Ontology in the Core of Information Management

Information Management in Infrastructure Building

Irina Peltomaa

and Esa Viljamaa

VTT Technical Research Centre of Finland, P.O.Box 3, FI-92101 Raahe, Finland

VTT Technical Research Centre of Finland, P.O.Box 1100, FI-90571 Oulu, Finland

Keywords: Information Management, Semantic Technologies, Ontology Development, Process Control, Infrastructure

Building.

Abstract: Information explosion sets challenges for companies but on the other hand offers opportunities to achieve

competitive advantage through successful information management. Semantic interoperability solutions and

ontologies especially, offer a powerful tool for information management. Ontologies provide a way to

integrated disparate information sources in complex environment. Large scale infrastructure building

process is extremely challenging assignment which successful follow through requires tool for effective

information management. Earlier research presented a prototype implementation called Dynamic Site

Control Centre (DSCC) for road construction process management. In this paper the formation and structure

of ontology for the prototype implementation is described.

1 INTRODUCTION

Earlier the gaining of information was on the focus

in information utilize; now the management of

information is the most important issue. The amount

of available information is enormous and still

growing; finding the right information from the vast

amount of data is the key issue in information

management. Kings of the hill are those companies

and organizations that are able to find the essential

information from the information flood, and utilize it

in a way the others cannot. This requires seamless

communication between men and machines, and

interoperability between systems.

Using semantics of data provides a powerful tool

for information management. Semantic

interoperability focuses on enabling content, data,

and information to interoperate with software

systems outside their origin (Pollock and Hogdson,

2004), and enables integration of data sources using

different vocabularies and different perspectives on

data (Ram and Park, 2004). Ontologies provide a

way to define semantics, provide support for

handling disparate data sources and provide a

mechanism to define complex knowledge models

(Zimmermann et al., 2005).

In complex environments mere technical

integration is not enough; information integration is

required (Pollock, 2001). Large scale infrastructure

building process is extremely challenging

assignment: several parallel and consecutive tasks,

complexity of structure, many companies involved,

technological variety, etc. Although research and

technological development have brought new tools

for leading such a process, there are still things to do

in information management of infrastructure

building.

In the domain of architecture, engineering, and

construction (AEC) and facilities management (FM)

lot of research is conducted to enhance the

information management. Due to diversity and

uniqueness of AEC/FM domains building an

universal solution or standard has been challenging

(Turk, 2006); (Venugopal et al., 2012), and the

development and deployment of systems integration

and collaboration technologies are behind other

sectors (e.g., manufacturing) (Shen et al., 2010). One

of the most recognized standardization efforts in

AEC/FM domain is Industry Foundation Classes

(IFC), but it has been criticized for the slow

adaptation in the real life (Howard and Björk, 2008).

Current solutions for information management in

AEC/FM domain are mostly made for building of

buildings and there are only few solutions for

infrastructure construction. Due to clear need for

enhanced information management in infrastructure

161

Peltomaa I. and Viljamaa E..

Ontology in the Core of Information Management - Information Management in Infrastructure Building.

DOI: 10.5220/0004438701610168

In Proceedings of the 15th International Conference on Enterprise Information Systems (ICEIS-2013), pages 161-168

ISBN: 978-989-8565-59-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

building industry Dynamic Site Control Centre

(DSCC) for road construction process management

was developed (Viljamaa et al., 2012); (Viljamaa et

al., 2013). This paper describes the formation and

structure of ontology for prototype implementation.

2 STATE OF THE ART

During past year’s information and communication

technologies have been developed and deployed

widely to various application areas; including

AEC/FM. Boddy et al., (2007) have made an

extensive review from data and application

integration research in construction domain, and

Shen et al., (2010) have conducted a comprehensive

review on system integration technologies in

AEC/FM. In the following the concepts of

interoperability and ontology are examined, and

some construction side ontologies and standards are

explored.

2.1 Semantics

Semantic interoperability is the ability of

participating system domains to understand the

meaning and use of terminology from different

domains, and to map between agreed concepts in

order to make a semantically compatible information

environment (Park and Ram, 2004). It also enables

data exchange between applications and multiple

applications to jointly contribute to the work at

hand; leading to smoother workflows and sometimes

to facilitated automation (Eastman et al., 2011).

