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The purpose of this paper is to introduce a new method of ontology‐based digital product data exchange. The digital product data are enriched with virtual prototype (VP) model information and the designed exchange process is discussed. Additionally, an algorithm enabling the ontology‐based data exchange method among missile guide systems is proposed and analyzed.

Semantic sevice‐oriented architecture (SSOA) is an innovative integration architecture for digital product information integration and process integration. In digital product ontology layer of SSOA, a new method of ontology‐based product data exchange is proposed. Compared with traditional information exchange, the method adds the VP's information to general digital products' information. The VP's information is described by building VP static structure ontology and dynamic behavior ontology. The exchange process of the novel method is discussed in detail. Finally, an example illustrating the data exchange among missile guide systems is given and the merits of the proposed method is analyzed.

Simulation information, especially VP information, is not always well defined and taken into account by product data exchange systems such as product data management or product lifecycle management. A formal description of VP information is often required in efficient and effective product data exchange. The goal is to define VP structure and behavior ontologies to enable ontology‐based digital product data exchange.

Using VP information including structure information and behavior information, the paper provides a new method of digital product data exchange.

The purpose of this paper is to improve the conceptual-based search by incorporating structural ontological information such as concepts and relations. Generally, Semantic-based information retrieval aims to identify relevant information based on the meanings of the query terms or on the context of the terms and the performance of semantic information retrieval is carried out through standard measures-precision and recall. Higher precision leads to the (meaningful) relevant documents obtained and lower recall leads to the less coverage of the concepts.

In this paper, the authors enhance the existing ontology-based indexing proposed by Kohler et al., by incorporating sibling information to the index. The index designed by Kohler et al., contains only super and sub-concepts from the ontology. In addition, in our approach, we focus on two tasks; query expansion and ranking of the expanded queries, to improve the efficiency of the ontology-based search. The aforementioned tasks make use of ontological concepts, and relations existing between those concepts so as to obtain semantically more relevant search results for a given query.

The proposed ontology-based indexing technique is investigated by analysing the coverage of concepts that are being populated in the index. Here, we introduce a new measure called index enhancement measure, to estimate the coverage of ontological concepts being indexed. We have evaluated the ontology-based search for the tourism domain with the tourism documents and tourism-specific ontology. The comparison of search results based on the use of ontology “with and without query expansion” is examined to estimate the efficiency of the proposed query expansion task. The ranking is compared with the ORank system to evaluate the performance of our ontology-based search. From these analyses, the ontology-based search results shows better recall when compared to the other concept-based search systems. The mean average precision of the ontology-based search is found to be 0.79 and the recall is found to be 0.65, the ORank system has the mean average precision of 0.62 and the recall is found to be 0.51, while the concept-based search has the mean average precision of 0.56 and the recall is found to be 0.42.

When the concept is not present in the domain-specific ontology, the concept cannot be indexed. When the given query term is not available in the ontology then the term-based results are retrieved.

In addition to super and sub-concepts, we incorporate the concepts present in same level (siblings) to the ontological index. The structural information from the ontology is determined for the query expansion. The ranking of the documents depends on the type of the query (single concept query, multiple concept queries and concept with relation queries) and the ontological relations that exists in the query and the documents. With this ontological structural information, the search results showed us better coverage of concepts with respect to the query.

The purpose of this paper is to examine the integration of digital library (DL) technologies with ontology‐based knowledge representation in providing semantic rich information access (IA) in e‐learning. DL technologies have powerful and flexible content management and access functionalities, whereas ontology helps teachers and students to link content materials to their learning objectives. This paper demonstrates that the integration provides a powerful and meaningful e‐learning environment.

DiLight is designed as an interactive e‐learning system that integrates DL and ontology technologies. By conducting comparative experiments involving DiLight in students' actual learning process, the authors examined the advantages and limitations of DiLight in e‐learning.

Compared to a widely used e‐learning environment, DiLight can provide significantly better support for students' complex IA tasks because DiLight is more useful for relationship discovery and problem solving. DiLight is also effective even when students were either less familiar with tasks or felt that they were more difficult. There is no single best access method for all learning situations. Therefore, multiple IA methods should be built into e‐learning systems. Although most of time the search was the first choice of the students, ontology‐based methods were useful in supporting them to complete their tasks too.

This is a comparative empirical study using an interactive e‐learning system called DiLight to explore the usage of integrated DL and ontology in e‐learning. The experiment results demonstrate the value of the multiple IA methods provided by DL, and the usefulness of integrating DL with ontology.

The purpose of this paper is to present an ontology‐based approach of context‐sensitive computing for the optimization of flexible manufacturing systems (FMS).

