Ontology-based Multi-Agent System to Support

8/3/2019 Ontology-based Multi-Agent System to Support

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ISSN 1392-8619 rint/ISSN 18223613 nin

http://www.tede.vgtu.lt

TechNologIcal aNd ecoNomIc developmeNT oF ecoNomY

Baltic Journal on Sustainability

2010

16(2): 327347

i: 10.3846/t.2010.21

ONOLOGY-BASED MULI-AGEN SYSEM O SUPPORBUSINESS USERS AND MANAGEMEN

Dejan Lavbi1, Olegas Vasilecas2, Rok Rupnik3

1, 3 University o Ljubljana, Faculty o Computer and Inormation Science,

Inormation Systems Laboratory, Traka 25, 1000 Ljubljana, Slovenia

E-mail: [email protected] (corresponding author);[email protected]

2 Vilnius Gediminas Technical University, Inormation Systems Research Laboratory,

Saultekio al. 11, 10223 Vilnius, Lithuania, e-mail: [email protected]

Received 11 January 2010; accepted 27 April 2010

Abstract. For some decision processes a signicant added value is achieved when enterprisesinternal Data Warehouse (DW) can be integrated and combined with external data gained romweb sites o competitors and other relevant Web sources. In this paper we discuss the agent-basedintegration approach using ontologies (DSS-MAS). In this approach data rom internal DW andexternal sources are scanned by coordinated group o agents, while semantically integrated andrelevant data is reported to business users according to business rules. Aer data rom internalDW, Web sources and business rules are acquired, agents using these data and rules can iner newknowledge and thereore acilitate decision making process. Knowledge represented in enterprisesontologies is acquired rom business users without extensive technical knowledge using user riendlyuser interace based on constraints and predened templates. Te approach presented in the paperwas veried using the case study rom the domain o mobile communications with the emphasison supply and demand o mobile phones.

Keywords: decision support, agent, multi-agent system, ontology, data warehouse, inormationretrieval, business rules, business process management.

Reerence to this paper should be made as ollows: Lavbi, D.; Vasilecas, O.; Rupnik, R. 2010.Ontology based multi-agent system to support business users and management, Technological andEconomic Development o Economy 16(2): 327347.

1. Introduction

Tere is a growing recognition in the business community about the importance o knowl-edge as a critical resource or enterprises. Te purpose o knowledge management is to
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328 D. Lavbi et al. Ontology based multi-agent system to support business ...

help enterprises create, derive, share and use knowledge more eectively to achieve betterdecisions, to increase o competitiveness and to decrease the number o errors. In order torun business eectively an enterprise needs more and more inormation about competitors,partners, customers, and also employees as well as inormation about market conditions,

uture trends, government policies and much more. Tere are several products and technolo-gies available on the market that support advanced Business Process Management (BPM)(rkman et al. 2007) and advanced decision support. Enterprises expect these applications tosupport wide range o unctionalities analysing customer proles, building and analysingbusiness strategies, developing customer-specic products, carrying out targeted marketingand predicting sales trends. Amount o documents in the Web, enterprise data repositories,and public document management systems with documents are rapidly growing. Tis hugeamount o data is managed in some extent, but knowledge workers, managers, and executivesstill have to spend much o their working time reading dozens o various types o electronic

documents spread over several sources in process o making decisions. Tere is just too muchinormation to digest in a daily lie. Te tremendous amount o documents that is still grow-ing has ar exceeded the human ability or comprehension without intelligent tools. Dierentapplications within inormation systems (IS) that support wide range o unctionalities needto be integrated in order to provide the appropriate level o inormation support. One o theprominent approaches or IS integration is the use o ontologies and Multi-Agent Systems(Fuentes et al. 2006; Soo et al. 2006; Dzemydien and ankeleviien 2009).

Te approach presented in this paper is targeted towards using ontologies or severaltasks, where emphasis is on using business rules (BR) approach or interoperability between

business user and IS. By introduction o BR approach business users do not have to be ullyamiliar with the technology to manipulate the common understanding o a problem domainin a orm o ontology and thereore enabling agents to execute dened analyses models. Teuse o ontologies in Multi-Agent System (MAS) environment enables agents to share a com-mon set o concepts about contexts, user proles, products and other domain elements whileinteracting with each other. Agents can exploit the existing reasoning mechanisms to inerderived contexts rom known contexts, to make decisions and to adapt to the environment,current status, and personal setting o the user. Te purpose o this paper is to present theapproach o integration o several inormation resources or Decision Support in Enterprises

using agent-oriented approach based on ontologies. Te goal o our research is to minimizethe gap between business users and agents as special type o application systems that perormtasks in their behal. Te intention was to apply BR approach or ontology manipulation inMAS. Ontology used in our Multi-Agent System or Decision Support in Enterprises (DSS-MAS) was divided into task and domain ontologies while business users were enabled tomanipulate them directly in a user riendly environment without requirement o detailedtechnical knowledge.