Semantic interoperability focuses on enabling

content, data, and information to interoperate with

software systems outside their origin (Pollock and

Hogdson, 2004). Therefore semantic interoperability

enables integration data sources developed using

different vocabularies and different perspectives on

data. To achieve semantic interoperability, systems

must be able to exchange data in such a way that the

precise meaning of the data is readily accessible and

the data can be translated by any system into a form

that it understands (Ram and Park, 2004).

Interoperability is comprised both technical

integration and information integration. The main

technical challenge is the lack of interoperability of

different systems and data sources thus most of the

current solutions are focused only on technical

integration, to link disparate software systems to

become part of a larger system. Information

integration is focused on preserving the meaning of

information while transforming the context.

Metadata must include human-defined context and

business rules in addition to typical metadata to fully

enable system interoperability. (Pollock, 2001)

Interoperability-based approaches focus on the

exchange of meaningful, context-driven data

between autonomous systems, concentrating on

exchanging minimal amount of information

(Pollock, 2001). Interoperability is considered as

achieved only if the interaction between two systems

can, at least, take place at the three levels: data,

resource and business process with the semantics

defined in a business context. This leads to

achieving interoperability on multiple levels: inter-

enterprise coordination, business process integration,

semantic application integration, syntactical

application integration, and physical integration.

(Chen and Doumeingts, 2003)

2.2 Ontology

Implementation of semantic interoperability requires

enhancing of data with the semantics, mapping and

combining the information by reasoning and making

the information available to the users personalized

according user needs and preferences. These tasks

require specific methodologies and tools. One

prerequisite of semantic interoperability is use of

ontologies.

Ontologies aim to capture consensual knowledge

in a generic way to be reused and shared across

software applications and by groups of people

(Gomez-Perez et al., 2005); (Gruber, 1995).

Ontology defines a common vocabulary for

information sharing in a domain (Uschold and

Gruninger, 1996); (Noy and McGuinness, 2001) and

it includes machine-interpretable definitions of basic

concepts in the domain and relations among them

(Noy and McGuinness, 2001).According to

Zimmermann et al. (2005) ontologies provide a way

to define semantics, provide support for handling

disparate data sources and provide a mechanism to

define complex knowledge models. Semantic

interoperability can be ensured by providing

contextual knowledge of domain applications (Ram

and Park, 2004).

Several researchers have compiled instructions

for developing ontologies (Gruber, 1995); (Uschold

and Gruninger, 1996); (Studer et al., 1998); (Noy

and McGuinness, 2001). In this research ontology

development follows the guidance introduced by

Noy and McGuinness (2001).

In order to successfully use ontologies

commitments has to be made, that are agreements to

use the shared vocabulary in a coherent and

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consistent manner. (Gruber, 1995) Also reusability

of the ontology is important feature of the ontology.

To achieve the requirements set by the reusability

the ontology must consists of small modules with a

high internal coherence and a limited amount of

interaction between the modules (Studer et al.,

2000). In this research the ontology is forming of

sub-ontologies, which define sub-processes of the

infrastructure building processes i.e. design process

and work progress.

It will rarely be the case that a single ontology

fulfils the needs of a particular application (El-

Diraby and Kashif, 2005); (Antoniou and van

Harmelen, 2008); (El-Gohary et al., 2011); more

often than not, multiple ontologies will have to be

combined. In the case of multiple ontologies,

ontology integration is challenging and important

task. (Antoniou and van Harmelen, 2008) The

techniques for ontology integration include

matching, mapping and alignment (Rebstock et al.,

2008).

In general there are three ontology architectures

to integrate information from heterogeneous

information sources: the single ontology approach,

the multiple ontology approach, and the hybrid

approach. Single ontology approach use one global

ontology for providing a shared vocabulary for the

specification of the semantics; all information

sources are related to the one global ontology

(single-shared ontology). In multiple ontology

approach, each information source is described by

its own ontology and source ontologies are mapped

to each other using inter-ontology mappings (one-to-

one). In hybrid approach the semantics of each

source is described by its own ontology, which is

built upon one global shared vocabulary or ontology

(mixed). (Uschold, 2000); (Wache et al., 2001);

(Alexiev et al., 2005); (Pradhan et al., 2011)

In this research, single ontology approach for

ontology utilization is used; each source is

preserving its own ontology and global, shared

ontology is constructed to act as common

vocabulary.

