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A knowledge-based solution for core competence evaluation in human-capital intensive companies

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Abstract: Determining fields of excellence in the know-how of knowledge intensive companies is often a crucial decisional process, aimed eg, at identifying the competence to be strengthened or to invest on in a long term strategy. In this paper we
  A Knowledge-based Solution for Core Competence Evaluation inHuman-Capital Intensive Companies Simona Colucci(Politecnico di Bari, Bari, Italy andD.O.O.M. s.r.l., Matera, Italys.colucci@poliba.it)Eugenio Di Sciascio(Politecnico di Bari, Bari, Italy,disciascio@poliba.it)Francesco M. Donini(Universit`a della Tuscia, Viterbo, Italy,donini@unitus.it) Abstract: Determining fields of excellence in the know-how of knowledge intensive companiesis often a crucial decisional process, aimed e.g., at identifying the competence to be strength-ened or to invest on in a long term strategy. In this paper we propose a semantic-based approachfor automatic extraction of such a specializing knowledge, usually called Core Competence inknowledge management literature. The proposed approach exploits Description Logics as for-malism for the representation of knowledge sources and implements novel reasoning services, inparticular informative common subsumers specifically devised for Core Competence evaluation. Key Words: Common Subsumers, Core Competence, Description Logics. Category: M.4, M.7, H.3.3 1 Introduction Since first investigations on the role of knowledge in human-capital intensive compa-nies, the capability to focus on a significant portion of the organizational know-howhas been identified as a crucial asset for business success. Such a belief is at the ba-sis of the so-called Resource-based Theory of the firm [Wernerfelt, 1995], according towhich unique company capabilities should be exploited to achieve competitive advan-tage [Barney, 1991, L. Halawi and McCarthy, 2005, Meso and Smith, 2000]. In partic-ular, the term Core Competence was introduced [Hamel and Prahalad, 1990] to denotesuch a specializing portion of organizational know-how. It is intuitive that the hardnessof identifying such an intellectual capital increases with the size of the company andwith the ambiguity of company know-how descriptions.In recent years we have been investigating knowledge-based approaches and so-lutions for a specific field of knowledge management, namely skills and competencemanagement, in the framework of Description Logics (DLs)[Baader et al., 2002] and  Proceedings of I-KNOW ’08 and I-MEDIA '08Graz, Austria, September 3-5, 2008  semantic technologies, both exploiting classical inference services and introducing newones [Colucci et al., 2007b]. As it is nowadays well-known, semantic-based technolo-giesaskforcompanyintellectualcapitaltobeunambiguouslydescribedinformalrepre-sentations, according to a shared vocabulary provided by ontologies modeling skills do-main. In particular, our solutions employ DLs for knowledge representation and exploitDL reasoning services to infer new knowledge on the elicited descriptions. Obviously,once company know-how has been formally represented in a common knowledge basein terms of individual profile descriptions and know-how, such a repository could beexploited to extract the most characterizing portion of company intellectual capital, i.e. company Core Competence. Nevertheless this is easier said than done, as well-knownreasoning services fail to provide such information. As we show later on, the appar-ently best suited inference service for the above task, the Least Common Subsumer  [Cohen et al., 1992], shows clear limits.In this paper we therefore propose new non-standard inference services on collec-tions of individual profile descriptions formalized in DL, for the automatic extractionof company Core Competence. Such specifically developed reasoning services are in-troduced in Section 2. Two different Core Competence evaluation approaches are thendetailed in Section 3, before closing the paper with conclusions. 2 New Services Definition In the automated Core Competence extraction we propose, we refer to ALN  (Attribu-tive Language with Number Restrictions) for formally describing knowledge sources of a company. ALN  provides a limited set of constructs, which allow for describing theknowledge domain by combining the basic elements of a DL, namely concept names ,representing objects of the domain — i.e. ProductionManagement,AssetAllocation,Creativeness and AssetManager — and role names , represent-ing possible binary relationships among concepts, i.e. knows,isAbleTo . Every DL in-cludes two concepts,  and ⊥ , representing a concept interpreted by the whole domainand by an empty set, respectively. ALN  allows also for qualified universal restric-tions — i.e. ∀ knows . AssetAllocation denotes an advanced knowledge in Asset Allo-cation — and number restrictions — i.e. ( ≥ 3 knows ) , ( ≤ 2 isAbleTo ) denote thepossession of at least three skills and at most two abilities — over roles. By using suchconstructs it is possible to detail concept inclusions and definitions , which constitutethe intensional knowledge of a DL system, what is called a TBox in DL and ontology in knowledge representation. For example the inclusion AssetManager  Manager asserts that the set of asset managers in the domain is included in the one of managers;theconceptdefinition AssetManager ≡ Manager ∀ knows . AssetAllocation givesinstead to managers endowed with Asset Allocation knowledge the name of Asset Man-ager, like the definition Manager ≡ ∀ knows . Management gives the name man-ager to subsets of domain possessing Management knowledge. 