to imprecise knowledge.However,the results of these studies constitute a Semantic Web that can answer requests almost perfectly with respect to precision.Nevertheless,they ensure low recall.As such,we propose in this re-search work a new generic approach of fuzzification that which allows a semantic representation of crisp and fuzzy data in a domain ontology.In the framework of our real case study,the obtained illustrate that our approach is highly better than the crisp one in terms of completeness,comprehensiveness,generality,comprehension and shareability.

An Approach towards Goal-Oriented Requirements Ontology: Consistency and Completeness Based Requirements Analysis

The paper presents a new approach to managing software requirement elicitation techniques with a high level of analyses based on domain ontology techniques, where we established a mapping between user scenario, structured requirement, and domain ontology techniques to improve many attributes such as requirement consistency, completeness and eliminating duplicate requirements to reduce risk of overrun time and budgets. One of the main targets of requirement engineering is to develop a requirement document with high quality. So, we proposed a user interface to collect all vital information about the project directly from the regular user and requirement engineering;After that, the proposal will generate an ontology based on semantic relations and rules. Requirements Engineering tries to keep requirements throughout a project’s life cycle consistent necessities clear, and up to date. This prototype allows mapping requirement scenarios into ontology elements for semantically interrupted. The general points of our prototype are to guarantee the identification requirements and improved nature of the Software Requirements Specification (SRS) by solving incomplete and conflicting information in the requirements specification.

Method and supporting framework for business domain-oriented web service discovery

This paper proposes a new business domain-oriented web service discovery method and framework to solve the low precision results caused by UDDI （universal description,discovery and integration） syntactic discovery and the difficulty in selecting from among functionally equivalent web services.When requesting services,service clusters are extracted from concrete services in terms of functional requests; then, through business information properties consultation,the most suitable services are determined and finally bound to user requests.The whole process is transparent to users. This framework is also tested and supported through a prototype based on a travel domain,IPVita （intelligent platform of virtual travel agency）.

Efficient semantic web service matching using common ontology

To solve the bottleneck problem in centralized service discovery methods,a novel architecture based on domain ontology for semantic service discovery is proposed.This distributed architecture can adjust the domain partition and allocate system resources automatically.The characteristics of this mechanism are analyzed,including scalability,self-organization and adaptability.In this mechanism,semantic web service discovery is separated into two parts.First,under balance tree topology,registry proxy can rapidly forward requests to the objective registry center,and avoid the bottleneck problem.Secondly,a semantic distance based service matching algorithm is proposed to promote the effect of service searching.The results of simulation experiments show that the proposed mechanism can serve as a scalable solution for semantic web service publication and discovery.And the improved matching algorithm has higher recall and precision than other algorithms.

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.

Geometry Services Construction Based on Ontology towards Cooperative Design in Cloud Environment

APIs of CAD system could be encapsulated to construct web services so as to provide standard access interfaces for web-based cooperative design. However, lack of semantic supporting make integration of heterogeneous modelling system limited to operational level. On the purpose of carrying out cooperative design in a flexible cloud environment, an intelligent and effective support mechanism is needed for mass and complex interaction in cooperative work. Therefore, Ontology-based Geometry Modeling Services Environment （OGMSE） is provided to realize a knowledge-level geometry modeling so as to supported semantic 3D model management and intelligent operation support. First, CAD APIs are encapsulated into web service for web invocation. Then, ontology contained structural semantic and operational semantic is built for management of models and modeling process. Lastly, conception-driven modeling mechanism is built to invocate service according to interaction requirement, thus to realize conception-level cooperative design. Based on an open geometry engine, a prototype system has been developed. The result shows that the approach provides a reference direction for large-scale application on the network.

Retrieving reuse component based on semantic

According to the current research status of component retrieval, the component description model based on facet classification is improved by adding semantic features. Furthermore, the component retrieval process model is put forward by combining the domain ontology with the relative concept match algorithm. A detailed illustration of a component reasoning engine and a component classification engine is given and the component classification algorithm is provided by using the Naive Bayes algorithm based on domain ontology. The experimental results show that the recall ratio and the precision ratio are obviously improved by using the method based on semantics, and demonstrate the feasibility and effectiveness of the proposed method.