Te remainder o this paper is structured as ollows. First we present some backgroundin the ollowing section 2 with emphasis on agents, ontologies and related work with clear

denition o the problem and solution proposal. Next, in section 3, we introduce our casestudy o integrated Multi-Agent environment rom the domain o mobile communicationswith emphasis on architecture and the roles o agents and ontologies. Te case study is ocused


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329Technological and Economic Developmentof Economy, 2010, 16(2): 327347

in one o the mobile operators and urthermore oriented to supply and demand o mobilephones. Aer presentation o system architecture and decomposition o ontology o everyagent rom DSS-MAS will be presented in detail. Details o case study implementation willbe given in section 4. Finally the last section 5 presents conclusions.

2. Some background on decision support, multiagent systems and ontologies

Decision support systems (DSS) have evolved signicantly and there have been many in-uences rom technological and organizational developments (Shim et al. 2002). DSS onceutilized more limited database, modelling, and user interace unctionality, but technologi-cal innovations enabled more powerul DSS unctionality. DSS once supported individualdecision makers, but later DSS technologies are applied to workgroups or teams, especiallyvirtual teams. Te advent o the Web has enabled inter-organizational DSS and has given

rise to numerous new applications o existing technology as well as many new decision sup-port technologies themselves. Internet acilitates access to data, inormation and knowledgesources, but at the same time, it threatens to cognitively overload the decision makers. Authorsin (Vahidov and Kersten 2004) claim that internet technologies require a new type o deci-sion support that provides tighter integration and higher degree o direct interaction with theproblem domain. Based on that they propose a generic architecture or dynamic and highlycomplex electronic environments where DSSs should be situated in the problem domain.Chen et al. conducted an interesting research about integrated interactive environment orknowledge discovery rom heterogeneous data resources (Chen et al. 2001). Teir work is

grounded on acquiring, collecting, and extracting relevant inormation rom multiple datasources, and then orming meaningul knowledge patterns. Te proposed system employscommon DW and OLAP1 techniques to orm integrated data repository and generate databasequeries over large data collections rom various distinct data resources.

MultiAgent Systems (MAS) oer a new dimension or cooperation and coordinationin an enterprise. Te MAS paradigm provides a suitable architecture or a design and im-plementation o integrated IS, especially DSS. With agent-based technology a support orcomplex IS development is introduced by natural decomposition, abstraction and exibility omanagement or organisational structure changes (Kishore et al. 2006). Te MAS consists o

a collection o autonomous agents that have their own goals and actions and can interact andcollaborate through communication means. In a MAS environment, agents work collectivelyto solve specic enterprises problems. MAS provide an eective platorm or coordinationand cooperation among multiple unctional units in an enterprise. Te research on agentsand MAS has been on the rise over the last two decades. Te stream o research on IS andenterprise integration (Lei et al. 2002; Kang and Han 2003; ewari et al. 2003) makes theMAS paradigm appropriate platorm or integrative decision support within IS. Similaritiesbetween the agent in the MAS paradigm and the human actor in business organisations interms o their characteristics and coordination lead us to a conceptualisation where agents

1 OnLine Analytical Processing (OLAP)


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in MAS are used to represent actors in human organizations. Several approaches (ewariet al. 2003; Rivest et al. 2005; Kishore et al. 2006; Soo et al. 2006) deal with agent supportor integration and decision support. Research in (Kishore et al. 2006) has shown that MASparadigm provides an excellent approach or modelling and implementing integrated business

IS. Authors within that research proposed a conceptual ramework or MAS based integra-tive business IS. Some promising results were also ound in (Soo et al. 2006), where authorspropose a cooperative MAS platorm to support the invention process based on the patentdocument analysis. Te platorm allows the invention process to be carried out throughthe cooperation and coordination among soware agents delegated by the various domainexperts in the complex industrial R&D environment.

oday, semantic technologies based on ontologies and inerence are considered as a prom-ising means towards the development o the Semantic Web (Davies et al. 2006). In the eld oComputer Science and Inormation echnology (I) in general ontology has become popular

as a paradigm or knowledge representation in Articial Intelligence (AI), by providing amethodology or easier development o interoperable and reusable knowledge bases (KB).Te most popular denition, rom an AI perspective, is given in (Gruber 1993) as ollows:An ontology is an explicit specication o a conceptualization, where a conceptualizationis abstracted view o the world that we wish to represent or some purpose. Ontologies canbe considered as conceptual schemata, intended to represent knowledge in the most ormaland reusable way possible. Formal ontologies are represented in logical ormalisms, such asOWL, which allow automatic inerencing over them. An important role o ontologies is toserve as schemata or intelligent views over inormation resources. Tus they can be used or

indexing, querying, and reerence purposes over non-ontological datasets and systems, suchas databases, document and catalogue management systems. Because ontological languageshave a ormal semantics, ontologies allow a wider interpretation o data that is inerence oacts which are not explicitly stated. In this way, they can improve the interoperability o theconceptualization behind them, their coverage o arbitrary datasets. Ontology can ormallybe dened as specic sort o knowledge base and can be characterized as comprising a 4-tuple (Davies et al. 2006):

O = C, R, I, A .

