production process component and a relationship between them represents ..... involved in the truck tire production at the Business Unit Truck of Pirelli Tires.
CKS–Net: a Conceptual and Computational Framework for the Management of Complex Knowledge Structures Stefania Bandini and Fabio Sartori Department of Computer Science, Systems and Communication (DISCo) University of Milan - Bicocca via Bicocca degli Arcimboldi, 8 20126 - Milan (Italy) tel +39 02 64487857 - fax +39 02 64487839 {bandini, sartori}@disco.unimib.it
Abstract. This paper presents CKS–Net, a conceptual and computational framework for the acquisition, representation and use of complex and heterogeneous knowledge. The framework aims to support effectively informal groups of workers in their daily activities. A typical example of such groups are Communities of Practice, which can be considered the most important centers for the creation of knowledge in an organization. The most interesting feature of Communities of Practice is that their members collaborate in order to solve new problems, by sharing their experience through a negotiation–reification process solutions. In order to capture the heterogeneous knowledge involved in this process the concept of Complex Knowledge Structure has been introduced. Then, the paper focuses on how Complex Knowledge Structures can be used by CKS–Net to support Communities in their decision making processes, showing how it can be profitably used as a framework for the development of case–based systems.
1 Introduction Knowledge Management (KM) can be considered as the process through which organizations generate value from their intellectual and knowledge–based assets [7] [5]. Thus, enterprises’ interest in supporting KM through the adoption of computer–based tools and methodologies has significantly grown over last years, becoming a trend within the different communities of researchers in Computer Science. According to [13], such tools and methodologies can be generally led to the Artificial Intelligence (AI) area, e.g. Knowledge Based Systems (KBS), data mining, e–learning application tools and so on. For this reason, a lot of researchers in AI have focused their attention on the development of sophisticated tools and methodologies to deal with the KM area and to provide companies with generic methods and tools to capitalize their knowledge. The results of these efforts seem to be encouraging: many KM frameworks have
been recently designed and (partially) implemented to this aim, and someone is trying to produce standards [12]. Anyway, it is not simple to evaluate the effectiveness of such generic methodologies, since they are still at an initial state: in particular, they are not suitable to acquire and represent heterogeneous knowledge coming from informal structures, like for example Communities of Practice [16] (CoPs). CoPs spontaneously arise within enterprises and characterize themselves as important centers for the creation of expert knowledge related to specific topics. Enterprises are interested in the development of CoPs, since they can be able to find innovative solutions to new problems, possibly remembering solutions adopted in the past and adapting them to arising situations. This paper introduces CKS–Net, a conceptual and computational framework for the acquisition, representation and use of knowledge owned by Communities of Practice. In order to allow an effective support to different kinds of CoPs, the concept of Complex Knowledge Structures has been introduced. A Complex Knowledge Structure (CKS) is a complete description of the negotiation– reification process among CoP members, that is the process through which knowledge circulates inside Communities of Practice and outside of them. Due to the variability of the elements composing a CKS and of the relations among them, a Complex Knowledge Structure is represented in CKS–Net as a labelled graph. More details about the genesis of Complex Knowledge Structures can be found in Section 2. One of the main objectives of CKS–Net is to help Communities of Practice in comparing new problems to solve with similar situations tackled in the past. This is a peculiarity of the framework that makes it particularly suitable for the development of case–based applications. Case Based Reasoning (CBR) [10] technology as a very suitable paradigm to deal with complex knowledge structures. It has been applied to many research areas [1] [14] [15] and it results to be the most natural approach for many research projects characterized by episodic knowledge, since it allows to find a solution to a new problem (i.e case) by the adaptation of solutions adopted in the past to solve the most similar problems to the current one. The key aspects of a Case Based application are the structure of the case and the nature of the similarity among cases. The structure of the case must define what kind of attributes has to be adopted to describe problems, in order to allow the comparison among them. The nature of the similarity must define a criterium, that can be a function (i.e Similarity function) or something more complex like a Similarity Metric [8], to compare cases according to their attributes, in order to determine what are the most similar past problems to the current one. The definition of case structure and similarity function is rather simple when the examined domains are characterized by well defined knowledge. Unfortunately, there exist a lot of situations in which the structure of the case is not unique, since it depends on the context in which it is analyzed: an important feature of the proposed framework is the similarity metric for the retrieval phase of CBR, based on the evaluation of the structural equivalence [9] of a new CKS with a past one. In particular, the similarity metric
currently adopted by the framework is based on the identification of so called critical nodes in the graphs representing the involved CKSs. The criticality of a node can be evaluated exploiting different methods: some definitions of criticality and methods to measure it will be given in Section 3.2, while their use in the development of case–based applications is described in Section 3.3.
