Decision Strategies for Rating Objects in Knowledge-Shared Research Networks

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1 Decson Strateges for Ratng Objects n Knowledge-Shared Research etwors ALEXADRA GRACHAROVA *, HAS-JOACHM ER **, HASSA OUR ELD ** OM SUUROE ***, HARR ARAKSE *** * nsttute of Control and System Research, Bulgaran Academy of Scences Acad. G. Bonchev str., Bl., P.O.Box 79, Sofa BULGARA ** Group of Automatc Control and echncal Cybernetcs, FB Unversty of Wuppertal, Fuhlrottstr. 0, D-4097 Wuppertal GERMAY *** Espoo-Vantaa nsttute of echnology Vanha maante 6, F-0600, Espoo FLAD Abstract: n ths paper, decson strateges for ratng objects n nowledge-shared research networs are proposed. he objects can be of varous nds and they are represented by an Object Vector that conssts of General Descrpton Part extended Dubln Core) and Classfcaton Part. Based on ths representaton, possble cases for searchng for approprate objects n the networ are consdered and decson strateges are developed for each one of them. n case, the search s performed for all type of objects for document objects, source code objects, executable code objects, pcture objects etc.) that are relevant for a user specfed tas. n ths case, three methods can be appled: ). Searchng by the KXSOM method, ). Searchng by decson mang approach based on fuzzy sets theory), ). Searchng by decson mang approach that s a combnaton of the KXSOM method and the decson mang approach. n case, the search s performed only for document objects and fve methods can be appled: ). Searchng by the KXSOM method, ). Searchng by the WEBSOM method, ). Searchng by the decson mang approach, 4). Searchng by decson mang approach, 5). Searchng by decson mang approach that s a combnaton of the WEBSOM method and the decson mang approach. n case, the search s done only for executable code objects that would be relevant for a user specfed tas. n ths case, decson mang approach V s appled. n the paper also a method s proposed for comparson of the developed decson strateges. Key-Words: Knowledge-Shared etwors, Decson Support Unt, Decson Strateges, Optmalty Crtera, Fuzzy Sets, Self-Organzng Maps.. Decson support unt as part of global nowledge-shared research networ t s proposed to desgn and establsh a framewor shell) for Global nternatonal) Knowledge Shared Research etwor []. he entre networ s desgned as a global networ va nternet) consstng of Local natonal) Research and Worng etwors LRW va ntranet and/or nternet). hs framewor conssts of co-operatng expert systems ntellgent Base Systems - BS) wth decson support modules for research management. he communcaton, especally the nowledge retreval exchange) and requests ntellgent Knowledge Acquston) between the LRWs s provded by applyng Mult- Agent-Structures. he structure of every ntellgent Base System BS) wthn the framewor of the Global Knowledge Shared Research etwor s gven n []. t ncludes the followng components []: Local Dctonary Local DC), Global Master) Dctonary Global/Master DC), Decson Support Unt DSU), ntellgent Flter, Meta Data Wrter, Dynamcal

2 Knowledge Data Base DKDB), Admnstraton nterface AD). he DKDB [] contans the nowledge about a specfc research area. Every object n the DKDB has a correspondng metadata object whch contans the metadata nformaton about the actual object. Every metadata object s represented by an Object Vector whch s consdered n secton. All metadata objects avalable at the local networ are stored n the Local Dctonary. hus, the Local DC represents the local nowledge. At every BS there s also a copy of the Global Dctonary that represents the nowledge of the global research networ. he Decson Support Unt DSU) s a part of every ntellgent Base System. he DSU searches n the Global Master) DC for the most approprate objects to solve a user defned tas. he DSU s also used for ntal loadng, automatc updatng and mrrorng of the Master DC through ORB Object Request Broer). he relatons between the nference/decson unt DSU), the Local DC and the Master DC wthn a sngle ntellgent Base System are represented n Fg.. Fg.. Relaton between nference/decson unt DSU), Local DC and Master DC wthn a sngle ntellgent Base System BS). he functons of DSU can be summarzed as follows: A). ntal loadng of Local Dctonares; B). ntal loadng of Master Dctonary; C). Automatc updatng of Master DC; D). Searchng n the Master DC for the most approprate objects for a user specfed tas; E). Mrrorng the Master DC to all ntellgent Base Systems each tme when the Master DC has been updated. he Rule-Base of DSU that ncludes the rules for performng the functons A), B), C), and E) s gven n detals n []. n ths paper, decson strateges for performng functon D) of DSU,.e. for searchng n the Master DC for the most approprate objects alternatves) for a user specfed tas are proposed. As a result from the searchng decson mang) the objects are ordered from best most approprate) to worst least approprate). he decson strateges are descrbed n detals n secton.