2.3 Standards and Applications

There have been numerous efforts to build AEC/FM

standards during past decades. The developments of

standards have been made in Europe and in America

(Eastman et al., 2011) and individual countries have

made their own standards (Kosovac, 2007). The key

problems in standardization have been low stage of

actual implementation and deciding the level of

generalization (Kosovac, 2007).

Recently, Building Information Modelling (BIM)

has been considered as an important enabling

technology for building lifecycle information

integration. It can facilitate collaboration among

stakeholders during the design, construction, and

maintenance of buildings and facilities. (Shen et al.,

2012)

BIM tools serving the AEC/FM industry cover

various domains and have different internal data

model representation to suit each domain. There is

no one single application that can provide the entire

set of functionalities required for the AEC/FM

industry. Yet there is a clear need for data exchange

between various actors in order to integrate the

various types of expertise needed to realize the

overall project. (Venugopal et al., 2012)

In BIM interoperability has traditionally relied

on file-based exchange formats limited to geometry,

and direct links based on the Application

Programming Interfaces (APIs) are the oldest and

still-important route to interoperability. Data models

were developed to support product and object model

exchanges within different industries. Data models

e.g. IFC, distinguish the schema used to organize the

data and the schema language to carry the data. The

major benefits of interoperability are not only to

automate an exchange (although replicating the data

in another application is certainly redundant

activity), but the larger benefits that refine

workflows, eliminate steps, and improve processes.

(Eastman et al., 2011)

However, Howard and Björk (2008) state that the

formal standards on BIM, such as the IFCs are

complex and have not had the resources for rapid

development and promotion that their potential

deserved. Therefore it will take some time for this

approach to be widely adopted. (Shen et al., 2012)

El-Diraby et al., (2005) presents domain

taxonomy for construction. The taxonomy is based

on IFC and several other classification systems. The

operation of developed domain ontology was

evaluated during e-COGNOS project (Vallejos et al.,

2007) as a part of web based knowledge

management software, which connected various

systems using Web Service technology. The

development of ontology architecture was continued

by adding more knowledge levels (application

knowledge, user knowledge) to domain ontology

(El-Diraby and Kashif, 2005).

In Finland determined work has been done to

develop the utilization of information models in

planning, construction and maintenance in

infrastructure building. The aim is that all big

infrastructure owners demand information model

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based services from year 2014 onwards. To support

this purpose an information transfer format

Inframodel (IM) has been developed. IM is based on

international LandXML standard. (InfraBIM, 2013)

In this research the design process sub-ontology is

based on the IM2 definition.

3 CASE DIGIINFRA

Viljamaa et al., (2013) introduces a prototype

implementation of DSCC, which purpose is to

enhance the information acquisition during road

construction process. DSCC integrates information

from different companies’ information systems

participating to road construction process. It

combines and refines the information gained and

visualizes the information for users, primarily to

construction work managers. The information

integration is done utilizing the semantics of

information, and ontologies. This chapter introduces

the development and structure of the ontology for

DSCC, and gives some examples of the use of the

ontology.

3.1 Overall Structure of the Ontology

DSCC ontology forms of sub-ontologies, which

define sub-processes of the infrastructure building

processes. Sub-ontologies include design process,

work progress, resource management, and user

management. Work progress ontology is the central

part of DSCC ontology, and it contains information

from several road construction process participants.

As ontology description language OWL Lite is

used and the ontology is created using Protégé

(Protégé, 2013) ontology editor. The ontology

structure and process data is stored to triplet

database to enable convenient deployment of the

data. As triplestore Sesame (OpenRDF.org, 2013)

framework with OWLIM (OWLIM, 2013) semantic

repository extension was used. As query language

SPARQL 1.1 is used.

3.2 Design Process

Design process sub-ontology is based on IM2

(InfraBIM, 2013); (Inframodel, 2013) information

transfer format, which is based on LandXML

standard. IM2 defines the geometry plan for the

infrastructure to be built. The model enables the

designing of different kinds of infrastructure, like

route design, road- and street design, railway design,

and waterway design. It also provides way to design

water supply and sewerage, surfaces, and

landscaping. DSCC prototype implementation

concentrates on route design phase.

Every route has one continuous horizontal

alignment, and it is made up of geometric elements

and stinglines. Route design consists of stringline

model and surface description. Alignments are

collections of geometric lines and stringlines. The

stringline model of the route is composed of

alignment descriptions depicted in stringlines

presented in layers. Alignment is an element, which

describes either geometric line or stringline.