A context‐sensitive computing approach is presented, integrated on top of FMS control platform. The approach addresses how to extract manufacturing contexts at source, how to process contextual entities by developing an ontology‐based context model and how to utilize this approach for real time decision making to optimize the key performance indicators (KPIs). A framework for such an optimization support system is proposed. A practical FMS use case within SOA‐based control architecture is considered as an illustrative example and the implementation of the core functionalities to the use case is reported.

Continuous improvement of the factory can be enhanced utilizing context‐sensitive support applications, which provides an intelligent interface for knowledge acquisition and elicitation. This can be used for improved data analysis and diagnostics, real time feedback control and support for optimization.

The performance of context‐sensitive computing increases with the extraction, modeling and reasoning of as much contexts as possible. However, more computational resources and processing times are associated to this. Hence, the trade‐off should be in between the extent of context processing and the required outcome of the support applications.

This paper includes the practical implications of context‐sensitive applications development in manufacturing, especially in the dynamic operating environment of FMS.

Reported results provide a modular approach of context‐sensitive computing and a practical use case implementation to achieve context awareness in FMS. The results are seen extendable to other manufacturing domains.

This paper aims to serve two main purposes. In the first instance it aims to it provide an overview addressing the state‐of‐the‐art in the area of activity recognition, in particular, in the area of object‐based activity recognition. This will provide the necessary material to inform relevant research communities of the latest developments in this area in addition to providing a reference for researchers and system developers who ware working towards the design and development of activity‐based context aware applications. In the second instance this paper introduces a novel approach to activity recognition based on the use of ontological modeling, representation and reasoning, aiming to consolidate and improve existing approaches in terms of scalability, applicability and easy‐of‐use.

The paper initially reviews the existing approaches and algorithms, which have been used for activity recognition in a number of related areas. From each of these, their strengths and weaknesses are discussed with particular emphasis being placed on the application domain of sensor enabled intelligent pervasive environments. Based on an analysis of existing solutions, the paper then proposes an integrated ontology‐based approach to activity recognition. The proposed approach adopts ontologies for modeling sensors, objects and activities, and exploits logical semantic reasoning for the purposes of activity recognition. This enables incremental progressive activity recognition at both coarse‐grained and fine‐grained levels. The approach has been considered within the realms of a real world activity recognition scenario in the context of assisted living within Smart Home environments.

Existing activity recognition methods are mainly based on probabilistic reasoning, which inherently suffer from a number of limitations such as ad hoc static models, data scarcity and scalability. Analysis of the state‐of‐the‐art has helped to identify a major gap between existing approaches and the need for novel recognition approaches posed by the emerging multimodal sensor technologies and context‐aware personalised activity‐based applications in intelligent pervasive environments. The proposed ontology based approach to activity recognition is believed to be the first of its kind, which provides an integrated framework‐based on the unified conceptual backbone, i.e. activity ontologies, addressing the lifecycle of activity recognition. The approach allows easy incorporation of domain knowledge and machine understandability, which facilitates interoperability, reusability and intelligent processing at a higher level of automation.

The comprehensive overview and critiques on existing work on activity recognition provide a valuable reference for researchers and system developers in related research communities. The proposed ontology‐based approach to activity recognition, in particular the recognition algorithm has been built on description logic based semantic reasoning and offers a promising alternative to traditional probabilistic methods. In addition, activities of daily living (ADL) activity ontologies in the context of smart homes have not been, to the best of one's knowledge, been produced elsewhere.

XML Schema is used to define schema of XML documents that have become standards for data exchange in various Web-based information applications. The main problem of XML Schema is that it emphasizes syntax and format rather than semantics and knowledge representation. Hence, even though having the advantage of describing the structure and constraining the contents of XML documents, XML Schema lacks the computer-interpretability to support knowledge representation for existing information systems. The purpose of this study is to propose role-mapping annotations for XML Schema (RMAXS) to integrate Semantic Web with XML Schema, which allows the facilitation interoperability between adjoining layers of the Semantic Web stack.

The XML, XML Schema, ontology, and rule can be completely integrated into a multi-layered intelligent framework (MIF) for XML-based applications in the current web environment. This work presents a semantic-role-mapping intelligent system, called SRMIS, based on the MIF. SRMIS consists of XML-based document repository, search engine, inference engine and transformation engine, which provides different approaches to present the various metadata and knowledge representations.

The traditional Semantic Web stack has three gaps between adjoining layers. The first gap, between the XML and XML Schema layers can be bridged with an XMLSchema-instance mechanism. The third gap, between the ontology and rule layers can be connected by building rules on top of ontologies. This study proposes RMAXS to couple the second gap, between the XML schema and ontology layers. The proposed multi-layered intelligent framework (MIF) adopts these coupling technologies to facilitate interoperability between adjoining layers. Therefore, the XML, XML Schema, ontology, and rule can be completely integrated into the MIF for intelligent applications in the web environment.

To demonstrate the SRMIS applications, this work implements a prototype that helps researchers to find interested papers.

This work presents a semantic-role-mapping intelligent system, called SRMIS, based on the MIF. SRMIS consists of XML-based document repository, search engine, inference engine and transformation engine, which provides different approaches to present the various metadata and knowledge representations. The proposed SRMIS can be applied in various application domains.

This study aims to provide an ontology based Baysian network for clinical specialty supporting. As a knowledge base, ontology plays an essential role in domain applications especially in expert systems. Interactive question answering systems are suitable for personal domain consulting and recommended for real-time usage. Clinical specialty supporting for dispatching patients can assist hospitals to locate desired treatment departments for individuals relevant to their syndromes and disease efficiently and effectively. By referring to interactive question answering systems, individuals can understand how to alleviate time and medical resource wasting according to recommendations from medical ontology-based systems.

This work presents an ontology based on clinical specialty supporting using an interactive question answering system to achieve this aim. The ontology incorporates close temporal associations between words in input query to represent word co-occurrence relationships in concept space. The patterns defined in lexicon chain mechanism are further extracted from the query words to infer related concepts for treatment departments to retrieve information.

The precision and recall rates are considered as the criteria for model optimization. Finally, the inference-based interactive question answering system using natural language interface is adopted for clinical specialty supporting, and indicates its superiority in information retrieval over traditional approaches.

From the observed experimental results, we find the proposed method is useful in practice especially in treatment department decision supporting using metrics precision and recall rates. The interactive interface using natural language dialogue attracts the users’ attention and obtains a good score in mean opinion score measure.

The purpose of this paper is to present and develop an ontology-based approach for automatic generation of assembly sequences.

In this approach, an assembly sequence planning ontology is constructed to represent the structure and interrelationship of product geometry information and assembly process information. In the constructed ontology, certain reasoning rules are defined to describe the knowledge and experience. Based on the ontology with reasoning rules, the algorithm for automatically generating assembly sequences is designed and implemented.

The effectiveness of this approach is verified via applying it to generate the assembly sequences of a gear reducer.

The main contribution of the paper is presenting and developing an ontology-based approach for automatically generating assembly sequences. This approach can provide a feasible solution for the issue that mathematics-based assembly sequence generation approaches have great difficulty in explicitly representing assembly experience and knowledge.

The purpose of this research is to introduce a mapping of the Simple Knowledge Organization Systems (SKOS) Core metadata to an ontology‐based model, whose main aim is to foster the semantic interoperability of different concept schemes.

Research objectives have been achieved through the introduction of a common ground for the definition of concepts, based on shared definitions included in widely used upper ontologies. This effort makes use of a particular upper ontology: OpenCyc, the open source version of Cyc, which is currently one of the most complete general knowledge bases.

An in‐depth study of the SKOS vocabulary has suggested its extension, with the aim of correcting the shortcomings related with SKOS schemes semantic interoperability. However, although such an extension would help avoid ambiguities and enable inter‐thesaurus interoperability, the paper is focused on using a non‐invasive contribution. Non‐invasive in that the SKOS Core should not be modified as a result of this activity, but also, non‐invasive in that current SKOS schemes would not require modifications.

The use of formal representations to provide the SKOS terms with computational semantics, as well as the introduction of an intermediate ontology‐based model built on the SKOS information. Although both proposals stand on one upper ontology (OpenCyc), they could be easily adapted to others, which provides an added value to this research work.

Product-service system (PSS) has been attracting attentions of global manufacturing to providing high-value added services in addition to their traditional product development and manufacturing business. For this reason, it is of great importance to research PSS. The purpose of this paper is to establish a systematic strategy and a system tool for PSS design.

A requirement-driven product-service system (RdPPS) is developed using requirements analysis and knowledge management technologies. A framework is proposed to support RdPPS by providing tools and methods for requirement analysis and processing, formalization of PSS by ontology-based knowledge representation, reasoning method for PSS solution finding, and solution optimizing and assessing. Finally, the design support strategies for RdPPS are investigated to demonstrate the usability and functioning of the developed system.

Many conventional design methods did not consider the influence of customer requirements (CRs) during the planning phase of PSS design. Moreover, a broader range of knowledge is required to PSS design, since both products and services are considered.

This research provides a solid foundation for PSS, and promotes an effective means for PSS design.

A RdPSS is presented. CRs are considered during the design phase of PSS as well as both product and service knowledge.