36. BIM Ontology
Aug 16, 2015
Figure 1. The BIM ontology v3.1 (Neuron Model v0.4, Updated July 28, 2016)
IMPORTANT: newer versions of the BIM Ontology are published as a BIMe Initiative resource. As of July 28, 2017, the image above and the information below may be out of date.
The BIM ontology is an informal, semi-structured, conceptual domain ontology used for knowledge acquisition and communication between people. It is intended to represent knowledge interactions (push/pull) between BIM players, their deliverables and requirements (Figure 2) as described within Papers A1 and A2 (Succar, Sher, & Aranda-Mena, 2007) (Succar, 2009) and facilitate the validation of conceptual models (Shanks, Tansley, & Weber, 2003).
The BIM ontology includes BIM-specific concepts, their relations and attributes which facilitate analysis of domain knowledge (Noy & McGuinness, 2001), enable the construction of a domain framework (Studer, Benjamins, & Fensel, 1998), and support knowledge acquisition and communication (Milton, 2007a, 2007b) (Cottam, 1999) (Studer et al., 1998). Figure 2 below illustrates how ontological objects underlie the BIM Framework. The concept map (Figure 2 - right) is a visual representation of the ontological relationship between the three concepts (BIM Fields, BIM Stages and BIM Lenses); while the visual knowledge model (Figure 2 - left) abstracts these relations into the Tri-axial Model, a simplified graphical representation to facilitate communication. As discussed in Papers A1 and A2, this combination of visual modelling, driven by explicit ontological relations, renders the BIM Framework and its many conceptual constructs more accessible for analysis, modification and extension.
Also, as depicted in the Conceptual Hierarchy post, ontological relations enable a ‘conceptual mesh’ linking different types of conceptual constructs: frameworks, models, taxonomies, classifications and specialized dictionary terms.
Figure 2. Visual Knowledge Model (left) + Concept Map (right)
Generating the BIM ontology
The BIM ontology has been generated by amending and reusing existing ontologies; a process recommended by Noy and McGuiness (2001). The reuse of an existing ontology followed Gruber’s criteria for shared ontologies: clarity, coherence, extensibility, minimal encoding bias and minimum ontological commitment (Gruber, 1995). Based on these criteria, the BIM ontology was first derived from the General Technological Ontology (Milton, 2007a) (Milton, 2007b) and the General Process Ontology (Cottam, 1999). While earlier iterations of the BIM ontology followed source definitions, newer iterations are more closely matched with the conceptual and practical requirements of the BIM domain.
Knowledge Objects
The BIM ontology comprises of four high-level knowledge objects: concepts, attributes, relations and knowledge Sets (Table 1):
|
Knowledge Objects |
Description |
Examples |
I |
Concepts (Table 2) |
A mental construct |
Component; Document; Role |
II |
Attributes (Table 3) |
Values and qualifiers associated with Concepts |
Cost; Count; Description |
III |
Relations (Table 4) |
Connections between Concepts; the effect of one Concept on another |
Approves; Detects; Supplies |
IV |
Knowledge Sets (Table 5) |
A purposeful compilation of Concepts, their Attributes and Relations |
Knowledge Foundations; Knowledge Blocks; Knowledge Views |
Table 1. BIM Ontology Knowledge Objects (v3.0, last updated August 16, 2015)
I. Concepts
Ability |
Activity |
Certificate |
Component |
Conception |
Conceptual Construct |
Constraint |
Data Source |
Data Use |
Deliverable |
Designation |
Document |