Where C is set o classes representing concepts we wish to reason about in the givendomain (Oer, Finding, Phone, Customer, etc.). R is set orelations holding between thoseclasses (Message hasRecipient Actor). I is a set o instances, where each instance can be aninstance o one or more classes and can be linked to other instances by relations (Nokia isA PhoneBrand; Finding309 hasValue 11,23). A is a set oaxioms (I a new customer buys isA Nokia E72, promotional discount o 10% should be oered). It is widely recommendedthat knowledge bases, containing concrete data (instance data or ABox) are always encodedwith respect to ontologies, which encapsulate a general conceptual model o some domainknowledge, thus allowing easier sharing and reuse o KBs. ypically, ontologies designed toserve as schema or KBs do not contain instance denitions, but there is no ormal restric-tion in this direction. Drawing the borderline between the ontology (i.e. the conceptual andconsensual part o the knowledge) and the rest o the data, represented in the same ormal


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language, is not always trivial task. In our approach we include instances as part o ontologiesbecause instances we dene are a matter o conceptualization and consensus and are not onlydescriptions, craed or some purpose.

Related work on using ontologies in inormation systems or decision support is exten-

sive. Regarding the domain o DW and OLAP analyses research has dealt with DocumentWarehousing (seng and Chou 2006) where extensive semantic inormation about the docu-ments is available but still not ully employed as in traditional DW. Te use o ontologies wasound useul as a common interpretation basis or data and metadata. Furthermore researchhas extended to Web DW (Marotta, et al. 2002) with the emphasis on managing the volatileand dynamic nature o Web sources. Utilization o ontologies is also addressed in Inorma-tion Retrieval (IR) where it has been used or uzzy tagging o data rom the Web (Buche,et al. 2006, Macias and Castells 2007), query construction tool in semi-automatic ontologymapping (Suomela and Kekalainen 2006) and semantic based retrieval o inormation rom

the World Wide Web (Shan et al. 2003; Garces et al. 2006). Use o ontologies in Data Min-ing (DM) has also been considered in (Bernaras et al. 1996; Zhou et al. 2002; Singh et al.2003; Cao et al. 2004) where ontology was used or representation o context awareness andhandling semantics inconsistencies. Ontologies have been widely used or data, applica-tion and inormation integration in the context o domain knowledge representation (Qiu2006). Jovanovi in (Jovanovi and Gaevi 2005) concludes that the need or knowledgesharing and interoperable KBs exists and the key element or achieving interoperability aredomain ontologies. In that approach XSL2 transormation is used to enable knowledge in-teroperability. Authors in (Vasilecas and Bugaite 2006) use ontologies or ontology based IS

development and they address the problem o automation o inormation processing rules.Tere are also other approaches (Fuentes et al. 2006; Orgun and Vu 2006; Clark and McCabe2007) that use ontologies as knowledge representation mechanisms. Authors in (Clark andMcCabe 2007) use a ormal ontology as a constraining ramework or the belie store o arational agent. Te static belies o the agent are the axioms o the ontology, while dynamicbelies are the descriptions o the individuals that are instances o the ontology classes.Another work presented in (Fuentes et al. 2006) also uses heterogeneous domain ontologyor location based IS in a MAS ramework with the emphasis on context-aware MAS. Teypropose a global ontology to let agents work with heterogeneous domains using a wireless

network and the intention is to provide customization about dierent environment servicesbased on user location and prole.

2.1. Problem and proposal or solution

Te review o related work presented in this section pointed out that modern DSSs changedquite substantially especially with the advent o the Web and availability o extensive inor-mation in online repositories. For managing complexity and integration issues with deci-

2 XSL (XSL ransormations) is a declarative, XML-based language used or the transormation oXML documents into other XML documents


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sion support many approaches relied on MAS paradigm and used ontologies as knowledgerepresentation mechanism. Te existing approaches mainly ocused on either supportingexisting business processes or improving decision support at some level o detail or integrationo several structured resources to achieve better decision support. o our knowledge none

o the approaches addressed the problem o enriching data rom internal data sources withunstructured data ound on internet. Te interactivity o reviewed solutions is also limited;meaning that business users are usually limited to small set o parameters they can dene toalter deault behaviour o the system. Tese user requirements are usually entered directly intothe system and no abstraction layers are provided as in business rules management systems(BRMS) to enable users without technical skills to manipulate the content.

Tis paper introduces a novel approach in integration o unstructured inormation oundin the Web with inormation available in several internal data sources (e.g. database, DW,ERP3, etc.). Te MAS paradigm with agents was used or implementation purposes, mainly

because related work pointed out that it is a very appropriate solution or integration o busi-ness IS. One o the reasons to choose agents is also modelling notion where business usersand agents are modelled in a very similar manner. Problem o interaction between humanactors and computer programs is also addressed by introduction o ontologies as knowledgerepresentation mechanism. Te approach presented in this paper is targeted towards usingontologies or several tasks, where emphasis is on using BR approach to ensure interoper-ability between business user and IS. Ontology is used not only or every agent to representthe interpretation o a problem domain but also or communication between agents andbusiness users. Te use o ontologies in MAS enables agents to share a common set o acts

used in user proles, product descriptions and other domain elements, while interactingwith each other. With exploiting reasoning mechanisms new ndings can be derived rominitially known acts and improve the KB by extending it with new knowledge. o simpliythis communication template system based on BR was introduced to enable manipulationo knowledge within the system by users with less technical skills and to control behaviouro individual agent. Te approach will be urther explained in the ollowing section. Tecase study presented in this paper is rom the domain o mobile telecommunications. It ispresented in detail (with the impact it has on improving decision support within enterprise)in section 4. In the domain o mobile communications that was used or case study we had

to dene several tasks in DSS-MAS, needed or decision support OLAP analyses, DM, IR,context and prole denition, notication, etc.