260 S. Colucci, E. Di Sciascio, F. M. Donini: A ...  Every DL allows for basic reasoning services inferring new knowledge from thedescriptions elicited in the TBox; in particular satisfiability and subsumption are de-fined for every DL. In a nutshell, satisfiability checks for internal coherency of conceptdescriptions, evaluating the consistency of elicited information; subsumption checksinstead whether a concept description is more generic than another one. Formally, sub-sumption is defined as follows, with respect to a domain interpretation function  I  : Definition 1 (Subsumption) Given two concept descriptions C  and D and a TBox T   in a DL L , we say that D subsumes C  w.r.t. T   if for every model of  T   , C  I  ⊂ D I  . Wewrite C   T   D , or simply C   D if we assume an empty TBox.Having a collection of concept descriptions in a DL L , the problem of determin-ing the Least Common Subsumer(LCS) of the collection has been proposed by Cohen,Borgida and Hirsh [Cohen et al., 1992] as a non-standard reasoning service. By def-inition, the LCS of a collection of concept descriptions represents the most specificconcept description subsuming all of the elements of the collection. Formally, we recallthe following definition: Definition 2 (LCS,[Cohen and Hirsh, 1994]) Let C  1 ,...,C  n be n concepts in a DL L . An LCS  ( C  1 ,...,C  n ) , is a concept E  in L such that the following conditions hold: (i) C  i  E  for i = 1 ,...,n (ii) E  is the least L -concept satisfying (i), i.e., , if  E   is an L -concept satisfying C  i  E   for all i = 1 ,...,n , then E   E   If the collection contains employee profile descriptions, as in our reference scenario,the LCS represents the competence shared by all the employees in the collection. Sucha concept description is a good candidate for determining the Core Competence of thecompany at a first sight. Nevertheless the need for the LCS to subsume each conceptin the collection causes its corresponding description to be too generic in most cases:if a competence has to be shared by the whole company personnel it needs to be quitegeneric. As a toy example, consider a small company in which only the following threeemployees work:– Nick  : AssetManager ∀ isAbleTo . Creativeness – Frank  : ∀ knows . AssetAllocation ∀ isAbleTo . Creativeness – Robert  : Engineer ∀ isAbleTo . Creativeness The only LCS of such a collection is Creativeness ability, which might result a notmuch significant knowledge. If we instead give up such a full skill coverage and acceptthe assumption that Core Competence needs to be possessed by a significant portion of company personnel, more interesting results can be achieved. Obviously the requireddegree of coverage may be set by company management. To this aim, we propose andintroduce new reasoning services. Definition 3 (k-CS) Let C  1 ,...,C  n be n concepts in a DL L , and let be k < n . A k -Common Subsumer  ( k -CS) of  C  1 ,...,C  n is a concept D such that D is an LCS of  k concepts among C  1 ,...,C  n .If the example company management decides that 2 / 3 of the employees have to S. Colucci, E. Di Sciascio, F. M. Donini: A ... 261  possess some knowledge to consider it part of the Core Competence, Asset Allocationknowledge represents a commonality between two employees (according to the defi-nitions at the beginning of the section) and then a Core Competence. Of course alsoCreativeness ability is a k − CS  of the collection, but it does not add any informativecontent to the LCS: for this reason we distinguish in the following k-Common Sub-sumers adding informative content to LCS. Definition 4 (IkCS) Let C  1 ,...,C  n be n concepts in a DL L , and let k < n . An Infor-mative k -Common Subsumer  (IkCS) of  C  1 ,...,C  n is a k -CS E  such that E  is strictlysubsumed by LCS  ( C  1 ,...,C  n ) .Among possible IkCSs , some are characterized by maximal cardinality of the setof subsumed concepts: in our example scenario, if we set k = 3 Asset Allocationknowledge stops being a k − CS  and the only common subsumer is Creativeness ability,which is not informative by definition. We define in the following concepts like AssetAllocation as best informative common subsumers (with k = 2 ). Definition 5 (BICS) Let C  1 ,...,C  n be n concepts in a DL L . A Best