Document Use |
Effect |
Equipment |
Event |
Example |
Facility |
Format |
Function |
Hardware |
Incentive |
Information Use |
Knowledge Domain |
Lesson |
Machine |
Measurement |
Medium |
Message |
Method |
Milestone |
Model |
Model Use |
Place |
Player |
Product |
Proof |
Recommendation |
Representation |
Requirement |
Responsibility |
Result |
Role |
Rule |
Scenario |
Software Application |
Space |
Standard |
System |
System Unit |
Target | Test | Tool | Trigger |
Table 2. Concepts (51 Concepts in v3.10, last updated July 28, 2016)
II. Attributes
Name |
Description |
Name |
Description |
Availability |
An integer or string indicating the basic existence or availability of a concept |
Location |
The coordinates of an object within a physical space |
Cost |
A monetary value expressed in whole numbers, fractions or decimals |
Market |
A defined economical boundary |
Count |
An expression of elemental numbers using integers |
Order |
An arrangement whether chronological or spatial – not preferential or developmental (refer to Grade) |
Description |
An explanation expressed using words, phrases and sentences |
Proposition |
A mutually exclusive distinction between clear choices |
Grade |
A variables denoting preference or developmental achievement expressed in integers, percentages or text |
State |
A description of condition whether temporary or permanent |
Link |
A hypertext connection |
Time |
An expression of chronology expressed in minutes, second, days, etc… |
Language |
The language used to define a concept or a relation |
Type |
A differentiation of genus |
Table 3. Attributes (14 Attributes in v3.0, last updated August 16, 2015)
III. Relations
Below is a list of all standard relationship labels (158 as of Aug, 2015) connecting different Concepts:
Abort |
Adopt |
Aggregate |
Affect |
Allow |
Allow for |
Analyse |
Append |
Approve |
Arrange |
Assemble |
Assess |
Analyse |
Audit |
Authorise |
Build |
Buy |
Capture |
Cause |
Certify |
Check |
Choose |
Classify |
Complete |
Collaborate with |
Collate |
Collect |
Commission |
Communicate with |
Compare |
Conduct |
Confirm |
Construct |
Consult |
Contact |
Contain |
Continue |
Control |
Coordinate |
Decrease |
Delimit |
Deliver |
Demolish |
Demonstrate |
Deselect |
Design |
Detect |
Determine |
Describe |
Develop |
Discover |
Divide |
Discuss with |
Document |
Draw |
Educate |
Empower |
Encourage |
Enforce |
Engage with |
Establish |
Estimate |
Exchange |
Explode |
Extract |
Evaluate |
Fabricate |
Facilitate |
Federate |
Follow |
Forecast |
Function as |
Gather |
Generate |
Guide |
Has part |
Has resource |
Identify |
Ignore |
Implement |
Improve |
Incentivise |
Increase |
Inform |
Initiate |
Innovate |
Integrate |
Interchange |
Interview |
Invent |
Involve |
Join |
Know |
Lead |
Link to |
Locate |
Maintain |
Make aware |
Make |
Maintain |
Manage |
Maximise |
Measure |
Merge |
Minimise |
Model |
Monitor |
Notify |
Observe |
Operate |
Own |
Participate in |
Perform |
Plan |
Populate |
Prepare |
Prescribe |
Prioritise |
Procure |
Produce |
Prove |
Provide |
Provide for |
Pull |
Push |
Qualify |
Quantify |
Question |
Receive |
Recommend |
Regulate |
Reject |
Replace |
Require |
Review |
Revise |
Run |
Sample |
Select |
Share |
Simulate |
Size |
Start |
Stop |
Supply |
Survey |
Test |
Track |
Train |
Transfer |
Transform |
Transmit |
Understand |
Update |
Use |
Validate |
Verify |
Visualise |
Warn |
Write |
Table 4. Relations (158 Relations in v3.0, last updated August 16, 2015)
IV. Knowledge Sets
Knowledge Sets are higher order Knowledge Objects composed of the other three lower order concepts, relations and attributes.