3. DSSMAS

3.1. DSSMAS architecture

DSS-MAS that we propose in this paper is introduced in Fig. 1. Te case study presented inthis paper is rom the domain o mobile telecommunications and is based on business envi-ronment and inormation resources rom one o the mobile operators. DSS-MAS is situated

3 Enterprise Resource Planning (ERP)


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in the environment o several existing systems, like Data Mining Decision Support System(DMDSS), or DW and various resources available outside o an enterprise on the WorldWide Web. Global goal that agents in DSS-MAS should achieve is to support decision makingprocess while using existing systems or business analysis and employing inormation rom

environment where enterprise resides. o support this goal DSS-MAS includes several agentroles that are as ollowing: Data Mining Agent (DMA), OLAP Agent (OLAPA), Inormation Retrieval Agent (IRA), Knowledge Discovery Agent (KDA), Notiying Agent (NA)and Mobile Agent (MA). Te agents in DSS-MAS have both reactive and proactive charac-teristics. Reactive are mainly due to responding to the environment according to the modeldened in the KB they use. Proactive are due to their ability to learn rom the environmentand change the initially dened KB to or example improve perormance. Ontologies are usedas a main interconnection object or domain knowledge representation, agent-to-agent com-munication and most important or agent-to-business user communication. An important

element o an environment is the World Wide Web, where agents acquire inormation orthe purpose o decision making. Retrieved inormation is saved in a KB and available orurther employment or DM and DW analyses. All inormation gathered rom internal and

Fig. 1. Architecture o MAS used or Decision Support in Enterprises


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external sources is considered by KDA, where inerence over several task ontologies used byindividual agents (DMA, OLAPA, IRA, etc.) is perormed. Moreover the sub goal o DSS-MAS is delivering o the right inormation at the right time and to the right users. Te systemneeds to be context aware and to consider the relevant eatures o the business, i.e. context

inormation such as time, location, and user preerences (Liao et al. 2005). Business users inDSS-MAS are able to employ agents to perorm tasks on their behal. For example, manag-ers in enterprises have to request reports rom their systems OLAP or rom transactionaldatabases, and managers have to review reports every appointed period o time (day, week,month, etc.). Tis task o inormation acquisition is predecessor or decision making andis more or less straightorward business user sends a request or analyses and reviews thecontent according to some Key Perormance Indicators (KPI). KPI is simply a measure operormance and is commonly used in enterprises to evaluate how successul they are. InDSS-MAS tasks like this are automated and user participation is reduced as much as possible.

An initial analysis model (e.g. OLAP or DM) has to be captured in the ontology by businessusers, while execution and optimisation is le or agents. Business users rst dene initialparameters or analyses to be perormed, while agents perorm these analyses and recom-mend improvements. When some action is required rom business user, he is notied andhas the ability to act or change rules o agents execution.

o enable these unctionalities we introduce ontologies as a mediation mechanism orknowledge exchange between actors (agents and business users) that cooperate in DSS-MAS.Te ollowing section will present the structure and organization o ontologies we have usedor the case study.

3.2. Te role o ontology

According to Guarino (Guarino 1998), ontology can be structured into dierent sub-ontolo-gies upper ontology, domain ontology, task ontology and the application ontology. Followingsimilar guidelines we have dened upper ontology named Common ontologyand combineddomain and task ontologies in Notiying ontology, Inormation retrieval ontology, DataMining and Warehousing ontology(see Fig. 2). Te proposed clustering o ontologies isbased on the common understanding o the problem domain being dened in Common on-

tology. Every agent has its own interpretation o a KB, which is a specialization o a Commonontology with detail denition o knowledge required by individual agent. Common ontologyis limited to abstract concepts and it covers reusable dimensions, which are primarily usedby KDA. ask ontologies speciy concepts o notication, IR, DM and DW. Mobile commu-nications in our case is the domain o all task ontologies and the emphasis is on supply anddemand o mobile phones. As already mentioned, we have used the knowledge managementapproach in our research where every agent has knowledge about its own problem domain.In this case whenever new acts about the common knowledge are discovered, which mightbe o interest or other agents, they are updated to the common ontology.

Te role o ontology in our approach is thereore twoold: (a) knowledge representationmechanism used by agents and (b) common understanding o problem domain used orcommunication between business users and agents by utilizing business rules manipulationwith introduced templates (see section 4.2).


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with taxonomy o various warning levels and business users classication by organizationalunit and decision making level.