Geometric line consists of horizontal geometry,

which is defined with lines, curves, and spirals; and

corresponding vertical geometry squared with

horizontal geometry, defined with points of vertical

intersection and circular curves. The surface of route

is defined with triangulation network. (InfraBIM,

2013)

Figure 1: Ontology snippet of design process sub-

ontology.

In IM2 the relations between different elements

of the design are described hierarchically. In DSCC

ontology the IM2 hierarchy was transformed to

‘isPartOf’ and ‘hasPart’ object properties. The

foundation class of design process sub-ontology is

Plan. Plan has some data properties, like date, and

time; and object properties, like

hasAlignments,

and

hasCoordinateSystem. In Figure 1 a snippet

of design process sub-ontology is depicted. Object

properties link formed individuals to traceable

chains. Plan has object property

hasAlignments

which connects Alignments class to Plan.

Correspondingly Alignments has object property

isPartOfPlan, which connects Alignments to

Plan. Plan may have none or one Alignments, and

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Alignments have one Plan.

Since IM2 geometry plan is XML formatted, it is

transferred to ontology format using XSLT

transformation simultaneously when the IM2 file in

question is selected in DSCC as a project geometry

file. In practise, the IM2 DOM is accessed using

SimpleXML PHP extension. The generated file is

imported in triplestore.

3.3 Work Progress

The development of work progress sub-ontology

was done from scratches. During specification phase

of the research several interviews were conducted

for the personnel of the companies participating road

construction process. The base structure of work

progress sub-ontology is described in

Figure 2.

Figure 2: Work Progress ontology snippet.

The natural way to describe the road construction

process seemed to be project based, so the

foundation class of work progress sub-ontology is

Project. Project class is connected to one plan

individual. According to definitions in plan, the

structure of the road is created to Project. In road

design the road is divided in smaller sections to ease

up the handling of the information of the road

design. The section of road called structural pole,

can be e.g. 100 meters long. Road is also divided

lengthways in structure layers according to different

mass layers forming the road. In DSCC ontology an

intersection of a structural pole and structure layer is

called PoleXLayer. This enables the separation of

structure layer for different tasks. Example of

PoleXLayer definition can be found from

Figure 3.

Figure 3: Task structure and PoleXLayer definition.

According to plan, StructuralPoles,

PoleXLayers, and StructureLayers are created.

Project is divided in smaller unities, Tasks, in which

the actual work is allocated. Task is connected to

certain StructureLayer, and to certain

StructuralPoles.

Figure 3 presents an example of this

connection. The road has been divided in thirteen

StructuralPoles and it has six StructureLayers.

Cutting-layer is only removal layer, marked with

pink; all the other layers are additive layers. The

work in StructureLayer BaseStructure is divided to

be done in two tasks (Task 3 and 4). The work in

Task 3 is allocated to StructuralPoles 1 to 4 and in

Task 4 to StructuralPoles 5 to 13. During road

design a certain mass is allocated for every pole in

every layer. In

Figure 3 this mass for the referenced

PoleXLayer is 39.16 tonnes.

The information about masses related to

PoleXLayers is got from design system.

Unfortunately, the used data format for road plan

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import (IM2) does not yet support direct structure

layer volume or mass definition, so the separate text

based layer information file was exported from the

used design software. The text file contains

information of each structure layer per each

structural pole pair i.e. for one PoleXLayer. The

information includes pole coordinates and ids with

Finnish building standard layer type and volume of

each layer. The essential information is parsed in

DSCC using separation characters like line feeds and

spaces. Example snippet of layer information for one

structural pole pair:

Code: Name: Unit: Quantity:

Group: Paaluväli: 0.00-10.00

1611 Maaleikkaus, eritt. m3ktr 54,09

1817 Luiskatäyte m3rtr 0,40

2100 Pääl.rak.osat,alusr.krkst m3rtr 2,38

2111 Suodatinkerrokset m3rtr 27,65

2131 Sitomattomat kant. krkst m3rtr 12,89

2141 Asfalttipäällysteet m2tr 41,42

2161 Piennartäyte m3rtr 0,13

2321 Nurmikot m2tr 31,39

2411 Tukikerrokset sorasta m3rtr 0,18

Coord: 65,057569264,25,451983081

From the basis of this mass information

StrucruralPoles and PoleXLayers are formed using

SimpleXML PHP extension and imported in

RDF/XML format to triplestore.

