an industrial resource generation in semantic web - Semantic Scholar

OntoSmartResource: An Industrial Resource Generation in Semantic Web Oleksiy Khriyenkd, Vagati Terriyan2 Industrial Ontologies Group. MIT Department, University of JyvLkylB. P.O. Box 35. FlN-40014, IyvsskylA. FINLAND, e-mail ’olkhiye@cc,jyu.fi. ’vagan @it.jyu.fi Abstmct . Semantic Web is a logical evolution of Ihe exisling Web. It was meant to serve Tor machines as today’s Web does Tor humans. Tho lerm “mchine~”according to Ihe existing Semantic Web’s vwabulary mostly means “mmputers”. However induslry needs such appliealiom, which eomider mcbines also w embedded eomputalianal entities wilhin field devices, p n o n a l devices, microwave overs, etc. I n olhor words, now we should involve the real (industrial) world objects as mourees into Semantic Web. Still the main object of such B world will bo a human, which becoming B resoume (not jwt a u e r oT resources) in the distributed environment. In this paper we inlraduce an extension a t the Semantic Web wourcos to a new generation of !he e n h a n d smil Semanlic Web resource (OntoSmrtResources). We consider following aspem as: agent-driven r m u r e e behavior mechanism, resource semantic descriplim and minlenanm, and Some aspels of the human m m m s relaled lo represenlalion and adaptation.

h d e r Terms Semanlic Web, Ontology, ComputerHuman Interlace, Agents, Penonalization ~

I. INTRODUCUON The Semantic Web [ I ] is a logical evolution of the existing Web. It is based 00 a c m ” n conceptual data model of great generality lhat allows both humans and machines to work with interrelated. bul disjoint, information as if il is a single global database. The goal of Semantic Web development is promotion of existing Web to qualiWtively new and higher level, utilizing machineprocessable metadata asaciated with Web resources. Next generation of intelligent applications will he capable to make use of such resource descriptions and perform resource discovery and integration based on its semantics. Semantic Web approaches to develop a global environment on top of Web with interoperable heterogeneous applications, web services, dala repositories, humans, e c . This time consuming search and navigalion Wsk has to be performed by a user and there i s no easy way to automate it. Databases and catalog information are generally being hidden behind HTML tabular representation or some digest page [21. The software applications and web-services are also available in a complex form This makes il tedious, if possible at all, to integrate heterogeneous decentralized resources for a user. Semantic Web aimq to aulomale the information discovery and integration. Compuler program (applications, services, agcnts) will be able 10 find and navigate today’s Web resources autonomously. Cumnlly, domains of shared and reusable Web content [7] and Intelligent Web Services (which might mean assume becoming uncenain. with a contert~dependcdand even uncennin behavior) (41 are in focus of Semantic Web

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Activity and semantic lechnology applications and correspondent ontologies develop most rapidly here. However for indushal adoption of Semantic Web technology these efforts seem 10 he no1 enough. In our opinion, the problem is initial orientation of s e m t i c technology development 10 World Wide Web digital resources. This resulted to omission from consideration of other industrial domain resources: devices, processes and even humans. From Ihe traditional Srmantic Web point of view, “machine” means a set of applications, web-services, agents, etc. Instead of previous Semantic Web’s meaning. nowadays “machines” can be also considered in the form o f embedded computational entities, such as e.g. intelligent parts of the field devices [XI. We should involve the objects of the real industrial world into Semantic Web. Of course, a mi” object of the real world was and is a human, which also musl be a resource (not just B user) of B global semantic enabled environment. C o m p e e r - H u m Interaction (CHI. also abbreviated a HCI, for human-computer interaction) is Lhe study of the ways that people use computers. It is also the very intense pnctice of making computers easier for people to use. W e however think that there is time already to study the ways the computers use people and how to make computer able to use people easier. A human in such context is considered as an “Intelligent Web-Service”, which can be “invoked by an external application and utilized e.g. as an intelligent “query- answering machine”. To provide such possibility it would be necessary to consider special type of interfaces (Computer-Human Interface YS. Human-Computer one). Such interface should be able 10 visualize to a human an application query in B way helshe underswnds and then, after query is answered according to a human manner, it should he coded back to the application understandable format. Consideing such services as resources of Semantic Web means the problem of utilizing existing approaches (e.g. OWL-S [3]) for annotating Web-Services based on ontology also to human-services. Also 811 other concerns of lhe Web~Scrvice community related e.g. to service discovery and integration might obtain specific features when adding human resource~toconsideration. In this paper WE introduce an extension of the Semantic Web resources to a new generation of the enhanced smartresources of Semantic Web (OnIuSmarIResuurces). Concerning the resource extension and enhancement we describe fallowing aspects as: agent-driven resource behavior mechanism, resource semantic description and maintenance, and some aspects of the human r e ~ o u r c e ~ related 10 representation and adaptation.