3.3. Te role o agents

Our case study uses domain o mobile telecommunications as a platorm where we ocus onthe sales o mobile phones and their accessories. Manipulation with internal data storage ishandled by two types o agents OLAPA and DMA. Tey both have distinct tasks but stillshare common goal periodically or on demand autonomously execute analyses models.Business users at rst dene these models and describe them with all required parameters (e.g.search or anomalies in sales o Nokia phones in last month period). Te inormation aboutthe execution is stored in the ontology (based on business user preerences) or is requestedby another agent o the system. Business user preerences in this context dene the execution

parameters about the analysis, or example the period at which the analysis is perormed (e.g.perorm analysis every other day at 13:00). OLAPA has on rsthand straightorward task operorming OLAP analyses on behal o an agent or a business user and reporting its nd-ings back to the requesting entity and all other entities that should be inormed, according tothe business policy. Nevertheless OLAPA does much more aer each execution it preparesthe report or business user based on ndings movements and KPIs. I certain nding issubstantially dierent rom nding obtained in previous case urther analysis is perormedto discover the reason o change by drilling down (more detailed) or up (less detailed) thehierarchies and levels.

Te knowledge is acquired in ontology. Business users can change the behaviour o agentsby changing the ontology using graphical user interace. Tis interace incorporates all logicalrestrictions dened in ontology and does not allow users to enter unacceptable values andthe most important is that it does not require technical knowledge rom users. Previous ourexperiences have shown that business users have great diculties especially with setting theparameters required to run DM and DW analyses models. So, user interace really has to beriendly and intuitive. In our approach this issue was solved by introducing the architecturedepicted in Fig. 3 and using templates as urther discussed in section 4.

Nowadays Web-based inormation retrieval systems are widely distributed and deeply

analysed rom dierent points o view. Te main objective o all such systems is to help us-ers to retrieve inormation they really need (obviously as quickly as it is possible) (Garceset al. 2006). While the techniques regarding DW, multi-dimensional models, OLAP, or evenad-hoc reports have served enterprises well, they do not completely address the ull scope oexisting problems. It is believed that, or the business intelligence (BI) o an enterprise, onlyabout 20% o inormation can be extracted rom ormatted data stored in relational databases(seng and Chou 2006). Te remaining 80% o inormation is hidden in unstructured orsemi-structured documents. Tis is because the most prevalent medium or expressing in-ormation and knowledge is text. For instance, market survey reports, project status reports,

meeting records, customer complaints, e-mails, patent application sheets, and advertisementso competitors now are all recorded in documents. For that reason in DSS-MAS we introducedIRA or retrieval o data mainly rom the World Wide Web. Te tasks that IRA perorms inpresented case study can be grouped into three categories:


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identication o new online shops,

analysis o mobile phones presented online and

extending Data Warehouse with inormation ound online.First two tasks are concerned about the supply o mobile phones at various online shops

worldwide. Identication o new online shops is conducted with web crawling and the useo several existing services on the Internet, such as Google, Google Product Search and Bing.Not only these internet resources are managed through ontology, but also rules or text ex-traction are dened as rules which make all domain knowledge available in IR ontology andnot encoded in agent itsel. More details about implementation o DSS-MAS case study canbe ound in section 4. Furthermore every shop ound online is analysed to identiy uniquepatterns or searching phones. Search patterns include guidelines or agents perormingsearch at various web pages. Tey are based on XQuery4 and regular expressions. o searchor phones at Google Products Search, the ollowing URL search pattern http://www.google.

com/products? q = ((\w +\s*) +) is used, accompanied with additional inormation or webscrapping o required inormation (e.g. price, availability etc.). Using these search patternsIRA is searching through online shops and determines phones with their market prices andstores this inormation into IR ontology to be available or urther knowledge inerence byKDA. Inormation o ound phones is used to determine new market trends, enable pricecomparison between competitors, acilitate possible inclusion in enterprises sales program,etc. One o the tasks that IRA also perorms is extending DW with inormation ound online.While business users perorm OLAP analyses, they deal with only internal inormation aboutthe business, but in process o decision making other resources also have to be examined,

e.g. news about the suppliers and competitors, opinions about certain products and organi-sations, etc. IRA thereore scans the DW dimension data (through hierarchies and levels)rom DW dimensional schema and uses this inormation or searching several internetresources (news archives, orums, stock changes, Google trends, etc.). When users reviewOLAP reports these data rom the Internet is also displayed according to their restrictionsin dimensions. For example, when business users are making decision whether to increasesupport to Nokia or Sony Ericsson phones it only has reports about sales o selected brandnames rom their market program. Using our approach the user is provided with additionaldata that is ound online and what will make decision better ounded. By this integration o

internal and external inormation users have integrated data source available that they canquery rom single location.

KDA is important element o DSS-MAS since it consolidates all ndings rom IR, DM andDW and urthermore delivers derived ndings to NA. o employ inerence capabilities overseveral ontologies the enterprises BR are essential. While business concepts are captured inontology, these concepts urther have to be restricted to dene specic meaning. GenerallyBR are prepared by business users and also some parts o BR in enterprises tend to changerequently; thereore we introduced architecture or BR management (see Fig. 3 and urther

4 XQuery is a query language that is designed to query collections o structured and semi-structureddata


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discussion in section 4). Findings o KDA are presented as instances oDomainspecifcelement and Findings classes (see ontology in Fig. 2).

As it can be seen rom Fig. 1 NA represents an interace to DSS-MAS or all external ap-plications and business users. Te main role o NA is the inormation dissemination by simply

delivering the right inormation at the right time to the right users. While in vast majorityo todays applications users have to request the inormation using so called pull model inour approach we implemented the push model, where inormation is proactively deliveredby agents to the user without a specic request. Tis is achieved by making system contextaware and considering the relevant eatures o the business, i.e. context inormation such astime, location, position in the organisational hierarchy, etc.

