SEMANTIC INTEROPERABILITY

Information Integration by using Ontology Mapping in Industrial Environment

Irina Peltomaa, Heli Helaakoski

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

Juha Tuikkanen

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

Keywords: Interoperability, semantic technology, ontology model, ontology mapping.

Abstract: Interoperability requires two components technical and information integration. Most of the enterprises have

solved the problem of technical integration but at the moment they are struggling with information

integration. The challenge in information integration is to preserve the meaning of information in different

context. Semantic technologies can provide means for information integration by representing the meaning

of information. This paper introduces how to use semantics by developing ontology models based on

enterprise information. Different ontology models from diverse sources and applications can be mapped

together in order to provide integrated view for different information sources. The domain area of this case

study is heavy industrial environment with multiple applications and data sources.

1 INTRODUCTION

Today business processes must be managed and

modified more effective than ever before. Processes

need to adapt to consolidation, mergers and

acquisitions, joint ventures, divestitures, regulatory

compliance issues, shifts in business models,

changing customer expectations, industry

standardization and business process outsourcing

(Smith and Fingar, 2002). Successful management

of a company requires overall management of all

processes and information connected to them. All

changes in business processes have influence on

several information systems in enterprises and

therefore seamless communication and integration of

data and information as well as synchronization of

inter-organizational business processes are complex

problems (Li et al., 2006). Enterprises are using

huge amounts of time and money to solve

interoperability problems of software systems. At

the moment enterprises are using 30 to 40 % of their

IT budget for integration problem (Gartner) and the

average time spent for integration of single interface

is 2 PM. The most worrying thing is that some

enterprises spend up to 80 % of their IT budget for

updating their legacy systems to integration and

extensions (Polikoff and Allemang, 2003). As the

enterprise collaboration is increasing al the time and

requiring more and more integration, the costs of

inadequate interoperability will be unsustainable.

Interoperability is comprised of both technical

integration and information integration. Most of the

current solutions are focused only on technical

integration, to link disparate software systems to

become part of a larger system while information

integration is focused on preserving the semantics

while transforming the context (Pollock, 2001). Yet

any moderately complex integration work requires

both types of integration. There is two strategies for

migrate system integration problem with the

enterprise: the development of an enterprise

message model as a reference point for flexible and

economic integration and the use of a semantic

broker so that each application would not have to

understand the semantics of every other application

(McComb, 2004). Today, enterprises are using the

message model as a technical integration solution

although the architecture still has many unsolved

problems like laborious configuration and

differences in the information interpretation between

designers. Examples of such technical solutions that

use common transportation layer for integration are

465

Peltomaa I., Helaakoski H. and Tuikkanen J. (2008).

SEMANTIC INTEROPERABILITY - Information Integration by using Ontology Mapping in Industrial Environment.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - DISI, pages 465-468

DOI: 10.5220/0001702504650468

 SciTePress

IBM WebSphere, Microsoft Messaging Queuing and

BEA ESB.

Information integration is based on semantic

interoperability that emphasizes the importance of

the information and focuses on enabling content,

data, and information to interoperate with software

systems outside their origin (Pollock and Hodgson,

2004). The aim of semantic interoperability is to

provide the ability to bridge semantic conflicts

arising from differences in implicit meanings,

perspectives, and assumptions for co-operating

enterprises, thus creating a semantically compatible

information environment based on the agreed

concepts between different business entities (Park

and Ram, 2004). The basis for ontology-driven

knowledge management was formed in EU projects

On-To-Knowledge (On-To-Knowledge, 2008) and

continued in Semantic Knowledge Technologies

(SEKT) (SEKT, 2008) that researched the

development and exploitation of semantic

knowledge technologies.

This paper proposes the use of semantics for

solving the interoperability problems above

technical integration. The presented work is

conducted in Sebi-project (Sebi, 2008) that

concentrates on solving interoperability problems in

industrial environment by using semantic

technologies and especially ontology-based

knowledge management. This paper presents the

architectural approach of using semantic

technologies in Sebi project. The presented approach

is tested in a case study of integrating information

from various sources in industrial production

environment. This paper gives a brief introduction to

the objectives of the case study and overviews the

basic elements of the development process.

2 CASE STUDY OF

INFORMATION INTEGRATION

Interoperability between systems can be supported

with semantic technologies by developing a shared

information model for the use of different solutions.

This model provides an integrated view for

heterogeneous data sources within a company.

Integrated view can be composed several ways

according to the requirements. It can support

business process management (BPM) or different

task-specific purposes within BPM like controlling

the delivery reliability or lead time. Semantic

models can also be used in enterprise collaboration.

Using semantically enriched data different parties

having divergence in ontologies are able to

communicate with each other; communication can

involve applications, information systems,

processes, companies in value-chain and humans.

The semantic interoperability research has

categorized three broad research areas: mapping-

based, intermediary-based, and query-oriented

approaches (Park and Ram, 2004). Mapping-based

approach attempts to construct mappings between

semantically related information sources while the

intermediary-based approach may also rely on

mapping knowledge established between a common

ontology and local schemas. Query-oriented

approach is focused on interoperable languages

which can be used for formulating queries over

several databases. Semantic architecture

methodologies are divided into three groups: one-to-

one mapping, single shared ontology and ontology

clustering (Alexiev et al., 2005). The methodologies

have different approaches of using global and local

ontologies, one-to-one paradigm uses local

ontologies alone, single-shared ontology use global

ontology without local ontology and mix of single-

shared and one-to-one mapping uses global ontology

with local ontologies (Bruijn and Feier, 2005).

According to Sebi view the interoperability can

be best achieved by combining all three different

approaches of interoperability: mapping-based,

intermediary-based and query-oriented. In this case

study we have used mixed paradigm with one global

and several local ontologies. The interoperability

architecture is presented in Figure 1.

