Geo-semantics and Ontology - Bridging AEC and GI systems

By Thomas Bittner

Bittner Ed.Note: This is the fourth installment to an 11-part series being published by Directions Magazine on the integration of CAD and GIS technology.Dr.Sisi Zlatanova is the editor of this series that will result in a book being published at the end of 2004.

One of the major problems facing systems for Architecture Engineering and Construction (AEC) and Geographic Information Systems (GI systems) applications today is the lack of interoperability among the various systems.In the process of integrating different software applications, substantial difficulties can arise when translating information from one application to another.Here we focus on semantic difficulties, which include cases where in two applications the same symbol is used to refer to different things and cases where different symbols denote the same thing.What is needed is some way of explicitly specifying the terminology of applications in an unambiguous fashion.Ontologies can provide such means.

In Computer Science an ontology is considered a specification of a conceptualization [11].Conceptualizations are ways in which humans understand and represent the world.In GI systems for example, we use concepts like parcel, highway, lake, etc.In AEC systems we use concepts like building, room, garden, backyard, etc.A specification then includes unique names for concepts (the vocabulary), and descriptions of the meaning of those names.The latter includes both concepts and relationships between concepts.

In this paper, we discuss how ontologies can be used to facilitate interoperability between AEC and GI systems.

On using ontologies
Jasper and Uschold [16] identify three major areas of uses for ontologies: (i) to assist in communication between human beings, (ii) to achieve interoperability (communication) among software systems, and (iii) to improve the design and the quality of software systems.Here we focus on (ii).

There are at least two different ontology-based classes of solutions to the problem of enabling different software applications to communicate: One is the standardization approach in which all applications adhere to a standard, which serves as a shared common ontology.As a result all applications which adhere to the standard use the same terminology in an unambiguous fashion.

In the second class of solutions ontology is used as an interlingua or reference ontology.In such a framework every application has its own specific ontology.Transformations of the vocabulary of every application into the vocabulary of the reference ontology and vice versa need to be provided.Applications with different internal ontologies then can interoperate by first transforming the statements (data) expressed in the vocabulary of the source ontology to statements (data) expressed in the vocabulary of the reference ontology, and from here to statements (data) expressed in terms of the vocabulary of the ontology of the target application.The transformations are performed in such a way that the meaning of the statements (data) is preserved.

When using an ontology as interlingua or reference ontology, two basic scenarios of generating the translations can be distinguished.Firstly, translations from/to the different software applications can be written by humans.Here the human beings writing the translations interpret the underlying ontologies.This is the state of the art today.In the second scenario the computer programs themselves are able to interpret the ontologies and perform the translation and communication process.This is the core of the dream of the Semantic Web [3,8].

It has been pointed out in the literature that the standard-based approach is useful in restricted domains and relatively homogeneous environments while the use of reference ontologies is more suitable in non-restricted domains and heterogeneous environments [6].

Data standards and reference ontologies for AEC and GI systems
To provide foundations for overcoming the historic distinction between AEC and GI systems and to facilitate interoperability between them, both classes of solutions, the standard-based approach as well as the reference ontology approach, can be exploited.

We argue that standardization will be successful in cases where both, AEC and GI systems, share common ground that can be made explicit in a shared common ontology.In our opinion, this will result in a large degree of standardization of the spatial components of AEC and GI systems.Already today data standards are applied quite successfully.Consider for example the body of standards that has been worked out within the OpenGIS community [7,2], or the norms set up by ISO [15].

Now consider the use of reference ontologies in facilitating interoperability between AEC and GI systems. In this context it is important to recognize that, strictly speaking, the spatial components of AEC and GI systems only specify the spatial locations of objects represented within those systems.Sharing of information about location is, from an ontological perspective, relatively simple.Sharing of information about non-spatial (or thematic) attribute data, however, is, from an ontological perspective, quite complex.This complexity arises from the need to specify the nature of the entities themselves and the need to take into account heterogeneity of the different kinds of things in the target domains of AEC and GI systems.

