The (semi-)automated integration of new information into a data model is a functionality which is required in cases when input documents are extensive and therefore a manual integration difficult or even impossible. We proposed the solution combining the ontology learning process with information acquisition from the Web (web mining). This approach offers a robust way how to integrate even previously unknown information disregarding target application or domain. The solution deals with facilitating identification of input data among existing concepts or with the definition of a new concept. The proposed solution was experimentally verified on the integration of an excel document containing spare parts and Ford Supply Chain Ontology.

For over twenty five years Ford has been utilizing an AI-based system to manage process planning for vehicle assembly at our assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on Ergonomics and Powertrain Assembly (Engines and Transmission plants). The knowledge about Ford’s manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology. To preserve the viability of the GSPAS ontology and to make it easily usable for other applications within Ford, we needed to re-engineer and convert the KL-ONE ontology into a semantic web OWL/RDF format. In this paper, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed Semantic Web technology in our application.

In 2007 we presented a paper that described the application of Natural Language Processing (NLP) and Machine Translation (MT) for the automated translation of process build instructions from English to other languages to support Ford’s assembly plants in non-English speaking countries. This project has continued to evolve with the addition of new languages and improvements to the translation process. However, we discovered that there was a large demand for automated language translation across all of Ford Motor Company and we decided to expand the scope of our project to address these requirements. This paper will describe our efforts to meet all of Ford’s internal translation requirements with AI and MT technology and focus on the challenges and lessons that we learned from applying advanced technology across an entire corporation.

The automotive manufacturing process is one of the most complex processes in industry today. The rapid changes in the marketplace and introduction of new technologies require that the underlying manufacturing processes keep pace. The use of intelligent systems for this environment is quickly becoming a necessity. In this paper, we describe how some of these intelligent methods are used at Ford Motor Company in the automotive assembly planning domain. The following applications of intelligent systems will be described: knowledge-based labor management, ergonomics analysis, and workcell layout optimization. We will also discuss how natural language processing can assist in understanding unstructured textual data. All of these examples show how computational intelligence can be successfully applied in an industrial setting.

Evolutionary computation has been successfully applied in a variety of problem domains and applications. In this paper we discuss the use of a specific form of evolutionary computation known as Cultural Algorithms to improve the efficiency of the subsumption algorithm in semantic networks. We identify two complementary methods of using Cultural Algorithms to solve the problem of re-engineering large-scale dynamic semantic networks in order to optimize the efficiency of subsumption: top-down and bottom-up. The top-down re-engineering approach improves subsumption efficiency by reducing the number of attributes that need to be compared for every node without impacting the results. We demonstrate that a Cultural Algorithm approach can be used to identify these defining attributes that are most significant for node retrieval. These results are then utilized within an existing vehicle assembly process planning application that utilizes a semantic network based knowledge base to improve the performance and reduce complexity of the network. It is shown that the results obtained by Cultural Algorithms are at least as good, and in most cases better, than those obtained by the human developers. The advantage of Cultural Algorithms is especially pronounced for those classes in the network that are more complex. The goal of bottom-up approach is to classify the input concepts into new clusters that are most efficient for subsumption and classification. While the resultant subsumption efficiency for the bottom-up approach exceeds that for the top-down approach, it does so by removing structural relationships that made the network understandable to human observers. Like a Rete network in expert systems, it is a compilation of only those relationships that impact subsumption. A direct comparison of the two approaches shows that bottom-up semantic network re-engineering creates a semantic network that is approximately 5 times more efficient than the top-down approach in terms of the cost of subsumption. In conclusion, we will discuss these results and show that some knowledge that is useful to the system users is lost during the bottom-up re-engineering process and that the best approach for re-engineering a semantic network requires a combination of both of these approaches.

Evolutionary computation has been successfully applied in a variety of problem domains and applications. In this paper we discuss the use of a specific form of evolutionary computation known as cultural algorithms that has been applied successfully in various real-world applications to solve problems of a very dynamic and complex nature. Cultural algorithms introduce a learning component into an evolutionary framework that influences the search strategy and is in turn modified by the best-performing members of the population during the entire process. Cultural algorithms have been used in various applications, including fraud analysis for automotive accident claims, the re-engineering of a dynamic automobile manufacturing knowledge base, the modeling of pricing strategies for automobiles in a multi-agent environment and for data mining.

Ford's Direct Labor Management System (DLMS) is a fully-deployed application that is being utilized at Ford's assembly plants throughout the world. DLMS has been in production since 1990, and maintainability of the knowledge base has become very difficult over time, due to changes in the following areas: the business environment, the processes and physical concepts being modeled, and the underlying hardware and software architecture. We have previously (2000, 2001) described how cultural algorithms can be used in a top-down and bottom-up fashion to re-engineer a dynamic semantic network-based knowledge base. In this paper, we compare the two approaches and discuss how they can be used in tandem to re-engineer a semantic network.

Evolutionary computation has been successfully applied in a variety of problem domains and applications. The authors discuss the use of a specific form of evolutionary computation known as cultural algorithms to improve the efficiency of the subsumption algorithm in semantic networks. Subsumption is the process that determines if one node in the network is a child of another node. As such, it is utilized as part of the node classification algorithm within semantic network based applications. One method of improving subsumption efficiency is to reduce the number of attributes that need to be compared for every node without impacting the results. We demonstrate that a cultural algorithm approach can be used to identify these defining attributes that are most significant for node retrieval. These results can then be utilized within an existing vehicle assembly process planning application that utilizes a semantic network based knowledge base to improve the performance and reduce complexity of the network.

Evolutionary computation has been successfully applied in a variety of problem domains and applications. We describe the use of a specific form of evolutionary computation known as cultural algorithms to improve the efficiency of the subsumption algorithm in semantic networks. Subsumption is the process that determines if one node in the network is a child of another node. As such, it is utilized as part of the node classification algorithm within semantic network based applications, One method of improving subsumption efficiency is to reduce the number of attributes that need to be compared for every node without impacting the results. We suggest that a cultural algorithm approach can be used to identify these defining attributes that are most significant for node retrieval. These results can then be utilized within an existing vehicle assembly process planning application that utilizes a semantic network based knowledge base to improve the performance and reduce complexity of the network.

Ford Motor Company's Direct Labor Management System (DLMS) is the knowledge-based subsystem of complex multiphase manufacturing process planning system. DLMS is a deployed production system that is used daily by Ford Body and Assembly Division personnel in the performance of their duties. The knowledge used to drive the assembly planning system is stored in a knowledge base that is based on the KL-ONE knowledge representation system. This paper discusses the use of KL-ONE in an application-oriented environment and gives a "report from the field" of KL-ONE in production use. Advantages and disadvantages of using KL-ONE to represent a dynamic, large-scale conceptual framework for process planning is discussed.>