Integration ontology

Common conceptual model

Various

concept models

Source data

DB 2DB 1

Source application

DB C

Mappings

Source data

DB DDB C

XML

Access A Access CAccess B

Business layer

Integrated access to

heterogeneous source data/

applications for users

(production managers, etc.)

Semantic Layer

Data Layer

Figure 1: Sebi interoperability architecture.

In data layer is the source data from

heterogeneous data sources and applications. The

source data is derived from various databases or

other electronic sources (eg. XML-files, ontology

ICEIS 2008 - International Conference on Enterprise Information Systems

466

files). Semantic layer represents the concept models

of domain information derived from various data

sources. Integration ontology is a common

conceptual model that defines key concepts of the

domain with related relations and attributes.

Integration ontology is developed with ontology

building tool in collaboration with domain experts.

The mappings between concept models and

integration ontology are defined with the same tool

in order to connect the source concept models to

integration ontology. Business layer offers different

views for integrated information according to user’s

need. The information is requested by executing

queries into integration ontology by using

middleware tool. Run-time implementation of

queries enables real-time access to distributed

information and supports for example decision

making in company management.

The case study describes the process of

developing interoperability for industrial

environment and the case enterprise operates in the

domain area of heavy manufacturing. The aim of the

case was to integrate the information used in

manufacturing process control. The enterprise has

several information systems (ISs) for controlling

manufacturing process; each IS having a totally

different concept hierarchy, database structures and

information models. This posed constant problems

for information management and furthermore caused

extra costs and loss of resources. The case involved

three ISs, one containing failure and breakdown

information, another IS controlling machines and

devices and the third IS needed the information from

the previous two. The operating environment of the

case is presented in Figure 2.

Production

system

Product

information

application

ooo

Production, maintenance, planning, etc.

Integration

interface

Event management

application

(measurement

analysation and

processing)

Automation

system

Production

system

Production

system

Measurement data

(production time)

Failure and

breakdown

information

Maintenance

information

application

Automation

system

Automation

system

Figure 2: Operating environment of the case.

The objective was to integrate ISs together in

order to offer a single view into machine, device,

breakdown, and failure information to enable

proactive maintenance in manufacturing process,

planning of maintenance work and controlling the

lead time of maintenance. The main requirements

for the solution were to enable frictionless

information sharing, flexibility of the integration and

effective maintenance of the integration.

The technical integration of the case was solved

by using a message broker that transmits information

between ISs, databases and different data sources. In

the semantic solution source ontologies from

different databases are modeled using ontology

engineering tool. In this case OntoStudio (Ontoprise,

2008) tool was used for developing automatically

source ontologies, designing integration ontology,

defining mappings between source ontologies and

integration ontology and creating intelligent

reasoning. The development of integration ontology

was also developed by OntoStudio. It was a very

demanding phase and it was done in collaboration

with experts from the domain area. The integration

defines shared semantics of the data and contains all

the important concepts with related attributes and

restrictions from the domain area.

The connections between source ontologies and

integration ontology were realized using mappings

and rules. When using mappings, the concept from

source ontology is connected to the suitable concept

in integration ontology. In complex cases, where the

concepts are not correspondent, the mappings can be

made by using reasoning. The reasoning may be

based on similarity of concepts and the meaning of

concepts. In this case the rules were created using F-

logic (Kifer et al., 1995) as an inferencing language.

Reasoning enables upper level mappings, where

rules deliver information to a corresponding place at

the lower level. This makes the maintaining of

mappings easy, because changes in upper level

cause changes in the whole concept tree. By using

rules the amount of mappings can be reduced and it

is easier to piece mappings together. Figure 3

describes a rule that get inputs from source and types

all failures which “FailureType_id” is one to

mechanical failure to the business ontology.

Integration ontology operates as a link between

different ISs by offering access to the information.

The information is queried through integration

ontology which provides access to information in

databases through source ontologies. The queries are

executed to the integration ontology using a

middleware tool.

SEMANTIC INTEROPERABILITY - Information Integration by using Ontology Mapping in Industrial Environment

467

Figure 3: Rule for reasoning the type of the failure.

3 CONCLUSIONS

System interoperability is complex problem which

has become unbearable for enterprises in dynamic

environment. Constant integration work consumes

time and money that enterprises could otherwise use

for development of new systems. System integration

and the maintenance of existing point-to-point

integration solutions are consuming lion’s share of

the companies’ IT budget. By using semantics the

prerequisites to solve the interoperability problem is

significantly increasing.

This paper presents our approach of semantic

solution. During the research different architectures,

methodologies and tools for semantic

interoperability were examined and the most suitable

alternatives where chosen. Chosen ontology

architecture allows flexible adaptation to the

changes which take place in system combination.

The interoperability between systems is

implemented through integration ontology. The

information from existing systems is modelled isto

concept models that are mapped to integration

ontology. Integration ontology offers real-time

access to information in integrated systems for users

and systems. It also offers effective tool for human-

to-human communication. The functionality of the

semantic solution was examined in case for

manufacturing system interoperability.

The development of semantic solution is still on

its early stage. The development requires new

working methods, because new technologies require

different approaches and the old working methods

are not necessarily suitable. The research will

continue by defining the time and costs of ontology

development. Also the technical side of the solution

and rule creation process still needs development.

During the research it was realized that the wide

adoption of semantics is still in future. Considerable

amount of research has been done in the area of

semantic interoperability, but real implementations,

especially in industry, are few. The adoption of

semantic technologies requires hard evidence of the

functionality in real-life cases and quicker

implementation pace. Whole process need to be

handled, mere technical solution is not enough.

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