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11. ONTOSMARTRESOURCE-A SMART RESOURCEOF THE SEMANTIC WER

What is the Sman-resource? Traditionally resources were considered as active and passive. A possivc resource just provides access toward itself. An actiw rtsource additionally can use other rcsourccs to support ils functional destination (find goal). If resourcc changes its content (changes iuelfj or its state is changed by the environment during the execution. then such resource becomes a dsnomic re~ource(in relevance to its content). Consider as an example a website, which automatically refreshes its information by accessing and using information from other sites; or some Web service based on neural network classifier. which incrementally l e a n s from external learning samples: or an industrial field device (which also can be considered as a Web resource adapted to the Web environment via intelligent soilware component (e.g. semantic wrapper) and p e r f o m predictive maintenance function, activation of the maintenance activities and etc.), whose parameten can be changed etc. I n these cases the necessity of some actions. which are concerned about these changes. arises. Such actions can he, for example: notification of other resources, which depend on changed resource's contenl, new advenising in case of the new charactenstics emergence: activation of the resource maintenance processing; etc. These actions are important for resources (system?), which are depended on information from other resources. There arc systems. such as: human resourcc management systems of the company or any other unions (research groups, clubs, leam), which are concerned with people or their acliuitics: all kinds of clusters (formation based on some common features of the components), which totally depend on a component's change: slatistical systems (customers cluslerization) or customization systems. especially for marketing environment. Sometimes a resource content modification (change) bnngs with it also a resource semantics change. I n this EBSC. most imponant aclion would be automated changing of the resource's semantic description (Semantic Mainrenance OJO resource). Now we may define the smar-resource. Smart-resource is a proactive dynamic resource, which sufficiently and proactively reacts on changes within iu ertemal environment or within itself. A . Resourcc Brhavior Regarding the idea. which was born as part of the OntoEnvironment conccpl [S, h ] , the resource managemenl agent shell (called OntoShrll) can be extended with a proactive goallbehavior interpretation module ResourreBPho,,iorAgrnt. Resour~!~SehoviorAgenl- is a resourcc management agent with programmable goals (behavior). It interprets assigned behavior rules and perform? actions concerned tu them. Such N I ~ S may be assigned in way "IF=>THEN rules (in simple case). where the conditions and actions are described according 10 common onlology. This ontology must contain main terms of cundilions and actions describing for all types of the resources. On the same level with inlerprctalion mechanism the REsourcrBrhnvir,rAgent must to be supplied with deducing (drriwlion) mcchanisni

based on assigned rules. This mechanism provides a possibilily IO simplify and combine the rules for effective deducing. Also. such mechanism can reveal contradictory (conklicting) rules. Feedback coupling use afler the aclions performing plays very imponant role. This information about consequence of the made decision allows behavior N ~ Scomction. Especially such mechanism is very important in case of automated replenishment and modification of the rules set during the experience (behavior N ~ S ) exchange between the resources. Such possibility to learn during the resource's life is additional advantage of the smart-resources. Concerning the organization (formation) of the ResourceBehaviorAgent, we need to have a favorable user interface for assigning of h e behavior rules. Such interface must provide optimal convenience for human during the rules assigning using necessary ontologies. For providing required convenience, such user interface must be dynamic and must adapt for each concrete lype of the resources. conditions and actions. In this case, we have to develop not only ontology for behavior describing (conditions and actions), but also develop ontology for description of the terms presentation from "behavior" ontology.

B. Semantic Maintenance oJa Resource

As was mentioned before. a resource semantics can he changed with a resource content modification. According to that one should have a duty Io change the semantic description of the resource accordingly. ResCJurce Description Semantic Maintenance System (RDSMSJ is a system (a set) of the mobile or static services, which automatically make (create, modify) new semantic desctiplion based on U changed rcsoume conlent. parameters, preferences, actions, etc (sea Fig.1.). A large amount of digital information available is wntten as text documents in lhe form of web pages, reports, papers, descriptions. emails, etc. Extracting the knowledge of interest from such documents from multiple sources in a timely fashion is therefore crucial. Depending on the class (type) of the resource. a set of such services offered by Resource Description Semantic Mointenonce Ssslem may be different and may be composed automatically to more complex services whenever necessary. Let us consider some research article as a sample of a resource, and then the semantic maintenance system can use such component services as: domain qualifier of the article content: counter of the words, pages, etc.: additional informalion qualifier (authom. contact addresses. referenccs); etc. Such services should be developed i n compliance with a set of resource characteristics. Interaction between these components and the resource will be organized through main semantic maintenance service. which uses the set of available internal services or finds external ones far making required descriptions. Such kind of alliance between a msource and the Resource Description Semantic Maintenance System can be organized to behave based on a community goal of a closed set of functioning resources (componenu). which compose ii IS]). The system can be organized for both private (individual) and shared use by a number of the resources brlonging to some class (type).

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Resdes-

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Semantic h4aintenance Center Fig.1. Resource Description Semantic Maintenance System - Semantic Maintenance of a resource. 111. HUMAN AS A SMART-RE~OURCE IN THE SEMANTIC W E B

Formerly, human was just a user of other resour~csin the Net, and was not rcganled as a resource in Semantic Web itself. However as a matler of fact, human is very active and dynamic resource. According to a modem vision of the Semantic Web Environment. humans can semantically discover and utilize no1 just an electronic document via a browser, but also they can discover and access any S m a n (connected via sensors) resource from the real world. At the same time, human is an intelligent object (resource), which can be useful for other resources (other humans or even software applications) as a seenice (expsn in SQCCifiCdomain) or an information source. That is why. we think reasonable to consider human as a resource (webservice or agent), which can be semantically discovered in the Web, queried and used by both any resource of vinual world (application, scwicc, and agent) and resources of real world (humans, smn-devices. etc). There are many emerging industrial applications, which depend on such resource. It makes a sense in new automated industrial environments where motivated by an embedded intelligent system man-device perform maintenance activities via use of domain-mimtcd maintenance services and humanexperts. Human will be motivated lo be a a web service especially in business environment where hrlshe can get money or other benefits by own knowledge and capabilities utilization. Of course, in this case. human should bc cenified as n web-service and should he responsible for hisher decisions and xlivitics. A. Human Adopialion

When we are talking about fluman Adaptation. it means adaptation in hoth human-software and roflwart-human directions. A human component must be connected IO the appropriate Weh service environment via software interface

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(HumanOnloAdapar). which should he able to react and take into accounl all changes (modifications) of a resome (human). It must have flexible intelligence far automatic registration of a change and. on the other hand, unobtrusive request interface to a human it represents. Interaction between (be HumanOnloAdapler and a human must be organized in understandable form for a human and partly in a way of Semantic conversation (semantically rich natural language ryuesu, answers. etc.). Involving such human representative into automated resource integration environment opens a new market for the producers and providers of the personal mobile devices. It gives a passibility for easy physical connecting of a real world resource (human) with its ~pr~sentative from vinual world. Why is a physical contact so imponant? Human can change himself in any moment of hisher life: change hidher prcferenccs during buying, ordering. renting something; or during any other aclivities. That is why, it is very imponanl that a personal assistan1 takes a pan in these activities. Of COUTSE, human c m change information related lo hindher via other points of acccss: during changing the information on a website, during filling some form, etc. In this case, automated synchronization of all these access ohjecfs plays very imponant role far combining and stonng of the HumnResource information. In other words, related to a human information should be collected via any personal devices, sensors, applicalions which are used by human in hidher real life. B. Ontology perronaliwfio" Concerning the user inlerfJCe for a human some problems emerge. rspecially in cases of interactions with: personal human component, which represents human in the integrated OntaEnvironment: any other scrvice. which has a human interface; tools, which deal with ~ n t ~ l ~ gHow i e ~to. constmct ontology to enable interoperability among humans in a handy (comfortable) way similarly as

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ontology. which meant to provide interoperability between applications'! There are some specilics. People are very different in many reasons: 0 Linguistic (language) and cultural difference. 0 Different notion concerning one (same) entity. a Different perception of the incoming infornution. 0 Etc. This problem i s related also to the business environment, to e-commerce domain. It emerges, because intemal information representation of each enterprise differs from others. Therefore, we cannot demand usage of common (standardized) language from different players to manage their own information. One solution of this problem is Ontology Personoliwtion. Ontology Personalimtion means development of the support mechanism for double-sided ontology. Each player will be able to create personal ontology, or in other words helshe can describe each object from common ontology (or often used pan of it) in t e m of own presentation way (language. terms, notion. etc.) Ontology Personalization would be a human adaptation on the Semantic Layer. Thus, we will have a double-sided ontology. From the one side we have a common ontology. which is used by any semantically enabled resources (elements of Ule OntoEnvironment). And from the other side we have B personal ontology in a concrete player understandable form. I n this c a ~ ewe need Some mechanism for ontology interpretation (translation) from both sides of double-sided ontology. The goal of such Onrologylnterprerer is two-forked interpretation of the notion concepts on the input and output of the human user interface (Fig.2.). There arc many different a-pecU. of the ontology personalization: 0 Lanvunve ond rems difference Each player has own language and may have own names of the terms (slang). In this case. Ontology Personalization mechanism allows an interaction between a player (smart-software or human) and OntoShell (adapter for a software resource or human interface) an existing personal language of the resource. Thus. in such way we can resolve the problem of the player's language heterogeneity. Personalization on this level can be realized with utilizing e.g. vocabulary of synonym. 0 Meoninas difuence of Ihe' some enrity The same entity can have different meanings for the different players depending on a domain of a player's activity. It does not mean that a player never uses some meaning of B notion. It means that a player uses the notion with apprOpkle distribution of probabilities of its meanings. Consider an example, when entity "Driver" has a set of Ihc meanings: a name of the movie, special software for a device. a car driver. special equipment for the golf. and a game (race emulator) (Fig personalization layer, a vocabulary of the multiformity with a usage factor and mechanism of the context definition play the main role. 0 Temnoral rxnansion of concents Sometimes, during the long term. a player uses a notion. which defines not just some object or entity, but also a set of its properties and relations (tics) with other

entities. For example, planning a h p includes ordering travel tickets (kind of transportation vehicle. convenience, price range, etc.). an apartment ordering (also with specifying a number of properties). etc. Such set of the notion's atuibutes may he used hy the player dunng a long term of its life, and mean to sewe as one personalized notion. Personalization on this level also can be used for improving a semantic search performance and a simple search query is assumed to be semantically enhanced with the Ontology Personalizmion mechanism facilitation. 0 Personalized reores~ntariono f informarion Ontology personalization plays a significant role in generating a personalized view at the information for each player. A player prefers to get and set information in a suitable way for (hidher). as better for (hisher) perception. Thus, there is a need to develop ontology of information rcpresentation and personalize it for each player. This information representation ontology is a basis for personalized dynamic human visual interface. which will usc personal user semantic-profile. Such Ontology Personalization mechanism must he in a close interaction with a global enhanced player's profiling system. The player's profiling system will provide access to the personal ontology independently no a way of the connection to the OntoEnvironmrnt (wired or wireless connection). Concerning all mentioned aspects of the ontology personalization we see, hat an Onrolog.ylntupreter is a complicated and compound module of the human adaptation mrchanism (HumanOntoAdapter). Onrologylnrerprerer must be supplied with a special intelligent and human-friendly tool for personal ontology creation and training. I t i s not obligatory to create a complete personal ontology if a player uses just part of it, which concerns a specific domain of hisher activities, or when storage space is restricted, e.g. in a personal mobile device. For ovemnning of the available part of ontology, an ontology swap-in mechanism (OntologyCaching) can be used (Fig.4.). C. Ontolog>,Persowlirotion Learning

Development of a dynamic flexible user interfaces plays one of the most important mles for the Ontology Personalization. Such interfaces have to provide a possibility to create. change and leam a personal ontology. With a goal to provide more suitable way to manage a personal ontology. each application must be supplied with such interface.

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Fig.2. Ontologylnterpreter - ontology pursonrlilation module.

Fig.?. Ontology Personalization - Heterogeneity of meanings.

"Language and term difference" aspect and "personalized representation of inlomation" may hc managed by creation and changing of a personal onlology. However, "a variety of the same concept meanings" and "temporal expansion of concepa" aspects of an ontology personalization require a special learning mechanism based on stored information related to a player (human). Ontology Personalization Learning helps human interfacesIO become intelligent enabling human presence in an OntoEnvimnment more easy and comfortable.

Iv. CONCLUSION In this paper we have extended lhe usual set of Semantic Web resources lo B new generation of the enhanced s m resources (OnloSmartResources). We considered following aspects as: a resource behavior mechanism and a Resource Description Semuntic Maintenance System for an OntoSmanResource. Each of such resources is supplied with a behavior mechanism (behavior agent) and due to it can be considered as a smart-resource. A support of B resource description semantic maintenance system opens B new possibilily far lhe dynamic resources 10 he proactive and do (ssmanlic) maintenance of own (meladata) content when needed Also in this paper we considered B human as a kind of smart-resource.

We involved human as a resource (player) of a global integrated semantic environment and cunsidcrsd lhe aspcca related lo the human representation and adaptation mechanism. Especially we placed the emphasis on application of an Ontology Penonaliration mechanism to simplify interactions between players of lhe OntoEnvironment and increase their collabralive performance. The benefit of involving humans into aulomated resource inlegralion environmenl can he seen in lhe emerging market for producers and providers of lhe new applications for personal mobile devices. I1 gives a possibility for easy physical connecting B real world resource (human) with its represenlalive from a virtual world. In this case, it is very important that personal assistant takes pan in human activities. V. REFERENCES Semantic Web. URL: h S p : l i w w . w 3 . ~ ~ ~ i u x l l l ~ w l . @SEmintici S.R.L.. URL: h t t p : l l w w w . ~ ~ ~ m a n t i E r . c o ~ .

QWL-S: Semantic Markup for Web-Smics". ?he OWL Scrvics Coal", Deccmkr 2W3. URL: h t t p : l l w w w . d ~ ~ . ~ ~ P ~ r v i ~ ~ ~ w l ril.Oiowlkhiml. D. Fenscl. "Semvllic Web Scrvicm: A CommunicalionInfrastrucfurc for eWork and eCnnmercc". In: Profeedinps of Lhe ICWE 2003. Oviedo. Spoiin. LNCS 2722. Springer. 2003. V. Teaiyan. 0. Khriymko. "Mobile AgeWBnsed Web Service Componenfs in Senirntic Web". &wrm-t.'nropron JoumaI o/ Eoinptise lechnologiss. Vol. 2. No. 2. m.to appear. 0. Khriycko. 0. Kononenko. V. Tmiym. "OsoEnvironmenl: An lnlcgnlion Lnfnstrucfure for Disbibuted Helcropneaus Resources". In: IASTED Inlrmnnolwl Con/