2 Complex Knowledge Structures CoPs are groups of people sharing expertise and passion about a topic and interacting on an ongoing basis to deepen their learning in this domain[16]. One of the most interesting feature of CoPs is their informal nature: a CoP is typically transversal with respect to the formal structure of an Organization. This means that members of a CoP usually belongs to different Business Units of a Company, different departments of a University and so on. Each member is bounded to others by common interests, friendship and other social relationships different from the normal day by day working activities. A CoP is an excellent vehicle for the circulation of ideas, information and knowledge within Organizations, thus they should be able to encourage the development of CoPs. When a new problem arises that a CoP member should solve, an immediate and deep communication process starts, involving all the people belonging to the CoP and eventually other CoPs. The process is typically iterative, ending when the problem is solved and generating new knowledge that will be useful in the future. This process can be called negotiation–reification and represents the way how CoPs are able to manage their core competencies, characterizing themselves as important centers for finding innovative solutions to a wide variety of problems in a lot of domains. The term Complex Knowledge Structure (CKS) will be from now on used to indicate a way to represent all the features of the negotiation–reification process inside CoPs. The reason for considering “Complex” the knowledge structures related to a negotiation and reification process is that a CKS is not only a representation of a simple piece of knowledge owned by CoP members, but also of the social relationships established among the people belonging to the Community during its generation: Definition 1 (Complex Knowledge Structure) A Complex Knowledge Structure is a mean to acquire and represent in a uniform fashion both the domain and social knowledge involved in a CoP negotiation–reification process. Domain knowledge concerns the problem–solving activity, while social knowledge is related to what kind of communications and relationships are established among CoP members in order to solve critical situations. Next section will explain how a conceptual model of the CKS–Net framework has been derived from the intuitive definition above.
3 The Framework In order to build an effective framework for supporting knowledge acquisition and representation when dealing with CoPs it is necessary to focus on three important questions: 1. WHAT are the main elements of a negotiation–reification process, i.e. what is the structure of a problem to be solved in terms of entities that are considered by CoPs’ members and relationships are established among them? 2. WHY CoPs’ members work on the structure defined by the previous point, i.e. what are the goals of the negotiation–reification process? 3. HOW CoPs’ members exploit their experience to modify or change the problem structure according to the wished aims? Moreover, the framework should be flexible enough to be domain–independent. This means that a framework for the management of CKSs should focus on the structural properties of CoPs’ reasoning rather than on the knowledge involved in a particular domain. In other words, the framework should describe the process through which CoPs switch from the definition of a problem features to its solution on the basis of a precise set of results to achieve rather than on the creation of a complete knowledge–model to do the same. Another important feature to take care of is the variability of CKSs pattern, depending on many factors (e.g. kind of domain, problem, people and so on): the adoption of a specific mean to represent it completely and correctly must be evaluated carefully. 3.1 CKS Representation in CKS–Net In order to capture the variability of CKSs, they are represented in CKS–Net by means of graphs (see Figure 1), whose nodes, each one representing an entity involved in CoP negotiation–reification process, are bounded by direct and labelled arcs, where an arc is an instance of relationships between two entities: Definition 2 (Complex Knowledge Structure) A Complex Knowledge Structure is a labelled and direct and graph CKS = hN, Ri where N = Nwt ∪ Nwy ∪ Nhw is a set of nodes and R = Rwt ∪ Rwy ∪ Rhw is a set of relationships among nodes.
WHAT-Node HOW-Node WHY-Node
WHAT-Rel HOW-Rel WHY-Rel
Fig. 1. The representation of CKSs in the CKS–Net Framework
In the definition above, Nwt , Nwy , Nhw (Rwt , Rwy , Rhw ) are the sets of nodes (relationships) for the description of the problem, called WHAT–Nodes (WHAT– Rel), for the specification of the solution to it, called HOW–Nodes (HOW–Rel) and for the definition of benefits/drawbacks, called WHY–Nodes (WHY–Rel)respectively. A graphical representation of the CKS definition is shown in Figure 1. The following two definitions allow to characterize formally the concept of node and relationship in the CKS–Net framework. Definition 3 (CKS Node) A CKS Node n ∈ N is a three–tuple n = hN am, RIN , ROU T i where 1. Nam is a node identifier; 2. RIN ⊂ R ∨ RIN = ∅ is the set (eventually empty) of its ingoing relationships; 3. ROU T ⊂ R∨ROU T = ∅ is the set (eventually empty) of its outgoing relationships. Definition 4 (CKS Relationship) A CKS Relationship r ∈ R is a three–tuple R = hN am, nout , nin i where 1. Nam is a relationship identifier; 2. nout ∈ N is the CKS Node r starts from; relationships; 3. nin ∈ N is the CKS Node r ends in. The two definitions above can be instantiated in order to obtain the definitions of WHAT–Nodes, HOW–Nodes, WHY–Nodes, WHAT–Rel, HOW–Rel and WHY–Rel. Figure 2 shows a graphical representations of the following formal definitions.