All knowledge about notication is dened in Notiying ontology, where every user hashis own context dened and the position within organisation across two dimensions or-ganisational unit (e.g. Marketing, Sales, Human resources, etc.) and decision making level

(e.g. Chie Executive Ocer (CEO), Chie Inormation Ocer (CIO), Chie Financial Ocer(CFO), Chie Marketing Ocer (CMO), Chie Analytics Ocer (CAO), etc.). According tothat position rules or delivery o several message types are dened. Tese message types rangerom Notication to Warning and Critical alert. Each message also addresses the domain ospecic organisational unit, e.g. when a new mobile phone is ound online at competitorswebsite, CMO and CAO have to be notied. Organisational structure, as part o Notiyingontology, also denes that both CMO and CAO are inerior to CEO thereore he is also noti-ed, but only in a case o a Critical alert. According to the business user prole, noticationcan be sent using several technologies rom Windows Alert, e-mail, Really Simple Syndication

(RSS), Short Message Service (SMS), etc. Tese notication types are also ordered by priorityor each business user and according to this type the content is also adapted.

Mobile agent is an example o an application that can reside on a mobile device (e.g. Per-sonal Digital Assistant (PDA), mobile phone, etc.) and uses resources o DSS-MAS throughNA. Te typical use case includes sending mobile agent across network to DSS-MAS, whereall needed inormation according to owner context is collected and then the mobile agentis returned back to originating location on a mobile device and presents the collected datato business user. When the process o acquiring data is in progress, business user does nothave to be connected to the network, he can just wait ofine until mobile agent is ready to

return with the ndings.In the ollowing section details about the case study implementation will be presentedwith technologies used, templates or business rules acquisition and presentation o onespecic scenario rom case study.

4. Case study implementation and discussion

4.1. echnology

Te selected language or ontology presentation is OWL DL (Russomanno and Kothari 2004),

since it oers the highest level o semantic expressiveness or selected case study and is oneo the most widely used and standardised ontology language nowadays that has extensivesupport in dierent ontology manipulation tools. Besides OWL logical restrictions, Semantic


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Web Rule Language (SWRL) rules were also used due to its human readable syntax and sup-port or business rules oriented approach to knowledge management (Horrocks et al. 2005).SWRL rules are stored as OWL individuals and are described by OWL classes contained inthe SWRL ontology. Te use o SWRL enables storing schema, individuals and rules in a

single component, which makes management much easier. SWRL rule orm in a combinationwith templates that is introduced in the ollowing subsection b is very suitable or knowledgeormalization by business users that do not have extensive technical knowledge.

Te ontology manipulation interace or business users is based on Protg OntologyEditor and Knowledge Acquisition System (Stanord Medical Inormatics 2006a) and SWRLab (Stanord Medical Inormatics 2006b) or Protg. It enables entering OWL individualsand SWRL rules where a step urther is made towards using templates or entering inorma-tion (see Fig. 3). At the execution level KAON2 inerence engine is used to enable inerencecapabilities. Due to limitation oSHIQ(D) subset o OWL-DL and DL-sae subset o SWRL

language, beore inerence is conducted, semantic validation takes place to ensure that allpreconditions are met. We selected FIPA5 compliant MAS platorm JADE6 in DSS-MAS be-cause it oers broad range o unctionalities and is most widely used platorm. Tis is due tovery good support and availability o agent ramework, where a lot o common agents tasksare already implemented (i.e. agent communication at the syntax level, agent management,migration o agents, etc.). For Mobile Agent implementation an add-on JADE-LEAP7 wasused to support the mobility o agents.

Fig. 3. Prototype o selected case study

5 Foundation or Intelligent Physical Agents (FIPA)

6 Java Agent DEvelopment Framework (JADE)7 Java Agent Development Environment-Lightweight Extensible Agent Platorm (JADE-LEAP)


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4.2. Mediation with BR templates

Using templates with ontology, business logic is excluded rom the actual soware codewhereas the majority o data or templates is acquired rom ontology axioms and natural

language descriptions in ontology, while other templates are prepared by users with techni-cal knowledge. Te main goal o using mediation with BR templates is to enable acquiringknowledge rom actual knowledge holders i.e. business users and enable transormation othis high-level knowledge into inormation system level, where this data together with con-cepts rom business vocabulary can be directly used or inerence purposes and bring addedvalue without any urther programming by technically educated users.

When acquiring new knowledge into the system rom business users, the process alwaysstarts with ocusing on concepts o business vocabulary that are persisted in a orm o ontol-ogy. Users can reely traverse through this inormation space, select concepts and urther

manipulate all related inormation within the selected context. Altering and adding newinormation is all time limited to ormal denition o concepts that is dened in ontology.For easier manipulation business user is aided with template and business vocabulary, so BRbuilding process is simplied as it will be presented in detail in the ollowing section.

Fig. 4 presents an example o BR template that is used or denition o aggregation ondings or domain specic elements. Te user interace that is available is directly linkedto ontology, where constraints on classes, properties and individuals are considered in real-time. Tis approach allows to minimize the risk o entering wrong constraints. Te DSS-MASsystem supports entering o new statements in several orms rom simple IF-HEN orm to

decision table or decision tree.