3 . Structure of the object vector he objects n the Knowledge-Shared Research etwor can be of varous nds such as document objects, executable code objects, source code objects etc. Each tem n the Local DCs and n the Master DC s represented by ts Object Vector whose structure s consdered n detals n []. he Object Vector conssts of parts: ). General Descrpton Part t ncludes general nformaton about the object. t s represented n an extended Dubln Core DC) format and s created by usng a specal program named MDWrter Meta Data Wrter []). hs part s constant part. ). Classfcaton Part t contans nformaton about the relevance of the object for the tass beng solved. hs part s varable part. t ncludes the followng felds: Accumulated Satsfyng Factor ASF) t reflects the accumulated usefulness of the object n the tass that have been solved untl the current moment; as Counter C) t counts the number of the tass for whch the object has been approprate; Mean Satsfyng Factor MSF) t represents the mean usefulness of the object; Keyword Matchng Factor KMF) t shows to whch extent the object eywords match the tas eywords; Actualty Factor AF) t shows how actual the object s. he way to determne all these factors s explaned n detals n [].. Decson strateges for ratng objects n the Master Dctonary hrough the decson mang mechansm the DSU wll search n the Master DC for the most approprate objects alternatves) for a gven tas the objects wll be ordered from best to worst). t has to be noted that the followng rule wll be appled: Rule D: Only objects whose Keyword Matchng Factor for a gven tas s greater than 0.8 wll be consdered as possble alternatves for ths tas. he search wll be consdered as possble cases. he searchng decson mang) mechansm wll be dfferent for these cases. Case : Search for all type of objects n the Knowledge- Shared etwor that are relevant for a user specfed tas. t wll be searchng for document objects, source code objects, executable code objects, pcture objects etc. n ths case, three methods can be appled: ). Searchng by the KXSOM method developed at the Espoo-Vantaa nsttute of echnology, Fnland, ). Searchng by decson mang approach, ). Searchng by decson mang approach that s a combnaton of the KXSOM method and the decson mang approach. ). Searchng by the KXSOM method; he KXSOM method s smlar to the WEBSOM method [4] that s descrbed below n case. he KXSOM method le the WEBSOM method s based on the Self-Organzng Maps SOM) ntroduced by Kohonen. he dfference s that the KXSOM method does not process the whole text of a document n order to buld a document map and uses a dfferent approach for searchng. he essental pont s that KXSOM uses the provded descrpton and subject eywords of the object from ts metadata n the database to construct the object map. n ths way, not only documents but all type of objects can be mapped document objects, source code objects, executable code objects, pcture objects etc.). ). Searchng by decson mang approach ; Accordng to ths approach, the estmaton of the relevance of the objects for a gven tas s based on the values of the Mean Satsfyng Factor, the Keyword Matchng Factor and the Actualty Factor of the Classfcaton part of the respectve tem Object Vector. he objects are regarded as dfferent alternatves to be used for the gven tas and they wll be rated from the best the most approprate) to the worst the least approprate). he ratng of the alternatves represents a multple crtera decson mang problem whch s formulated n the followng way: Fnd the best alternatve object A respectvely rate the alternatve objects from best to worst) that optmzes the followng crtera: ) = MSF ) max ) ) = KMF ) max ) ) = AF ) max, ) where MSF A ) s the Mean Satsfyng Factor of the alternatve object A, KMF A ) s the Keyword Matchng Factor of object A for the current tas and AF A ) s the Actualty Factor of object A. he way of calculaton of these Factors s gven n [].