WHAT-Rel
WHY-Rel WHAT-Rel HOW-Rel HOW-Rel HOW-Rel
WHAT-Rel
WHAT-Rel
WHY-Rel
HOW-Node
WHAT-Node
WHY-Rel WHY-Rel
WHY-Rel
WHY-Node
Fig. 2. The three kinds of node of a Complex Knowledge Structure. A HOW– Node can have one or more ingoing HOW–Rels and one or more outgoing WHY–Rels, or can be an original WHAT–Node that change its nature due to a looping HOW–Rel. A WHY–Node can have zero outgoing relationship if it represents a simple benfit/drawback, one or more outgoing WHY–Rel if it represents a benefit/drawback with one or more side–effects.
Definition 5 (WHAT–Node) A WHAT–Node nwt is a CKS–Node where the set of ingoing relationships is a subset of the WHAT–Rel set and the set of outgoing relationships is a subset of the union between the WHAT–Rel set and the HOW–Rel set: n ∈ Nwt ⇔ RIN (n) ⊆ Rwt ∧ ROU T (n) ⊆ Rwt ∪ Rwt Definition 6 (HOW–Node) A HOW–Node nhw is a CKS–Node where the set of ingoing relationships is a subset of the union between the WHAT–Rel set and the HOW– Rel set and the set of outgoing relationships is a subset of the union between the HOW– Rel set and the WHY–Rel set: n ∈ Nhw ⇔ RIN (n) ⊆ Rwt ∪ Rwt ∧ ROU T (n) ⊆ Rhw ∪ Rwy Definition 7 (WHY–Node) A WHY–Node nwy is a CKS–Node where the set of ingoing relationships is a subset of the WHY–Rel set and the set of outgoing relationships is a subset of the union between the WHY–Rel set and the empy set: n ∈ Nwy ⇔ RIN (n) ⊆ Rwy ∧ ROU T (n) ⊆ Rwy ∪ ∅ According to the definitions of node above, the following definitions, graphically represented in Figure 3, for WHAT–Rel, HOW–Rel and WHY–Rel can be given. Definition 8 (WHAT–Rel) A WHAT–Rel rwt is a CKS Relationship where the starting node nout and the ending node nout belong to the Nwt set:
HOW-Rel HOW-Node
WHAT-Node WHAT-Rel
HOW-Rel WHAT-Node
WHAT-Node
WHAT-Node HOW-Rel
WHAT-Rel
WHY-Rel HOW-Node WHY-Rel WHY-Node
WHY-Rel
WHY-Node
Fig. 3. The three kinds of relationships in Complex Knowledge Structure representation. A WHY–Rel can be used to express the benefits of an adopted solution or a causal relationships between two WHY–Nodes, representing an effect and a side–effect respectively.
r ∈ Rwt ⇔ nout (r) ∈ Nwt ∧ nin (r) ∈ Nwt Definition 9 (HOW–Rel) A HOW–Rel rhw is a CKS Relationship where the starting node nout belongs to the Nwt set and the ending node nin belongs to the union between the Nwt and the Nhw sets: r ∈ Rhw ⇔ nout (r) ∈ Nwt ∧ nin (r) ∈ Nwt ∪ Nhw Definition 10 (WHY–Rel) A WHY–Rel rwy is a CKS Relationship where the starting node nout belongs to the union between the Nhw and the Nwy sets and the ending node nin belongs to the Nwy set: r ∈ Rwy ⇔ nout (r) ∈ Nhw ∪ Nwy ∧ nin (r) ∈ Nwy WHAT–Rels and HOW–Rels are generic: their semantic depends on the context the CKS to be represented is related to. This is the most important characteristic of CKS–Net: by its exploitation it is possible to describe CoPs and, in particular, the negotiation–reification process, from multiple point of views. For instance, WHAT–Rels could be used to illustrate a negotiation–reification process in terms of – Social relationships among CoP members. In this case, each WHAT–Node bounded by a WHAT–Rel represents a person involved in the negotiation– reification process, and the WHAT–Rel explains what kind of interaction is established between them (e.g. asks to, helps, doesn’t speak to, etcetera);
– Know–How relationships among the parts of a product or the phases of a production process. In this case, each WHAT–Node represents a product or production process component and a relationship between them represents a piece of core–knowledge owned by the whole CoP (e.g. is a, is made of, is followed by, etcetera). – Interactions among different CoPs. In this case, each node is a specific CoP, and the relation between them specifies what kind of cooperation exists when they have to solve a common problem. A CKS devoted to represent interactions among different CoPs will be similar to the CKS for the analysis of social relationships among CoP members, but it will typically contain a smaller number of nodes and relationships. WHY–Rels are typically meant as causal relations: – if a WHY–Rel links a HOW–Node to a WHY–Node it specifies that a HOW– Rel has been previously instantiated in order to obtain an effect represented by the WHY–Node; – if a WHY–Rel links a WHY–Node to another one it specifies that a side– effect, represented by the node the WHY–Rel enter, has been obtained by the instantiation of a specific HOW–Rel. The side–effects could be positive, negative or not important. 