Fig. 4. BR template or general nding denition

IFCondition := { x : x Domain specifc elementFinding} |Condition| 1

HENResult := { y : yFinding} |Result| 1

Te ollowing example in Fig. 5 represents a BR that states: I there exist two consequentincreases o sold phones o the same phone brand and a new phone o this phone brand was

ound online within last 2 weeks, then oer a promotion discount o 10% on this new phone to

all new customers.

When constraint presented at Fig. 5 is transormed to execution orm at inormationsystem level, standardized SWRL and OWL languages are used to enable reusability (seeFig. 6). By this transormation a rule is produced that can be directly used in the inerenceengine to produce results in a orm o inerred triples that are presented to the user.


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4.3. Case study

One o the most common cases o using DSS-MAS system is combing inormation oundonline with BI reports (DW, DM, IR, etc.) developed on internal data in enterprise DW. Fig. 7presents one o this scenario.

Scenario presented at Fig. 7 is triggered by results o IRA activity, when three new mo-bile phones: Apple iPhone 3GS, Nokia E72 and Sony Ericsson Xperia 1 are ound by IRA atonline mobile shops. According to the execution policy rom Common ontology, OLAPAis notied with a request to rebuild all DW reports where brands o identied phones canbe ound in dimension elements. Aer running OLAP on Sales schema with constrains o

Nokia brand in Phone dimension and last year in Date dimension OLAPA creates a reportas depicted in Fig. 8.

IFFirst nding is Increase (Finding) which {

is related to rst amount sold which is Measure (OLAP element) ANDis related to rst date which is Dimension (OLAP element) ANDis related to rst phone which is Phone (Domain specifc element) which {

has characteristic brand which is Phone brand (Domain specifc characteristic)}

} ANDSecond nding is Increase (Finding) which {

is related to second amount sold which is Measure (OLAP element) ANDis related to second date which is Dimension (OLAP element) which {

isgreater than rst date} ANDis related to second phone which is Phone (Domain specifc element) which {

has characteristic brand which is Phone brand (Domain specifc characteristic)}

} ANDFound phone is New phone (Domain specifc element) which {

has characteristic brand which is Phone brand (Domain specifc element) ANDhas date o appearance ound date which is Dimension (OLAP element) which {

isgreater than now 14 days}

} ANDNew customer is New customer (Domain specifc element)

HENPromotion discount is Discount price (Finding) which {

is related to new customer ANDis related to ound phone ANDhasvalue 10 ANDhas unit %

}

Fig. 5. Example o a rule, developed by using template


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Fig. 6. Constraint presented in ontology in SWRL and OWL syntax

Fig. 7. Case study o using DSS-MAS in mobile phone domain


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Fig. 8. Business Intelligence ndings

[Phone] by brand [Nokia], by date [Q1, 2010][Amount sold], [risen by 11.23%][Phone] by brand [Nokia], by date [Last month][Amount sold], [risen by 5.87%]

At the inormation system level the rst nding is represented as an excerpt rom ontol-ogy and is depicted in Fig. 9.

Te elds that appear in the report are all instances oDomain specifc element, OLAPelement and Finding rom ontology (see Fig. 2). Aer these ndings have been asserted,KDA will be executed to derive new knowledge. Based on these new acts represented at Fig. 8and enterprise business rules (see example in Fig. 5), the KDA produced results representedin Fig. 10, by using inerence engine knowledge is asserted in ontology.

Aer consolidation o all new ndings KDA sends message to NA with request to orwardnotications to appropriate users. Te result o triggered activity o NA is the list o businessusers that have to be notied about this event. Te list shows that in this case CMO and CEO

Fig. 9. Example o representing the nding in ontology

Fig. 10. Derived nding

[New customer], [Nokia E72][Promotion discount], [10%]


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FACS

New [Phone][Nokia E72] is available on the market.[Amount sold] o[Phone] by brand [Nokia] and by date [Q1, 2010] have [risen by 11.23%]and by date [last month] have [risen by 5.87%].

CONCLUSION

[Phone][Nokia E72] should be oered to [new customer] and oered at [promotion discount]o[10%].

Fig. 11. Report o ndings with explanation

have to be notied whereas their context has to be considered. According to CMOs preer-ences an e-mail is sent with the ollowing content presented in Fig. 11.

Te CEO uses a Mobile Agent on his mobile device and is also notied by a truncatedmessage with new nding, while explanation is available upon request.

5. Conclusion

In this paper we have discussed DSS-MAS where internal and external data is integrated usingagent-oriented approach and ontologies as a common understanding o a problem domainand or communication between business users and agents. Agents were used due to theirmentalistic notions or modelling, similarities between the agent in the MAS paradigm and

the human actor in business organisations, and also possibilities or the use o ontologiesas means o agents internal knowledge base representation. Te external inormation romthe Web was integrated by IRA agent with the data in organisations DW and aer apply-ing BR new knowledge was derived by employing agents inerence capabilities. asks likeinormation retrieval rom competitors, creating and reviewing OLAP reports are autono-mously perormed by agents, while business users have control over their execution throughmanipulation oknowledge base. Te research also has emphasized agent-to-business usercommunication, trying to minimize that gap. Tis was accomplished by introducing dierentviews on ontologies or business user and agent. While agents deal with ormal description o

business concepts, logical constraints and rules, business user has a simplied view o ormaldescription o knowledge. User is able to manipulate with ontology through templates, wherelittle technical knowledge is required. Te mediation mechanism transorms these businesslevel concepts into ormal specication at the level o inormation system.

