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Research on Integration of Ontology-Based Product Knowledge: Framework, Schema and Algorithm
Abstract
To deal with the problem that consistency in semantic representation of exchanged product knowledge in collaborative manufacturing, an ontology-based framework of knowledge integration and sharing was presented, and in the meantime an integration algorithm based on ontology mapping and merging was designed under this framework. First of all, a collaborative operation model for enterprise collaboration was discussed, and then an ontology-based framework of knowledge integration and sharing was established. Under the condition of analyzing the structure of product knowledge ontology, an ontology logic schema of knowledge concept was designed. Aiming at the ontology logic schema, an ontology integration algorithm of product knowledge was also improved. The algorithm is composed of ontology mapping and merging. Finally, the proposed ontology integration algorithm has been verified to be effective through the comparing experiment.
... Tran et al. proposed a method to implement ontology in KBE application development which is for 3D geometry generation, to gain the freedom to incorporate development tools regardless of discipline or platform [14]. Liu et al. presented an ontology-based framework to deal with the consistency in semantic representation of exchanged product knowledge in collaborative manufacturing [15]. Sanya et al. constructed the ontology model of their KBE system in the aerospace industry, which strengthens the knowledge reuse and eliminates platform-specific approaches to developing similar KBE systems [16]. ...
To expedite a semantic product design environment, the relevant minimal set of design rules could optimally be distilled from the given disparate design rules. However, in disparate design rules, some attributes are frequently unused in other design rules. Treating all the attributes in all the rules increases reasoning complexity. In the situation of incomplete information, treating the attributes, which may be relevant to some rules and not to others, as “missing” ones is found to cause reasoning problems. In this paper, we present a new algorithm, i.e. the disparate attributes algorithm, for managing semantic assembly design rules. The proposed disparate attributes algorithm by identifying “inapplicable” information extends a rough set approach for managing semantic assembly design rules with systematic selection of various minimal sets of rules. To validate the disparate attributes algorithm, the approach is tested with multiple realistic assembly design cases.
The knowledge representation is the symbolization process of knowledge. In nature, knowledge representation adopts a technology model to map the knowledge required to solute problems into data structures to find a solution to these problems. The process of knowledge representation transforms the relevant knowledge into data structures. The goal of knowledge mapping in an intelligent learning system is to realize the machine resolution to complex intelligent questions. However, it is extremely difficult for machines to resolve these questions directly. An ontology model of framework representation for knowledge is proposed based on ontology and framework technology. The result shows the proposed model provides a novel and easy method to represent knowledge in intelligent learning systems.
To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse—definitions of classes, relations, functions, and other objects—is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations.We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how to translate from a very expressive language into restricted languages, remaining system-independent while preserving the computational efficiency of implemented systems. We describe how these problems are addressed by basing Ontolingua itself on an ontology of domain-independent, representational idioms.
Knowledge is increasingly regarded as one of the key success factors of today's industry. This is partly due to the rapidly growing amount of potentially available information. The probably more important aspect of this development is that access to information becomes ever faster and more comprehensive. Product development and production have always been knowledge -driven processes. Nevertheless, some things have changed recently: In a world that is economically networked, it becomes important not only to own and eventually use information, but to use it as efficiently as possible, to exchange it and to generate new knowledge based on existing information. Although this insight is widely accepted, it is sometimes still neglected by industrial practice. A comprehensive knowledge management support for extended enterprises requires to take into account challenges that knowledge, ontology and product life cycle pose.
This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far achieved limited success.
Expert human input is essential in almost all cases. Achievements have been made largely in the form of tools and aids to assist the human expert. Many research challenges remain in this field and many of such challenges need to be overcome if the next generation of the Semantic Web is to be realized.
Interoperability over distributed ontologies is one of the six challenges for Semantic Web. Ontology mapping is the key point to each the interoperability. In this paper, based on Bayesian decision theory, the authors propose an approach called RiMOM (Risk Minimization based Ontology Mapping) to automatically discover the mapping between ontologies. RiMOM treats the mapping problem as a decision problem and formalizes mapping discovery as that of risk minimization. Based on multiple strategies, RiMOM deals with not only 1: 1 mapping, but also n: 1 mapping. Experiments on several public data sets show that RiMOM can outperform existing methods in terms of precision and recall.
