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Su-Field analysis, as one of TRIZ analytical tool for solving inventive problems, can be used to improve the performance of the technical system effectively. Generally, choosing an appropriate inventive standard is critical to solving inventive problems efficiently and accurately. However, these standards are summarized and categorized based on the enormous amount of patents in different domains, and they are built in the high level of abstraction, independently of the specific application field, making their use require much more technical knowledge than other TRIZ tools. In order to facilitate the use of inventive standards, especially for capturing the uncertainty or imprecision depicted in the standards, a rule-based heuristic methodology is proposed in this paper. Firstly, Su-Field analysis ontology and fuzzy analysis ontology are built to represent the precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) inference and fuzzy inference are performed for generating heuristic concept solution. Finally, a prototype is developed and the resolution of the case of the "Auguste Piccard's Stratostat" in prototype is elaborated in detail. [ABSTRACT FROM AUTHOR]

Abstract: Su-Field analysis, as one of the inventive problem solving tools, can be used to analyze and improve the efficiency of a technical system. Generally, the process of using Su-Field models to solve a specific inventive problem includes building a Problem Model, mapping to a Generic Problem Model (the abstract Problem Model to describe the Problem Models with similar properties), finding a Generic Solution Model (the abstract Solution Model to represent the Solution Models with similar modifications) based on the corresponding inventive standard, and finally establishing and instantiating a Solution Model. As one of the most important phases of Su-Field analysis, the last step is normally implemented manually with the help of a knowledge base of physical effects, which link generic technical functions with specific applications and systems. The physical effects compatible with the context of the specific problem should be chosen to assist the users to instantiate the Solution Model. However, the physical effects and the specific problems are built at different levels of abstraction, and it is difficult for the users to choose. In order to facilitate the use of physical effects, this paper firstly proposes a new way of representing physical effects by making explicit a state change, that is, the couple of two states, before and after applying the physical effect. Then, knowledge about using physical effects is formalized in OWL (Ontology Web Language), and constraint knowledge, such as the preconditions to use each kind of physical effects, is formalized in SWRL (Semantic Web Rule Language). Finally, the reasoning process of using physical effects is performed with the support of the Jess (Java Expert System Shell) rule engine. The case of a “Diving Fin” is used to illustrate the whole process in detail. [Copyright &y& Elsevier]

Abstract: With the development of the theory of inventive problem solving (TRIZ), different knowledge sources were established in order to solve different types of inventive problems, such as 40 inventive principles for eliminating the technical contradictions. These knowledge sources with different levels of abstraction are all built independent of the specific application field, and require extensive knowledge about different engineering domains. In order to facilitate the use of the TRIZ knowledge sources, this paper explores a new inventive problem solving approach based on ontologies. In this approach, the TRIZ users start solving an inventive problem with the TRIZ knowledge source of their choice to obtain an abstract solution. According to the selected items of that first knowledge source, the similar items of other knowledge sources are obtained based on the semantic similarity calculated in advance. Considering that all the TRIZ knowledge sources are described as short-texts, the missing links among the TRIZ knowledge sources are defined based on short-text semantic similarity. At the same time, the ontology reasoning mechanism, deployed on Protégé and JESS, is used to provide heuristic solutions dynamically for TRIZ users. The case of a “Space Boiler” is used to show this ontology-based inventive problem solving process in detail. [Copyright &y& Elsevier]

With the development of the Theory of Inventive Problem Solving (TRIZ), different models and knowledge sources were established in order to solve different types of inventive problems. These knowledge sources with different levels of abstraction are all built independently of the specific application field, and require extensive knowledge about different engineering domains. In order to facilitate the use of these TRIZ models and knowledge sources, an intelligent knowledge management system – IngeniousTRIZ was developed in this research. On the one hand, according to the TRIZ knowledge sources ontologies, this system offers to the users the relevant knowledge sources of the model they are building. On the other hand, the system has the ability to fill “automatically” the models of the other knowledge sources. Firstly, the TRIZ user chooses a TRIZ knowledge source to work for an abstract solution. Then, the items of other knowledge sources, which are similar with the selected items of the first knowledge source, are obtained based on semantic similarity calculated in advance. With the help of these similar items and the heuristic physical effects, other concept solutions are generated through ontology inference. In order to show this whole process, the resolution of the case of the “diving fin” in IngeniousTRIZ system is elaborated in detail. [ABSTRACT FROM AUTHOR]