Presented approach was veried and implemented using a case study rom the domain omobile telecommunications, where the aim was to provide the knowledge worker an intel-ligent analysis platorm that enhances decision making process. Te application domain wasreduced to its sub domain dedicated or supply and analysis o demand o mobile phonesin one o the mobile operators. DW system is constructed rom several heterogeneous data

sources where majority o those sources are internal to the enterprise. Our approach addedinormation ound on the Web (i.e. competitors oers, stock rates, etc.) to these internal datasources and improved the decision support process within the enterprise. Te proposed ap-


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proach also addressed business users and their communication with the system which wassimplied by using templates to dene some business requirements that were transormedinto analyses models (OLAP, DW, etc.), automatically perormed by agents which reportedresults back to users in charge. Te case study presented in the paper was implemented in

Java and using mainly open source technologies.

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DAUGIAAGENINE SISEMA GRINDIAMA ONOLOGIJA VERSLOVAROOJAMS IR VADYBININKAMS PAREMI

D. Lavbi, O. Vasilecas, R. Rupnik

SantraukaPriimant kai kuriuos sprendimus reikminga pridtin vert pasiekiama, kai mons duomen saugyklagali bti integruojama ir sujungiama su iorini konkurent bei kitais svarbiais duomenimis, gaunamaisi interneto altini. Straipsnyje nagrinjamas tokio udavinio sprendimas taikant ontologija ir daugia-ageniu integracijos metodu grindiam bd, naudoya daugiaagentje sistemoje sprendimams rengti(DSS-MAS). aikant bd duomenys i vidini duomen saugykl ir iorini altini nagrinjami beirenkami koordinuojam agent grupi. Vliau semantikai integruoti bei atrinkti reikmingi duomenyspateikiami verslo vartotojams pagal galiojanias verslo taisykles. Kai verslo taisykls, duomenys i vidiniduomen saugykl ir interneto altini yra gauti, agentai naudodami iuos duomenis, gali ivesti naujasinias ir palengvinti sprendim rengimo proces. inios, vaizduojamos mons ontologijoje, gaunamos

i verslo vartotoj nekeliant jiems aukt isilavinimo reikalavim, nes naudojama nealinga ssaja, kurigrindiama prasmingais ribojimais ir i anksto apibrtais ablonais. Straipsnyje pateiktas bdas buvopatikrintas nagrinjant atvej i mobilij komunikacij dalykins srities ir pabriant mobilij teleontiekim bei paklaus.

Reikminiai odiai: sprendim primimas, agentai, daugiaagent sistema, ontologija, duomen sau-gykla, inormacijos irinkimas, verslo taisykls, verslo proces valdymas, DSS-MAS.

Dejan LAVBI. PhD, eaching Assistant. Inormatics department o Faculty o Computer and Inorma-tion Science at University o Ljubljana. He graduated in 2004 and received his PhD in 2010 at Faculty oComputer and Inormation Science at University o Ljubljana. He is author o 6 original scientic papersand more than 10 published scientic conerence contributions. His research interests are intelligent

agents, Multi-Agent Systems, knowledge management, ontologies, business rules and Semantic Web.His work is mainly oriented toward using Semantic Web technologies in business applications. He hasbeen involved in several research and commercial projects on strategic planning, methodologies or ISdevelopment, using intelligent agents and business process automatization and management.

Olegas VASILECAS. Pro. Habil. (hp) Dr Olegas Vasilecas is ull time proessor at the InormationSystems Department, and senior researcher and head o Inormation Systems Research Laboratory inVilnius Gediminas echnical University. He is author o more than 200 research papers and 5 books inthe eld o inormation systems development. His research interests: knowledge, including business ruleand ontology, based inormation systems development. He delivered lectures in 7 European universi-ties including London, Barcelona, Athens and Ljubljana. O. Vasilecas carried out an apprenticeship in

Germany, Holland, China, and last time in Latvia and Slovenia universities. He supervised 6 successullydeended doctoral theses and now is supervising 5 doctoral students. He was leader o many internationaland local projects. Last time he leaded Business Rules Solutions or Inormation Systems Development(VeIS) project carried out under High echnology Development Program.

Rok RUPNIK. PhD, Assistant Proessor. Bachelor o Science in computer and inormation science (1994).Master o science in computer and inormation science (1998). Ph.D. in computer and inormation sci-ence (2002). Author o several papers in international journals. Member o: ACM, IACIS (InternationalAssociation o Computer Inormation Systems) and PMI (Project Management Institute). PMP (ProjectManagement Proessional) certicate by PMI (2009). Research interest: project management, I govern-ance, strategic planning o inormation systems, inormation systems development methodologies, data

mining, decision support.

Ontology-based Multi-Agent System to Support

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