35. Point of Adoption
37. Model Uses - Conceptual Structures

36. BIM Ontology

  BIM-OntologyFigure 1.  The BIM ontology v3.1 (Neuron Model v0.4, Updated July 28, 2016)

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. 

BIM-Ontology---2-Images
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

  

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