To achieve interoperability at the level of attribute data, we need to provide reference ontologies that are general enough to specify the conceptualization of entities that range from small-scale objects like cups or tables to large-scale human artifacts such as buildings, cities, highway systems, or political subdivisions, as well as natural phenomena of geographic scale such as rivers, lakes, wetlands, environments, or ecosystems.

These different kinds of concepts belong to different domain ontologies, e.g., ontologies of ecosystems, transportation ontologies, ontologies of real estate, etc. Domain ontologies describe the basic concepts of a particular domain. But ontologies can successfully facilitate the interoperability AEC and GI system attribute data only if they also provide an account of more fundamental notions that are assumed in all of these specialized domains. Ontologies at this level of generality are called top-level ontologies [17].

Top-level ontologies
Top-level ontologies provide the basis upon which domain ontologies are built.This is because, despite all the distinctions and the variety of different notions, all domain ontologies use the same top-level concepts and relations.Examples of those top-level notions include: categories like endurants and perdurants, which refer to different modes of existence in time [14]; mereological (part-of) relations [19,13], topological (connectedness) relations [21], and dependence relations.In addition, the notions of granularity and context are treated in top-level ontologies [4].Currently there are several different top-level ontologies under development.Two important examples are DOLCE [17] and Basic Formal Ontology (BFO) [10].Here we will focus on BFO but we could have chosen DOLCE equally well.

The most basic categorical distinction between entities at the top-level has to do with different modes of persistence through time.Two categories of persistent entities can be distinguished: endurants (objects) and perdurants (processes).Endurants are wholly present (i.e., all their proper parts are present) at any time at which they exist at all.For example, you (an endurant) are wholly present in the moment you are reading this.No part of you is missing.Endurants can change and yet remain the same.For example, all the cells in your body are replaced over a period of 10 years, nevertheless you are the same person today as you were 10 years ago.

Perdurants (processes), on the other hand, are only partially present at any time at which they exist - they evolve over time.For example, at this moment only a (tiny) part of your life (a perdurant) is present.Larger parts of your life - such as your childhood - are not present at this moment.

Understanding the distinction between endurants and perdurants is important when time and change are incorporated into information systems [18,10].The notions of process and change are critical in domains in which GIS has been used traditionally, for example in hydrology and in environmental science [9].To overcome the historical distinction between AEC and GI systems, both need to take into account the notions of object, process and change.Incorporating these notions into reference ontologies that provide a bridge between the two is the first step toward developing applications that go beyond today's capabilities of both kinds of systems.

Endurants are divided into two major categories: independent endurants such as cups, buildings, bridges, and highway systems, and dependent endurants such as qualities (the size of this cup), roles (the role a person fills when he is the janitor of a particular building or the president of a particular country), and functions (the air conditioner's function of cooling down the air).Here we focus on the former category.BFO distinguishes the following kinds of independent endurants: substances, fiat parts of substances, aggregates of substances, and boundaries of substances.

Substances are maximally connected entities, e.g., they have connected bona fide boundaries.These are boundaries which correspond to discontinuities in the underlying reality. For example, human beings and automobiles are substances.But neither your nose nor your arms are substances.Both are fiat parts of you, i.e., delimited (at least partly) by boundaries that do not correspond to discontinuities in the underlying reality, but rather to a human demarcation of parts in a continuum.Similarly, mountains are fiat parts of the planet Earth, or land parcels are fiat parts of the surface of the earth.Aggregates of substances are also independent endurants which are not substances.Examples of aggregates are: your family, the heating facilities in a given building, the water supply facilities in a town, etc.

Historically, CAD and thus AEC systems have been strong at modeling aggregates, while fiat subdivisions such as land parcels were modeled primarily in GIS.To overcome this distinction it is important to incorporate the notions of substance, fiat part, and aggregate into both systems as well as into reference ontologies that provide the bridge between them.


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Other installments published to date in this series:
Large-scale 3D data integration - An Introduction to the Challenges for CAD and GIS Integration
Bridging the Worlds of CAD and GIS - Part 1 of a Series on CAD-GIS
3D Data Acquisition and Object Reconstruction for AEC/CAD
In addition to the articles themselves, numerous readers have also posted comments on them (found at the bottom of each article).

Published Saturday, August 14th, 2004

Written by Thomas Bittner

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