4 n order to determne the best alternatve object A best respectvely to order the objects from best to worst), a decson mang approach s developed that s based on fuzzy sets theory. he approach s named decson mang approach and s descrbed by the followng steps:. hree types of fuzzy preference relatons are ntroduced. he fuzzy relatons are: - preference relaton Q wth respect to the crteron to be optmzed: Q = { ) : A A, )} 4) - preference relaton Q wth respect to the crteron to be optmzed: Q = { ) : A A, )} 5) - preference relaton Q wth respect to the crteron to be optmzed: Q = { ) : A A, )}, 6) where, and are membershp functons. Functon ) shows how good the alternatve A s compared to alternatve A j wth respect to optmzaton of crteron, functon A, A ) - wth regard to optmzaton of crteron j and ) - wth respect to optmzaton of crteron. t s proposed for the membershp functons ), ) and ) of relatons Q, Q and Q to be computed as follows: 0, f A) < >, f A) > < A) ), = ) + j ), ) ) 7) f A) > > A) ), A) ) + ) f A) < < Here, s the average value of the crteron among all avalable alternatves,.e.: n = ) n = 8) he average value of the crteron serves as a bass for comparson of alternatves.. Determne the total fuzzy preference relaton and the fuzzy subset of nondomnated alternatves. he total fuzzy preference relaton s defned as: Q = {, A ) : A, A A,, A )} 9) j j and t shows how good the alternatve A s compared to the alternatve A j wth respect to optmzaton of all crtera ), ), ). he membershp functon ) s computed as follows: A, A ) λ, A ), 0) j = j = where λ s the weght of the -th crteron. hese weghtng coeffcents are chosen n a such a way so they wll satsfy: = λ =, λ > 0 ) s Let ) be the correspondng strctly fuzzy preference realton to ) wth the membershp functon [5]: s ) = max{[ ) j, A )],0} ) hen the fuzzy subset of nondomnated alternatves s descrbed wth a membershp functon as [6]: s A ) = max, A ) ) j Aj A. Select the best alternatve A best order alternatves from best to worst). he best alternatve s the one that maxmzes A ) [6],.e.: A best = arg max ) 4) A A he alternatves are ordered from best to worst n decreasng order of ). ). Searchng by decson mang approach - combnaton of the KXSOM method and the decson mang approach. he decson mang approach can be descrbed by the followng steps: j

5 . Apply the KXSOM method to determne the set of alternatves A = { A, A,... An } that match the tas eywords.. Rate the alternatves found n step from the best the most approprate) to the worst the least approprate) by applyng the decson mang approach. 4). Comparson of methods ), ) and ). For comparson of the three methods, the followng crtera can be used: Crteron : f J = max, 5) t whch s a coeffcent showng the effcency of the search - the rate of fndng the useful objects related to the subject specfed by the user; f s the number of the useful objects found n the Local DC related to the specfed subject; t s the total number of all useful objects n the Local DC related to the subject. Crteron : un J = mn, 6) f whch s the rate of fndng unapproprate objects; un s the number of the unuseful objects not applcable to the subject specfed by the user) found n the Local DC; f s explaned above. Crteron : J = t mn, 7) whch s the tme spent for the search. he three methods descrbed above can be regarded as three alternatves to search for all type of objects n the networ and the tas s to fnd the best alternatve method respectvely rate the methods from best to worst) that optmzes the crtera 5), 6), 7). n order to solve ths decson mang tas, the decson mang approach descrbed above can be appled. Case : Search only for document objects n the Knowledge- Shared etwor that are relevant for a user specfed tas. n ths case, fve methods can be appled: ). Searchng by the KXSOM method, ). Searchng by the WEBSOM method [4], ). Searchng by the decson mang approach appled n case ; 4). Searchng by decson mang approach appled n case that s a combnaton of the KXSOM method and the decson mang approach, 5). Searchng by decson mang approach that s a combnaton of the WEBSOM method and the decson mang approach. ). Searchng by the KXSOM method; he KXSOM method was descrbed n case. ). Searchng by the WEBSOM method [4]; he WEBSOM method [4] s a neural networ method that automatcally organzes arbtrary freeform text document collectons nto a specfc order. WEBSOM s based on the Self-Organzng Maps SOM) ntroduced by Kohonen. he method ncludes the followng steps: ). Preprocessng specfc flters are used to remove non-textual and structural nformaton from documents; ). Document encodng the document s encoded as a hstogram of ts words. t s represented as a document vector where the each dmenson n the vector corresponds to a word n the vocabulary and the value of the dmenson descrbes how many tmes the words occurs n the document; ). Buldng Word Category Map WCM) the words of free natural text are clustered onto neghborng grd ponts of a specal SOM. hus, nterrelated words that have smlar contexts appear close to each other on the map; 4). he documents are encoded by mappng ther text, word by word, onto the Word Category Map and a hstogram of the hts on t s formed. he document map s then formed wth the SOM algorthm usng the hstograms as fngerprnts of the documents. ). Searchng by the decson mang approach appled n case ); he search for document objects only can be performed also by the decson mang approach that s appled n case descrbed above. hus, the estmaton of the relevance of the documents for a gven tas wll be based on the values of the Satsfyng Factor, the Keyword Matchng Factor and the Actualty Factor. 4). Searchng by decson mang approach appled n case ) - combnaton of the KXSOM method and the decson mang approach ; he decson mang approach was descrbed n case. 5). Searchng by decson mang approach - combnaton of the WEBSOM method and the decson mang approach. he decson mang approach can be descrbed by the followng steps:. Apply the WEBSOM method to determne the set of documents alternatves) A = { A, A,... An } that match the tas eywords.. Rate the documents found n step from the best the most approprate) to the worst the least approprate) by applyng the decson mang approach.