The creation of project is done using DSCC

prototype’s graphical user interface (GUI). The

project creation phase includes creation of project

individual, importing design information, and

importing mass information. Also information about

tasks and resources connected to tasks received from

project management software can be imported to

DSCC prototype. Resource and task scheduling

information is imported during project creation

process. Only supported file format is MS Project

XML format that is parsed using SimpleXML PHP

extension. The feature makes it possible to reuse

existing plans that can usually be converted to MS

Project format in many corresponding tools. The

tasks mentioned in scheduling are added to project’s

tasks and resources mentioned are created

accordingly. Resources are connected to

corresponding tasks according to project schedule

management information.

Tasks and resources can also be created using

DSCC prototype GUI. The user feeds the

information in user interface and a new user or

resource is added to triplestore using triplestore

interface module.

3.4 Resource Management

The development of resource management sub-

ontology was also done from scratches. The data to

this sub-ontology is mainly coming from work

machine information systems, so the model was

formed on the basis on this information. The base

structure of resource management sub-ontology is

described in

Figure 4. The foundation class is

Resource, which contains common data properties

for all resource. There are two kinds of resources

differentiated from the base resource: WorkMachine

and TransportEquipment. Load is connected to

TransportEquipment; this class contains information

about the quality of cargo (sand, stone, crush,

concrete) and the amount of the cargo (in tonnes).

Figure 4: Resource management ontology snippet.

Machine control system is integrated to DSCC

prototype using XMPP protocol, which is used by

project partner’s commercial machine control

system. DSCC receives real time information about

WorkMachine’s e.g. excavator’s location and state.

The DSCC import client reads essential excavator

data from the XMPP multi-chat group where

excavators publish their status data, and updates data

to the semantic database. The excavator data

includes position, status and used control model.

Information about TransportEquipment is

uploaded directly to triplestore through mobile web

GUI. Uploaded data contains e.g. location of

transport equipment and information about load in

transportation like mass volume and type. The

mobile web GUI uploads and requires information

from triplestore using triplestore interface module.

During construction process the information

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about the realization of mass transportation is

imported to DSCC prototype. DSCC is calculating

the readiness of tasks and project by comparing the

realized mass to planned mass. The planned mass for

task is calculated from the mass information of

PoleXLayers connected to task. The ratio of realized

and planned masses is changed to percentages and

visualized to user in GUI. The readiness of the

project is calculated from the readiness of tasks

correspondingly.

3.5 Usage of Ontology in DSCC

The base structure of the ontology and all the

runtime information used in running the DSCC

prototype is stored in triplestore. The

communication between DSCC prototype and

triplestore is done through triplestore interface

module, which forms SPARQL queries according to

information received from DSCC. SPARQL enables

execution of complex queries quite simply without

several queries or complex nested queries as is

needed in case of Structured Query Language (SQL)

and relational databases. The used triplestore

implementation Sesame supports also SPARQL

update which enables updating of stored information

very easily.

The DSCC prototype consists of six GUI views,

which contain information about projects, tasks,

locations, users, resources, and process status. The

GUIs are implemented using up-to-date web

technologies which enable device and application

independent development and use. According to user

requirements DSCC fetches and presents the

information stored in triplestore. (Viljamaa et al.,

2013)

4 CONCLUSIONS

Information interoperability and information

management are complex problems which several

research teams have tried to solve in various

application domains, including AEC/FM. The

diversity and one-of-a-kindness of AEC/FM have

hindered of development of information

management solutions. Information models tend to

grow wide and complex which slows down the

adaption, like in case of IFC.

In this paper an ontology development and

structure for enhanced information acquisition

prototype in infrastructure construction process is

introduced. The DSCC prototype aims to intensify

the information management during road

construction process using semantic interoperability

tools, e.g. ontologies. The conducted research

concentrates on describing the data semantics of the

infrastructure construction process. The idea of

ontology development was to keep the accuracy of

ontology on suitable level in order to keep the

structure as simple as possible. The developed

ontology was divided to sub-ontologies according

road construction processes. Some of the sub-

ontologies were developed from the basis of existing

information models and some from scratches. The

chosen ontology architecture was single ontology

approach, which can be seen as the first stage of

integration implementation. The following step

could be the use of global ontology with local

ontologies, where local ontologies describe the

structure of data source currently fused to global

ontology using parsers.

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