|
Name |
Description |
Example |
1 |
Knowledge Foundations |
A structured view of concepts and their relations. Knowledge Foundations include dictionaries, classifications, taxonomies, models, frameworks and theories |
The BIM Ontology, The BIM Framework, Granularity Levels, Organizational Scales… |
2 |
Knowledge Blocks |
A self-contained knowledge item used to build larger knowledge structures |
A competency item, dictionary item, model use… |
3 |
Knowledge Tools |
An interactive view of concepts and their relations intended to assess, assist and educate its users. A tool has modifiable variables leading to varied outputs based on inputs |
A calculator, an online tool, a cad software… |
4 |
Knowledge Workflows |
A repeatable set of activities conducted as part of a larger process to deliver a measurable outcome |
An assessment methodology, a knowledge capture technique, a construction method, a verification routine…. |
5 |
Knowledge Views |
A delimited self-contained view of multiple concepts and their relations - irrespective of knowledge content format (text, images or graphs) or knowledge content medium (hardcopy or softcopy) |
A training manual, journal article, CAD drawing, poster, web page, video, concept map, repertory grid, process map, concept map, flowchart, Gantt chart… |
Table 5. Knowledge Sets (4 sets in v3.1, last updated July 28, 2016)
Publication Log - Summary
V. |
Date |
Description |
1.0 |
18 Oct ‘07 |
Initial version submitted as part as a research proposal at the University of Newcastle, NSW |
1.2 |
6 Dec ‘08 |
Published as Table 6 within Paper A2 |
2.0 |
13 Dec ‘13 |
Published as Appendix A of the PhD Thesis |
3.0 |
16 Aug ’15 |
First published on BIMframework.com |
3.01 |
29 Apr '16 |
2 new Concepts added, 1 Concept modified |
3.02 |
28 Jun '16 |
5 new Concepts added |
3.03 |
2 Jul '16 |
1 Concept modified |
3.10 |
28 Jul' 16 |
1 Concept added, 1 Knowledge Set renamed |
3.11 |
21 Aug' 16 |
2 Concepts added |
3.12 |
23 Jan' 2017 |
Removed the ‘active’ tone from all Relations. Added 1 Concept + 2 Relations. Modified 1 Concept |
References
Cottam, H. (1999). Ontologies to Assist Process Oriented Knowledge Acquisition (Draft). Retrieved from http://www.inovexadvancedsolutions.ltd.uk/spede/default.htm
Gruber, T. R. (1995). Toward principles for the design of ontologies used for knowledge sharing? International journal of human-computer studies, 43(5-6), 907-928. Retrieved from http://www.sciencedirect.com/science/article/B6WGR-45NJJDF-K/2/b47f5cb67315c76b60ac39f44e0a2cec
Milton, N. R. (2007a). Knowledge Acquisition in Practice: A Step-by-step Guide: Springer, London.
Milton, N. R. (2007b). Specification for the General Technological Ontology (GTO). http://www.pcpack.co.uk/gto/notes/files/GTO%20Spec%20v4.doc Retrieved from http://www.pcpack.co.uk/gto/notes/files/GTO%20Spec%20v4.doc
Noy, N. F., & McGuinness, D. L. (2001). Ontology Development 101: A Guide to Creating Your First Ontology. http://www.lsi.upc.edu/~bejar/aia/aia-web/ontology101.pdf Retrieved from http://www.lsi.upc.edu/~bejar/aia/aia-web/ontology101.pdf
Shanks, G., Tansley, E., & Weber, R. (2003). Using ontology to validate conceptual models. Communications of the ACM, 46(10), 85-89. doi:10.1145/944217.944244
Studer, R., Benjamins, V. R., & Fensel, D. (1998). Knowledge engineering: Principles and methods. Data & Knowledge Engineering, 25(1-2), 161-197. Retrieved from http://www.sciencedirect.com/science/article/B6TYX-3SYXJ6S-G/2/67ea511f5600d90a74999a9fef47ac98
Succar, B. (2009). Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in Construction, 18(3), 357-375. Retrieved from http://dx.doi.org/10.1016/j.autcon.2008.10.003
Succar, B., Sher, W., & Aranda-Mena, G. (2007). A Proposed Framework to Investigate Building Information Modelling Through Knowledge Elicitation and Visual Models. Paper presented at the Australasian Universities Building Education (AUBEA2007), Melbourne, Australia. http://aubea.org.au/ocs/viewpaper.php?id=15&cf=1