6 6). Comparson of methods ), ), ), 4) and 5). t can be done n a smlar way as t was descrbed n case. Case : Search only for executable code objects n the Knowledge-Shared etwor that are relevant for a user specfed tas. n ths case, a new approach decson mang approach V) can be appled to estmate the relevance of the executable code objects. hs approach s descrbed by the followng steps:. Estmate the prelmnary relevance of the objects. Apply the decson mang approach used n the general case ) to order the objects from best most relevant) to worst least relevant) based on the values of the Mean Satsfyng Factor, the Keyword Matchng Factor and the Actualty Factor. Only the frst few objects of ths order the objects that are most relevant to the tas) wll be appled to solve the tas.. Estmate the fnal relevance of the objects. hs wll be based on the results produced by the objects n solvng the user-specfed tas. hs wll be done only for the frst few objects determned n step. t s supposed that the tas specfed by the user represents a multple crtera optmzaton problem and the user wshes to fnd the best method the best executable code object) to solve t. he multple crtera can be represented n the followng general form: ) max 8) =,,...,m he objects wll be rated from best to worst based on the results that they have produced. For ths purpose, a decson mang algorthm smlar to decson mang approach can be appled, where the form of optmalty crtera 8) depends on the specfc tas that has to be solved.. Determne the Satsfyng Factor of the objects. he Satsfyng Factor SF A ) of an object A wll be determned after usng the object for the gven tas and t wll be based on the results produced by the object. t was mentoned above n the descrpton of decson mang approach ) that the alternatves are ordered from best to worst n decreasng order of ) the membershp functon of the fuzzy set of nondomnated alternatves). herefore, the value of A ) obtaned n determnng the best alternatve A to solve the user-specfed tas can be used as an estmate of the relevance of the object. hus, the value of the Satsfyng Factor s: SF A ) = ) 9) After SF A ) has been determned, then the Accumulated Satsfyng Factor ASF), the as Counter C) and the Mean Satsfyng Factor MSF) have to be updated. References: []. A. Grancharova, H. J. ern,. Suuronen, H. Arasnen, H. A. our Eldn, Decson support unt as part of global nowledge-shared research networ, Euromeda 000 Conference, Antwerp, Belgum, 8-0 May, 000. [].. Suuronen, A DKDB system for Knxmas project, Espoo-Vantaa nsttute of echnology, Knxmas project [].. Suuronen, MDWrter Metadata Wrter for Knxmas project), Espoo-Vantaa nsttute of echnology, Knxmas project [4].. Honela, S. Kas, K. Lagus,. Kohonen, WEBSOM - Self-organzng maps of document collectons, Proceedngs of WSOM 97, Worshop on Self-Organzng Maps, Espoo, Fnland, 4-6 June, 997, pp.0-5. [5].. Popchev, V. Peneva, An algorthm for comparson of fuzzy sets, Fuzzy Sets and Systems, vol.60, 99, pp [6]. S. Orlovs, Decson mang wth a fuzzy preference relaton, Fuzzy Sets and Systems, vol., 978, pp Acnowledgement hs wor s carred out under EU CO COPERCUS Project Knowledge Shared XPS - Based Research etwor Usng Mult-Agent Systems KXMAS), Grant CO-o: 977.

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