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Building Knowledge about Buildings

Building Knowledge about Buildings. Matt Young and Eyal Amir University of Illinois, Urbana-Champaign. The Problem. Need a way to represent information about buildings. A wealth of information exists in floor plans, but what information do we need? How to encode it?. Our Goals.

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Building Knowledge about Buildings

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  1. Building Knowledge about Buildings Matt Young and Eyal Amir University of Illinois, Urbana-Champaign

  2. The Problem • Need a way to represent information about buildings. • A wealth of information exists in floor plans, but what information do we need? How to encode it?

  3. Our Goals • A general framework for representing buildings which is: • Simple enough to add data quickly/automatically. • Complete enough to accurately represent the structure of a building. • Able to answer queries regarding that structure.

  4. Overview • Previous Work • Overview of Our Language • Comparison with Current Technology • Other Applications • Future Work

  5. Previous Work - Cyc • Contains a “Building” constant, defined as “A specialization of both FixedStructure and HumanShelterConstruction.” • By following assertions through the hierarchy, we can learn certain information about a building such as what rooms it contains, how many levels it has, etc. • However, there is no structured presentation of how things are connected together, how the building is actually constructed.

  6. Previous Work - IFC Data Model • International standard for architectural firms, CAD developers, and construction companies. • Very detailed information about building construction. • However, also contains a great deal of information about processes, analysis, CAD data, etc. • Also, it is inconsistently implemented.

  7. Our Solution • A language designed specifically to capture only the structure of a building. • Encoded as an ontology in OWL DL, for ease of use with the Semantic Web, and (hopefully) full decidability on inference.

  8. Language – General Classes • Classes define different features of a Building. • Four main classes • Building • External_Feature • Internal_Feature • Material • Subclasses define distinct feature types.

  9. Language - Properties • Properties define relations between features. • Most are defined symmetrically, for strong connectedness.

  10. Language - Assertions • Assertions enforce proper construction of buildings. • Ensure that certain properties must be filled with some value (or possibly more than one value). • There are no value restrictions.

  11. Language - Specialization • Language can be extended with subclasses of the general classes define to subtypes of each feature.e.g. House is a subtype of Building, Bedroom is a subtype of Room. • Subtypes are defined by additional restrictions, some of which may be value restrictions. • Subtypes can also be inferred, but this slows down search considerably.

  12. Language - Limitations • No spatial information (size, shape). • No information about environment surrounding building. • Some features are difficult to encode: • Features serving multiple purposes (e.g. A roof also serving as a wall, such as in an A-frame). • Features which are both external and internal.

  13. Comparison with Current Technology

  14. Other Applications • Find paths out of buildings (fire escapes). • Complete a building floor plan given a partial encoding of the building. • Use a knowledge base encoded in this language to categorize buildings given partial information about them.

  15. Future Work • Adding spatial information without losing decidability. • Adding encoding for surrounding environment and for objects within the building to create a full virtual world space. • Encoding data automatically from floor plans or IFC models.

  16. Conclusion • Special thanks to Eyal for all his help and guidance. • Questions / Comments ?

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