A fuzzy extension of the semantic Building Information Model

Abstract The Building Information Model (BIM) has become a key tool to achieve communication during the whole building life-cycle. Open standards, such as the Industry Foundation Classes (IFC), have contributed to expand its adoption, but they have limited capabilities for cross-domain information integration and query. To address these challenges, the Linked Building Data initiative promotes the use of ontologies and Semantic Web technologies in order to create more formal and interoperable BIMs. In this paper, we present a fuzzy logic-based extension of such semantic BIMs that provides support for imprecise knowledge representation and retrieval. We propose an expressive fuzzy ontology language, and describe how to use a fuzzy reasoning engine in a BIM context with selected examples. The resulting fuzzy semantic BIM enables new functionalities in the project design and analysis stages—namely, soft integration of cross-domain knowledge, flexible BIM query, and imprecise parametric modeling.

[1]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[2]  Jakob Beetz,et al.  IfcOWL: A case of transforming EXPRESS schemas into ontologies , 2008, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[3]  Pieter Pauwels,et al.  A semantic rule checking environment for building performance checking , 2011 .

[4]  Miguel A. Patricio,et al.  Topological properties in ontology-based applications , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[5]  Troels Andreasen,et al.  Conceptual querying through ontologies , 2009, Fuzzy Sets Syst..

[6]  Ian Horrocks,et al.  Handbook of Knowledge Representation Edited Description Logics 3.1 Introduction , 2022 .

[7]  Umberto Straccia,et al.  Managing uncertainty and vagueness in description logics for the Semantic Web , 2008, J. Web Semant..

[8]  F. Henry Abanda,et al.  Trends in built environment semantic Web applications: Where are we today? , 2013, Expert Syst. Appl..

[9]  Kenneth T. Sullivan,et al.  How To Measure the Benefits of BIM - A Case Study Approach , 2012 .

[10]  Umberto Straccia,et al.  Optimising fuzzy description logic reasoners with general concept inclusion absorption , 2016, Fuzzy Sets Syst..

[11]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[12]  Rung Ching Chen,et al.  Merging domain ontologies based on the WordNet system and Fuzzy Formal Concept Analysis techniques , 2011, Appl. Soft Comput..

[13]  Seppo Törmä,et al.  Semantic Linking of Building Information Models , 2013, 2013 IEEE Seventh International Conference on Semantic Computing.

[14]  Umberto Straccia,et al.  Fuzzy Bilateral Matchmaking in e-Marketplaces , 2008, KES.

[15]  Karen Kensek,et al.  Building Information Modeling: BIM in Current and Future Practice , 2014 .

[16]  Fernando Bobillo,et al.  Crisp Representations and Reasoning for Fuzzy Ontologies , 2009, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[17]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[18]  Fernando Bobillo,et al.  DeLorean: A reasoner for fuzzy OWL 2 , 2012, Expert Syst. Appl..

[19]  James A. Rodger,et al.  A fuzzy linguistic ontology payoff method for aerospace real options valuation , 2013, Expert Syst. Appl..

[20]  Umberto Straccia,et al.  Reasoning within Fuzzy Description Logics , 2011, J. Artif. Intell. Res..

[21]  Hani Hagras,et al.  A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation , 2010, IEEE Transactions on Fuzzy Systems.

[22]  Elie Sanchez,et al.  Fuzzy Logic and the Semantic Web , 2005 .

[23]  Boris Motik,et al.  HermiT: A Highly-Efficient OWL Reasoner , 2008, OWLED.

[24]  Joan Torrens,et al.  On a class of operators for expert systems , 1993, Int. J. Intell. Syst..

[25]  Jakob Beetz,et al.  BIMQL - An open query language for building information models , 2013, Adv. Eng. Informatics.

[26]  Pieter Pauwels,et al.  Supporting Decision-Making in the Building Life-Cycle Using Linked Building Data , 2014 .

[27]  Adrian Popescu,et al.  Fuzzy Ontology Alignment using Background Knowledge , 2014, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[28]  Rolf Grütter,et al.  Towards Spatial Reasoning in the Semantic Web: A Hybrid Knowledge Representation System Architecture , 2007, AGILE Conf..

[29]  Umberto Straccia,et al.  fuzzyDL: An expressive fuzzy description logic reasoner , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[30]  Umberto Straccia,et al.  Fuzzy description logics under Gödel semantics , 2009, Int. J. Approx. Reason..

[31]  Sebastian Rudolph,et al.  Foundations of Semantic Web Technologies , 2009 .

[32]  M. Vila,et al.  A general model for fuzzy linear programming , 1989 .

[33]  Chang-Shing Lee,et al.  A fuzzy ontology and its application to news summarization , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[34]  E. Herrera-Viedma,et al.  A new consensus model for group decision making using fuzzy ontology , 2013, Soft Comput..

[35]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[36]  Umberto Straccia,et al.  Fuzzy Ontology Representation using OWL 2 , 2010, Int. J. Approx. Reason..

[37]  Edward Curry,et al.  Linking building data in the cloud: Integrating cross-domain building data using linked data , 2013, Adv. Eng. Informatics.

[38]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[39]  Markus Krötzsch,et al.  The Incredible ELK , 2013, Journal of Automated Reasoning.

[40]  Umberto Straccia,et al.  Towards spatial reasoning in fuzzy description logics , 2009, 2009 IEEE International Conference on Fuzzy Systems.