This is the second of a two-part paper to review ontology research and development, in particular, ontology mapping and evolving. Ontology is defined as a formal explicit specification of a shared conceptualization. Ontology itself is not a static model so that it must have the potential to capture changes of meanings and relations. As such, mapping and evolving ontologies is part of an essential task of ontology learning and development. Ontology mapping is concerned with reusing existing ontologies, expanding and combining them by some means and enabling a larger pool of information and knowledge in different domains to be integrated to support new communication and use. Ontology evolving, likewise, is concerned with maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired. It is apparent from the reviews that current research into semi-automatic or automatic ontology research in all the three aspects of generation, mapping and evolving have so far achieved limited success. Expert human input is essential in almost all cases. Achievements have been made largely in the form of tools and aids to assist the human expert. Many research challenges remain in this field and many of such challenges need to be overcome if the next generation of the Semantic Web is to be realized.
Engineering design processes comprise highly creative and knowledge-intensive tasks that involve extensive information exchange and communication among distributed teams. In such dynamic settings, traditional information management systems fail to provide adequate support due to their inflexible data structures and hard-wired usage procedures, as well as their restricted ability to integrate process and product information. In this paper, we advocate the idea of Process Data Warehousing as a means to provide a knowledge management and integration platform for such design processes. The key idea behind our approach is a flexible ontology-based schema with formally defined semantics that enables the capture and reuse of design experience, supported by advanced computer science methods.
Ontology mapping is the key point to reach interoperability over ontologies. In semantic web environment, ontologies are usually distributed and heterogeneous and thus it is necessary to find the mapping between them before processing across them. Many efforts have been conducted to automate the discovery of ontology mapping. However, some problems are still evident. In this paper, ontology mapping is formalized as a problem of decision making. In this way, discovery of optimal mapping is cast as finding the decision with minimal risk. An approach called Risk Minimization based Ontology Mapping (RiMOM) is proposed, which automates the process of discoveries on 1:1, n:1, 1:null and null:1 mappings. Based on the techniques of normalization and NLP, the problem of instance heterogeneity in ontology mapping is resolved to a certain extent. To deal with the problem of name conflict in mapping process, we use thesaurus and statistical technique. Experimental results indicate that the proposed method can significantly outperform the baseline methods, and also obtains improvement over the existing methods.
In order to realize automated knowledge acquisition, we have to solve many problems such as lack of reusability and sharability of knowledge, which is one of the shortcomings in the current knowledge base technology, to fill the conceptual gap between the computer and domain experts and so on. Recent research activities in knowledge acquisition community are focused on task ontology, because it is expected to contribute a lot to making it easier to elicit expertise from domain experts. The authors have been involved in the research of knowledge acquisition and knowledge reuse. This article is concerned with task ontology and its use in a task analysis interview system MULTIS. We first discuss the knowledge reusability to identify appropriate task ontology. Then, we introduce a two-level mediating representation that contributes to bridging the gap and hence to making the task analysis interview fluent. MULTIS has been implemented in Macintosh Common Lisp.
To realize a truly collaborative product design and development process, effective communication among design collaborators is a must. In other words, the design intent that is imposed in a product design should be seized and interpreted properly; heterogeneous modeling terms should be semantically processed both by design collaborators and intelligent systems. Ontologies in the Semantic Web can explicitly represent semantics and promote integrated and consistent access to data and services. Thus, if an ontology is used in a heterogeneous and distributed design collaboration, it will explicitly and persistently represent engineering relations that are imposed in an assembly design. Design intent can be captured by reasoning, and, in turn, as reasoned facts, it can be propagated and shared with design collaborators. This paper presents a new paradigm of ontology-based assembly design. In the framework, an assembly design (AsD) ontology serves as a formal, explicit specification of assembly design so that it makes assembly knowledge both machine-interpretable and to be shared. An Assembly Relation Model (ARM) is enhanced using ontologies that represent engineering, spatial, assembly, and joining relations of assembly in a way that promotes collaborative assembly information-sharing environments. In the developed AsD ontology, implicit AsD constraints are explicitly represented using OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language). This paper shows that the ability of the AsD ontology to be reasoned can capture both assembly and joining intents by a demonstration with a realistic mechanical assembly. Finally, this paper presents a new assembly design information-sharing framework and an assembly design browser for a collaborative product development.