An ontology-based approach for inventive problem solving

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 Protege 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.

[1]  Jaap Gordijn,et al.  Value Webs: using ontologies to bundle real-world services , 2004, IEEE Intelligent Systems.

[2]  G. S. Alʹtshuller,et al.  The Innovation Algorithm:TRIZ, systematic innovation and technical creativity , 1999 .

[3]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[4]  John J. McCarthy,et al.  The Rule Engine for the Java Platform , 2008 .

[5]  Samson W. Tu,et al.  Writing Rules for the Semantic Web Using SWRL and Jess , 2005 .

[6]  H. Lan,et al.  SWRL : A semantic Web rule language combining OWL and ruleML , 2004 .

[7]  François Rousselot,et al.  An Application of Semantic Distance between Short Texts to Inventive Design , 2011, KEOD.

[8]  Michele Dassisti,et al.  Ontology-based system for supporting manufacturing sustainability , 2012, Annu. Rev. Control..

[9]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993 .

[10]  Jaap Gordijn,et al.  Energy Services: A Case Study in Real-World Service Configuration , 2004, CAiSE.

[11]  Karen Sparck Jones.  A statistical interpretation of term specificity and its application in retrieval , 1972 .

[12]  Zuhair Bandar,et al.  Sentence similarity based on semantic nets and corpus statistics , 2006, IEEE Transactions on Knowledge and Data Engineering.

[13]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[14]  Paul W. Prickett,et al.  The development of a modified TRIZ Technical System ontology , 2012, Comput. Ind..

[15]  Denis Cavallucci,et al.  Use of formal ontologies as a foundation for inventive design studies , 2011, Comput. Ind..

[16]  Denis Cavallucci,et al.  Parameter network as a means for driving problem solving process , 2007, Int. J. Comput. Appl. Technol..

[17]  Henrik Eriksson,et al.  Using JessTab to Integrate Protégé and Jess , 2003, IEEE Intell. Syst..

[18]  Charles T. Meadow,et al.  Text information retrieval systems , 1992 .

[19]  Asunción Gómez-Pérez,et al.  Methodologies, tools and languages for building ontologies: Where is their meeting point? , 2003, Data Knowl. Eng..

[20]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[21]  Denis Cavallucci,et al.  A Method for Facilitating Inventive Design Based on Semantic Similarity and Case-Based Reasoning , 2015 .

[22]  Altshuller.  Creativity As an Exact Science , 1984 .

[23]  François Rousselot,et al.  A substance-field ontology to support the TRIZ thinking approach , 2007, Int. J. Comput. Appl. Technol..

[24]  Deborah L. McGuinness,et al.  OWL Web ontology language overview , 2004 .

[25]  Curt Burgess,et al.  Explorations in context space: Words, sentences, discourse , 1998 .

[26]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[27]  Loh Han Tong,et al.  Grouping of TRIZ Inventive Principles to facilitate automatic patent classification , 2008 .

[28]  Michael E. Lesk,et al.  Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone , 1986, SIGDOC '86.

[29]  Nicola Guarino,et al.  Editorial: The role of formal ontology in the information technology , 1995, Int. J. Hum. Comput. Stud..

[30]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[31]  Michael E. Lesk,et al.  Computer Evaluation of Indexing and Text Processing , 1968, JACM.

[32]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[33]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .