EMBET: Towards User Assistance, Collaboration and Knowledge Sharing *

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1 EMBET: Towards User Assstance, Collaboraton and Knowledge Sharng * Mchal Laclavík 1, Martn Šeleng 1, Ladslav Hluchý 1 1 Insttute of Informatcs, Slovak Academy of Scences, Dubravska cesta Bratslava, Slovaka laclavk.u@savba.sk Abstract. In ths paper we descrbe a User Assstant for collaboraton and knowledge sharng. Theory, mplementaton and use of such system are descrbed. The key dea s that a user enters notes n a partcular stuaton/context, whch can be detected by the computer. Such notes are later dsplayed to other or the same users n a smlar stuaton/context. The context of user s detected from computerzed tasks performed by user. The system s based on EMBET archtecture (Experence Management based on Text Notes). Other systems n applcaton envronment can also provde context senstve notes to be dsplayed for the user. Thus users can collaborate and fll up applcaton specfc knowledge base wth useful knowledge, shown to users n the rght tme. 1 Introducton The experence management [1] solutons are focused on knowledge sharng and collaboraton among users n organzatons. A lot of companes have recognzed knowledge and experence as the most valuable assets n ther organzaton [2]. Experence s somethng that s owned by people only, not obtanable by computer systems. Anyhow, accordng to the state of the art n the area we can a create experence management system, whch wll manage (not create) experence and wll be able to capture, share and evaluate experence among users. We can understand experence through text notes entered by a user. Such form of experence s the most understandable for humans, but t can be grasped by a computer system, though only partally. A computer system needs to return experence n a relevant context. Thus we need to model the context of the envronment and capture and store the context of each entered note. In ths paper we descrbe such soluton for the experence management based on text notes EMBET entered by users. The key dea s that a user enters notes n a partcular stuaton/context, whch can be detected by the computer. Such notes are later dsplayed to other or the same users n a smlar stuaton/context. The context of a user can be detected from many * Ths work s supported by projects K-Wf Grd EU RTD IST FP , NAZOU SPVV 1025/2004, RAPORT APVT , VEGA 2/6103/6, VEGA 2/7098/27.

2 sources actons provded n applcaton envronment, ntranet systems, portals, a step n a busness process or a workflow management system, used fles, emals receved or detecton of other events performed n the computer. The soluton was used and evaluated n the Pellucd IST project 1 and t s further developed n the K-Wf Grd IST project 2. The man objectve of the User Assstant soluton based on EMBET system 3 s to provde a smple and powerful collaboraton and knowledge sharng platform based on experence management nfrastructure, whch can be applcable n many areas. The dea s to return nformaton, knowledge or experence to users when they need t. Therefore t s crucally mportant to model and capture a user context and the descrbed soluton can be used only n applcatons where actons/tasks performed by a user are computerzed and can be captured and reported to the system n the form of events. The artcle frst dscusses a theoretcal approach of the EMBET soluton and ts archtecture, followed by examples gven for the Flood Forecastng grd applcaton. 2 Theory of the Approach The EMBET system s bult on several theores and dealng wth several theoretcal challenges: Experence Management, Context Modelng and Context Detecton, and Context Matchng 2.1 Experence Management Accordng to Bergman [1], the experence management s smply the capablty to collect lectons from the past assocated to cases. We have a person who has a problem p, whch s descrbed n a certan space, called the Problem Space (P). In the experence Management system we have Case-Lesson pars (c, l), where we have a Case Space (C) and a lesson space (L). To provde approprate lesson learned we need to fulfll the followng steps: 1. User context detecton from the envronment whch descrbes problem P 2. Our Model s descrbed by ontology and Notes are stored wth assocated context, whch descrbes space C 3. Notes represent learned lesson L whch s assocated wth space C (note context). The note context s matched wth a user problem descrbed by the detected user context. Context matchng technques are appled to fnd match between knowledge and user context. As a result all applcable notes are matched and returned. 4. The lesson s left to be appled by the user by readng approprate notes

3 More on applyng ths theory n EMBET can be found n [3] [4]. 2.2 Context Modelng and Context Detecton For context and also knowledge modelng we use semantc web approach ontologes and the CommonKADS [5] methodology. We are able to model and detect context when the applcaton doman s well modeled usng ontologes. Our model s an ontology model based on fve man elements: Resources, Actons, Actors, Context and Events. Other ontology models can be attached easly to the model where concepts from foregn ontology are specfed as context f not specfed otherwse. The man dea s to model envronment events whch provde context of the user. For more detals see [6][7]. In EMBET we need to detect user context from events transformed to the ontology model. For each applcaton we need to specfy an approprate algorthm for user context updatng based on user related events [4] We also need to detect context of nformaton, knowledge or experence entered by a user. In chapter 4 we descrbe a concrete example of such detecton. Detected context s only suggested to the user n form of checklst and a user confrms fnal knowledge /note context. A checklst s created of user current context and context detected from text of notes usng automatc semantc annotaton technques of a knowledge note text. Ths semantc annotaton s descrbed n detal n [8], [9]. 2.3 Context Matchng The role of EMBET s to assst users n relevant knowledge/suggestons, whch are applcable to ther current stuaton. EMBET needs to return experence n context where experence s relevant. Thus we need to match context of knowledge and context of a user to return approprate knowledge to the user. In K-Wf Grd we use a smple matchng technque where ontology elements present n knowledge context have to be found n user context; otherwse knowledge notes are not dsplayed. However, ths smple matchng algorthm s not suffcent n some applcatons and we need to use technologes based on smlarty matchng [10][11]. Another problem concernng votng on knowledge notes occurs, f smlarty mechansms are used snce a vote weght depends on a smlarty value and need to be determned. Currently we are developng and testng a new context matchng algorthm based on ntersecton of user and knowledge context. We defne 3 sets of concepts n (1): = { }, K = { K K K }, W { k, k,..., k } U u1, u2,..., un,,..., m 1 2 = l (1) 1 2 Where U s a set of user context, K s a set of knowledge notes and W s a set of knowledge context for knowledge K. A problem occurs n context matchng, because there are four dfferent ways to match user context and knowledge context (2).

4 U = W, U W, U W, U W W U U W (2) It s clear that the best opton s the frst one because there s exact match n contexts. The queston s what about the other equatons? We decde to defne the relevance r (3) (4), whch sorts the detected knowledge from best to worst. r U W = U W (3) In (4) we show dfferent approach to compute dfferent relevance n some cases. r = 2 U W U + W (4) For all knowledge where r > 0 we create sorted lst where the most relevant knowledge s on the top. Both approaches (3) (4) gve equal relevance n the two cases descrbed n (4): { } { } { } { } 1. U = u, u W = k, k, k, u = k U = u, u, u W = k, k, u = k Ths equalty s our future work. In addton, we would lke to deal wth votng on dsplayed knowledge notes. We need to calculate dfferent votng weghts based on relevance of user and knowledge context. In our future work we wll also evaluate usage of both relevance approaches (3) (4). (5) 3 Archtecture and Technology Archtecture of EMBET (Fgure 1 left sde) conssts of 3 man elements: - Core of the system - GUI - System Memory EMBET Core provdes the man functonalty of EMBET. It determnes a User context and searches for the best knowledge (n a form of text notes) n ts Memory. The knowledge s subsequently sent through XML-RPC or SOAP to EMBET GUI. EMBET GUI Graphcal User Interface (Fgure 1 rght sde) vsualzes the knowledge and the user s context nformaton to the user. Furthermore t nforms the EMBET core about user context changes. The context can be reported also drectly to the EMBET core from external systems (e.g. from workflow systems, receved emals, or fle system montors). EMBET GUI vsualzes knowledge based on XML 4 4

EMBET Archtecture and Graphcal User Interface EMBET Core - EMBET GUI nterface s used for an XML data exchange between EMBET Core and EMBET GUI.

5 transformaton to HTML through XSL 5 Templates processng. Moreover EMBET GUI has an nfrastructure for a note submsson and context vsualzaton. It further provdes a user wth feedback (votng) on knowledge relevance. In addton t contans a user nterface for knowledge management by experts where an expert can change a note and ts context. Fg. 1. EMBET Archtecture and Graphcal User Interface EMBET Core - EMBET GUI nterface s used for an XML data exchange between EMBET Core and EMBET GUI. The Interface s based on the XML-RPC 6 protocol for an XML message exchange. Interface to Memory s used for nformaton and knowledge extracton and storage. It s based on RDF/OWL data manpulaton usng Jena API, whch EMBET Core uses to extract and store knowledge. Experence s represented by text notes, an unstructured text, entered by a user. Ontology s stored and managed n the Web Ontology Language (OWL) 7. The Jena Semantc Web Lbrary 8 s used for knowledge manpulaton and knowledge storng. The Java technology s used for developng the system and user Interface s based on the JSP technology. The XSL templates are used to transform XML generated from OWL to dsplayed HTML

4 Example of Use To better llustrate the use of EMBET n the process of user assstance, we present the followng example from the K-Wf Grd project s flood forecastng applcaton, whch extends the flood

Left: Enterng new Note; Left: Note Context Detecton, checked tems are current user context, unchecked tems are elements detected from text of the note. User selects only those tems whch are relevant.

6 4 Example of Use To better llustrate the use of EMBET n the process of user assstance, we present the followng example from the K-Wf Grd project s flood forecastng applcaton, whch extends the flood predcton applcaton of the CROSSGRID (IST ) [12] project. Fg. 2. Left: Enterng new Note; Left: Note Context Detecton, checked tems are current user context, unchecked tems are elements detected from text of the note. User selects only those tems whch are relevant. The applcaton's man core conssts of smulaton models seres for meteorologcal, hydrologcal and hydraulc predctons. The models are organzed n a cascade, wth each step of the cascade beng able to employ more than one model. For example, the frst step - the meteorologcal weather predcton - can be computed usng the ALADIN model, or the MM5 model. Consder that the user has used the MM5 meteorology model and he/she wants to descrbe ts propertes (gathered knowledge), whch may be relevant for other users. The proposed model of nteracton s as follows: - A user enters a note through UAA, statng that the MM5 model s not approprate for weather forecast n September for the Bratslava area (Fgure 2). - From the workflow n whch the user states ths note, we know drectly the current user context (checked tems on Fgure 2) - Some of current context can be relevant to note and some does not have to be. The note s processed and ts text related to the context, as well as the relevant context tems are found n the ontology memory (GOM) (Fgure 2). In ths case, by fndng the text MM5 we can assume that MM5 Meteorology Servce s the relevant part of the context. There s other context relevant nformaton whch can be detected lke September, the tme n whch ths note s vald. - After the context detecton, the user s provded wth a checklst (Fgure 2) where the user may select only the relevant parts of the note context. - A user selects parts of the context, whch were detected by the system as really relevant. He/she can modfy the contents of the lst and fnally submt the note.

- Each tme anyone works wth the MM5 servce for the Bratslava area n September, the note s dsplayed or t can be also dsplayed n smlar contexts - Each note can be evaluated by a user as beng good or

7 - Each tme anyone works wth the MM5 servce for the Bratslava area n September, the note s dsplayed or t can be also dsplayed n smlar contexts - Each note can be evaluated by a user as beng good or bad and the current results are always dsplayed along wth the vote. Ths model gves a good bass for experence management and knowledge sharng n a vrtual organzaton as well as for applcaton-related collaboraton among users. We use EMBET system also n Raport project 9. In Raport the EMBET s used to support knowledge sharng and collaboraton n admnstratve applcaton dealng wth process of mltary exercse preparaton. In Raport we also use ACoMA system [13]. ACoMA system analyzes [8] [9] emal communcaton and attached knowledge notes to the emals. It annotates the emal to detect ontology elements from header and text of emal and then sends the nformaton to EMBET tool whch gets nformaton about current status of the process from the organzatonal memory. It detects that the current actvty of the busness process exercse smulaton s preparaton of exercse smulaton (and the output document s Form A). Based on the tme schedule, EMBET tool determnes the status of the process s D-75 (.e. specfcaton must be sent n D-70 days, whch s 5 days). From the text specfkacu (regular expresson s wrtten just for specfkac ) t can suggest where to fnd the document form to be feld n. EMBET tool then sends all the nformaton back to ACoMA tool, whch modfes orgnal emal message as shown n Fg. 3. Emal wth attached nformaton s then sent to mal server (added text s shown as html attachment n the emal message). Fg. 3. Example of emal modfed by ACoMA 9

8 5 Concluson and Future Work Our soluton was evaluated on the K-Wf Grd IST project, focused on buldng grdbased workflows, where users need to collaborate and share knowledge about dfferent applcatons, computatonal models, grd resources or data and t s further evaluated and developed wthn Raport project. Prevously t was evaluated on a selected admnstraton applcaton n the Pellucd IST project, where the context or the problem of a user was detected n the Workflow Management Applcaton. Such soluton may be appled n many further areas where the user problem can be detected from computerzed tasks. Usually ths occurs n any busness process where actons are performed va a computer, e.g. workflow processes, document management, supply chan management or dynamc busness processes where emal communcaton s n use. People lke to enter notes or memos. Ths s a natural way of notfcatons for themselves or others to remnd them of problems, actvtes or solutons. Therefore we thnk that such soluton can be successfully appled and commercalzed wth good results. EMBET s also beng further developed to support emal communcaton [13]. Such extenson offer knowledge to user nsde of emal message. We wll also evaluate dfferent approaches of context matchng. References 1. Ralph Bergmann: Experence Management: Foundatons, Development Methodology, and Internet-Based Applcatons (Lecture Notes n Artfcal Intellgence), Thomas H. Davenport, Laurence Prusak, Workng Knowledge, ISBN: , Laclavk, M., et al.: Experence Management Based on Text Notes (EMBET); Innovaton and the Knowledge Economy, Volume 2, Part 1; Edted by Paul Cunngham and Mram Cunngham; IOS Press, pp ISSN , ISBN Mchal Laclavk: Experence Management based on Ontology and Text Notes (The EMBET System); Work for the RNDr. degree; Prrodovedecka fakulta, UPJŠ Košce, August Schreber et al.: Knowledge Engneerng and Management: the Common-KADS methodology, ISBN: , Laclavk, M., et al.: AgentOWL: Semantc Knowledge Model and Agent Archtecture; In Computng and Informatcs. Vol. 25, no. 5 (2006), p ISSN Mchal Laclavk, et al.: Semantc Knowledge Model and Archtecture for Agents n Dscrete Envronments; In: Fronters n AI, Vol 141, Proc. of ECAI 2006 Conference, G.Brewka et al.(eds.), IOS Press, pp ISBN ISSN Laclavk, M., et al..: Ontology based Text Annotaton OnTeA; Informaton Modellng and Knowledge Bases XVIII. IOS Press, Amsterdam, Mare Duz, Hannu Jaakkola, Hannu Kangassalo, Yasush Kyok (Eds.), Laclavk, M., et al.: OnTeA: Sem-automatc Ontology based Text Annotaton Method; In: Tools for Acquston, Organsaton and Presentng of Informaton and Knowledge. P.Navrat et al. (Eds.), 2006, pp.49-63, ISBN Laclavk M.: Ontology and Agent based Approach for Knowledge Management; Thess submtted for the PhD degree; Insttute of Informatcs, Slovak Academy of Scences, 2005

9 11. Balogh Z., Budnska I.: OntoSm - Ontology-based Smlarty Determnaton of Concepts and Instances. In: Tools for Acquston, Organsaton and Presentng of Informaton and Knowledge. P.Navrat et al. (Eds.), Bratslava, 2006, pp.64-70, ISBN Hluchy L., et al.: Flood Forecastng n CrossGrd project. In: EAGC, 2004, LNCS 3165, Sprnger-Verlag, 2004, pp , ISSN , ISBN Seleng, M., et al.: Automated Content-based Message Annotator AcoMA; In: Proceedngs of ITAT 2006 Informaton Technologes - Applcatons and Theory, Peter Vojtas (Ed.), Department of Computer Scence, Faculty of Scence, Pavla Jozef Safark Unversty, Kosce, 2006, pp , ISBN ;

A Binarization Algorithm specialized on Document Images and Photos

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