InformativeCommon Subsumer  (BICS) of  C  1 ,...,C  n is a concept B such that B is an Informative k -CS for C  1 ,...,C  n , and for every k < j ≤ n every j -CS is not informative.For collections whose LCS is equivalent to the universal concept  , the followingdefinition makes also sense: Definition6(BCS) Let C  1 ,...,C  n be n conceptsinaDL L .A  BestCommonSubsumer  (BCS) of  C  1 ,...,C  n is a concept S  such that S  is a k -CS for C  1 ,...,C  n , and for every k < j ≤ n every j -CS ≡ .Consider for example a new employee :– Fred  = Manager ∀ knows . ProductionManagement .The only LCS of the collection including the four employees is the universal con-cept. On the contrary, for k = 3 we have Creativeness ability as k-common subsumer,which is informative w.r.t. the LCS (it is equivalent to the universal concept) and best:if we add one unit to k the k-CS reverts to the universal concept. 3 Solutions to Core Competence Evaluation Problem In this paper we provide two processes for Core Competence evaluation: the first oneexploits the services introduced in Section 2 to discover unknown fields of excellenceof the company; the second one checks for the possession of a list of known target com-petencies by a significant portion of company personnel and explains how to reach thetarget in case the check fails.Both of the approaches ask for the concepts to be written in Concept Components ac-cording to the following rules. If  C  is a concept description in a DL L , with C  written ina conjunction C  1 ··· C  m , the Concept Components of  C  are defined as follows: if  C  j , with j = 1 ...,m is either a concept name or a negated concept name or a numberrestriction, then C  j is a Concept Component  of  C  ; if  C  j = ∀ R . E  , with j = 1 ...,m , 262 S. Colucci, E. Di Sciascio, F. M. Donini: A ...  then ∀ R . E  k is a Concept Component  of of  C  , for each E  k concept component of  E  .The definition of  Subsumers Matrix in the following is preliminary to both processes of Core Competence evaluation. Definition 7 (Subsumers Matrix) Let C  1 ,...,C  n be a collection of concept descrip-tions C  i in a Description Logic L and let D j ∈ { D 1 ,...,D m } be the Concept Com- ponents deriving from a set of concepts. We define the Subsumers Matrix S  = ( s ij ) ,with i = 1 ...n and j = 1 ...m , such that s ij = 1 if the component D j subsumes C  i ,and s ij = 0 if the component D j does not subsume C  i .Referring to Subsumers Matrix, we define: Concept Component Signature ( sig D j ): set of indeces of concepts C  1 ,...,C  n sub-sumed by D j ; observe that sig D j ⊆{ 1 ,...,n } ; Concept Component Cardinality ( T  D j ): cardinality of  sig D j , that is, how many con-cepts among C  1 ,...,C  n are subsumed by D j . Such a number is  ni =1 s ij ; Maximum Concept Component Cardinality ( M  S  ): maximum among all conceptcomponent cardinalities, that is, M  S  = max { T  D 1 ,...T  D m } ; Second Maximum Concept Component Cardinality ( PM  S  ): maximum among thecardinalities of concept components not subsuming all n concepts in the collection( PM  S  = max { T  D j | T  D j < n } ); by definition PM  S  < n ; Common Signature Class (  sig Dj ): concept formed by the conjunction of all conceptcomponents whose signature contains D j : { D h | sig D j ⊆ sig D h } 3.1 Core Competence ExtractionDefinition 8 (Collection Subsumers Matrix) Let C  1 ,...,C  n be a collection of con-cept descriptions C  i in a Description Logic L . We define the Collection SubsumersMatrix as a Subsumers Matrix in which D j ∈{ D 1 ,...,D m } are the concept compo-nents deriving from all concepts in the collection.In the following we define, with respect to a collection of concept descriptions, BCS  the set of BCSs, BICS  the set of BICSs, ICS  k the set of IkCSs, given k < n and CS  k the set of k-CSs, given k < n . In [Colucci et al., 2008] we proposed Algorithm 1 deter-mining the sets BICS  , CS  k , ICS  k , BCS  of a collection { C  1 ,...,C  n } of conceptsin ALN  , whose Subsumers Matrix is given as input. In order to understand the ratio-nale of the proposed algorithm, consider the company with the four employees (Nick,Frank, Robert and Fred) in the tiny example in Section 2. The concept componentscoming from the collection of employees are: D 1 = ∀ knows . Management , D 2 = ∀ knows . AssetAllocation , D 3 = ∀ isAbleTo . Creativeness , D 4 = Engineer , D 5 = ∀ knows . ProductionManagement . The collection subsumers matrix is shown in theleft-hand side of Figure 1. If  k = 2 , the only components with cardinality at least equalto k are D 1 , D 2 and D 3 and then their common class signature is added to the set CS  k (line 3), which contains the k − CSs D 1 , D 2  D 3 and D 3 . The check in line 4 resultstrue for all of the three components, given that the only concept subsuming the fouremployee profiles is the universal concept; the three concepts in CS  k are then added to S. Colucci, E. Di Sciascio, F. M. Donini: A ... 263
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