Semantic Complex Event Detection System of Express Delivery Business with Data Support From Multidimensional Space

The express delivery induslry of China is relatively backward in the automation degree of critical business processes. The basic reason is that the business-related supporting data, which is scattered in the multidimensional space, is difficult to utilize and process. This paper proposes an automatic data acquisition fi-amework to resolve such difficulty, which synthetically utilize intelligent inemet of things （IoT）, semantic web and complext event processing （CEP） technology. We also implement a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages. The simulation results show that the system has good performance and feasible enough to deal with the complex business which need data support fTom multidimensional space.

Construction of a fluvial facies knowledge graph and its application in sedimentary facies identification

Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and compilation of sedimentary facies.Traditional sedimentary facies analysis is a labor-intensive task with the added complexity of using unstructured knowledge and unstandardized terminology.Therefore,it is very difficult for beginners or non-geology scholars who lack a systematic knowledge and experience in sedimentary facies analysis.These hurdles could be partly alleviated by having a standardized,structured,and systematic knowledge base coupled with an efficient automatic machine-assisted sedimentary facies identification system.To this end,this study constructed a knowledge system for fluvial facies and carried out knowledge representation.Components include a domain knowledge graph for types of fluvial facies(meandering,braided and other fluvial depositional environments)and their characteristic features(bedforms,grain size distribution,etc.)with visualization,a method for query and retrieval on a graph database platform,a hierarchical knowledge tree-structure,a data-mining clustering algorithm for machineanalysis of publication texts,and an algorithm model for this area of sedimentary facies reasoning.The underlying sedimentary facies identification and knowledge reasoning system is based on expert experience and synthesis of publications.For testing,17 sets literature publications data that included details of sedimentary facies data(bedforms,grain sizes,etc.)were submitted to the artificial intelligence model,then compared and validated.This testing set of automated reasoning results yielded an interpretation accuracy of about 90%relative to the published interpretations in those papers.Therefore,the model and algorithm provide an efficient and automated reasoning technology,which provides a new approach and route for the rapid and intelligent identification of other types of sedimentary facies from literature data or direct use in the field.

Video structural description technology for the new generation video surveillance systems

The increasing need of video based applications issues the importance of parsing and organizing the content in videos. However, the accurate understanding and manag- ing video contents at the semantic level is still insufficient. The semantic gap between low level features and high level semantics cannot be bridged by manual or semi-automatic methods. In this paper, a semantic based model named video structural description （VSD） for representing and organizing the content in videos is proposed. Video structural descrip- tion aims at parsing video content into the text information, which uses spatiotemporal segmentation, feature selection, object recognition, and semantic web technology. The pro- posed model uses the predefined ontologies including con- cepts and their semantic relations to represent the contents in videos. The defined ontologies can be used to retrieve and organize videos unambiguously. In addition, besides the de- fined ontologies, the semantic relations between the videos are mined. The video resources are linked and organized by their related semantic relations.

Personalized course generation and evolution based on genetic algorithms

Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance.

Exogenous approach to improve topic segmentation

Purpose-Topic segmentation is one of the active research fields in natural language processing.Also,many topic segmenters have been proposed.However,the current challenge of researchers is the improvement of these segmenters by using external resources.Therefore,the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation.Design/methodology/approach-New topic segmenters(TSS-Onto and TSB-Onto)are proposed based on the two well-known segmenters C99 and TextTiling.The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource.Subsequently,an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works.Findings-Based on this study,the authors showed that adding semantic knowledge,which is extracted from a domain ontology,improves the quality of topic segmentation.Moreover,TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation.Research limitations/implications-The main limitation of this study is the used test corpus for the evaluation which is not a benchmark.However,we used a collection of scientific papers from well-known digital libraries(ArXiv and ACM).Practical implications-The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing.Originality/value-The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge.This knowledge is extracted from an ontological external resource.

A Semantic Approach to Workflow Management and Reuse for Research Problem Solving

The investigation proposes the application of an ontological semantic approach to describing workflow control patterns,research workflow step patterns,and the meaning of the workflows in terms of domain knowledge.The approach can provide wide opportunities for semantic refinement,reuse,and composition of workflows.Automatic reasoning allows verifying those compositions and implementations and provides machine-actionable workflow manipulation and problem-solving using workflows.The described approach can take into account the implementation of workflows in different workflow management systems,the organization of workflows collections in data infrastructures and the search for them,the semantic approach to the selection of workflows and resources in the research domain,the creation of research step patterns and their implementation reusing fragments of existing workflows,the possibility of automation of problemsolving based on the reuse of workflows.The application of the approach to CWFR conceptions is proposed.