3.2 Isolated and Critical Nodes In the last subsection it has been described how a CKS is described within the CKS–Net framework. Now, attention will be focused on how the graph–based representation of CKS described so far can be exploited in order to make CKS– Net an effective tool for supporting CoPs in their activities. First of all, the following definitions should be given to identify two important kinds of nodes. Definition 11 (Isolated Node) A node belonging to the net that represents a specific Complex Knowledge Structure is said isolated if there are no relationships which bind it to any other node. Definition 12 (Critical Node) A node belonging to the net that represents a specific Complex Knowledge Structure is said critical if it allows to preserve the structural properties of the net itself. The criticality level of a node can be evaluated according to a couple of distinct kinds of measures (see Figure 4). Up to now, a Complex Knowledge Structure has been considered as the representation of a problem solved by a CoP exploiting a negotiation among its members. But a Complex Knowledge Structure can be profitably used to describe unsolved problems too: Definition 13 (Unsolved Complex Knowledge Structure (UCKS)) A Complex Knowledge Structure is said to be Unsolved if Nhw and Rhw are empty sets.
1. CKS with no isolated WHY-Nodes
2. CKS with isolated WHY-Nodes
B A Outcome Outcome
Description
Description
Critical Node WHAT-Rel
B
HOW-Rel WHY-Rel
Fig. 4. Critical nodes when 1. the CKS has no isolated WHY–Node and 2. when the CKS has at least one isolated WHY–Node
For this reason, a criticality measure for nodes of a Complex Knowledge Structures has been introduced taking care of HOW–Nodes, while a second criterium has been followed to establish whether a node of a UCKS is critical or not: Definition 14 (Critical Nodes for CKSs) A node n ∈ N of a Complex Knowledge Structure is said critical if one of the following conditions is satisfied: – n ∈ Nhw ; – n ∈ Nwy ; – n ∈ Nwt ∧ ∃m ∈ Nhw , R ∈ Rhw | nRm. Definition 15 (Critical Nodes for Unsolved CKSs) A node n ∈ N of an Unsolved Complex Knowledge Structure is said critical if one of the following conditions is satisfied: – – – –
n ∈ Nwy ; n ∈ Nwt ∧ ∀m ∈ N | nRm @p ∈ N | pRm; n ∈ Nwt ∧ ∃R ∈ Rwt | nRn; n ∈ Nwt ∧ RIN (n) = ∅ ∧ ROU T (n) = ∅.
The introduction of Unsolved Complex Knowledge Structures allows to adopt a case–based strategy to find a possible solution to the problem represented by them on the basis of their comparison with past Complex Knowledge Structures: a retrieval algorithm has been designed based on the comparison of critical nodes, that is the subject of the next section. A last important notion must be introduced in order to understand how it works, that is the equivalence of critical nodes, shown in Figure 5:
1. A and B are the same node
2. A is more generic than B
A Jane
3. A and B have the same incoming relationships (SPEAK)
A
3. A and B a HOW-Node and an ISOLATED WHAT-Node respectively
SPEAK A
A
B MADE OF
MADE OF A
SPEAK
SPEAK
B Jane B
B
B IS-A PART-OF
Fig. 5. Equivalence between two critical nodes a and b when 1. a and b are the same nodes 2. there is a direct (e.g. part–of) or indirect (e.g. is–a) relationship linking a and b or viceversa 3. a and b are different but they have the same ingoing relationships’ set 4. a and b are a HOW-Node and an Isolated WHAT-Node respectively.
Definition 16 (Equivalent Critical Nodes) Given two Complex Knowledge Structures CKS1 and CKS2 and two critical nodes a ∈ NCKS1 and b ∈ NCKS2 . Two critical nodes a and b are said to be equivalent if one of the following conditions is verified: 1. 2. 3. 4.
a = b; a 6= b ∧ ∃R1 ∈ R(a), R2 ∈ R(b) | aR1 b ∨ bR2 a; a 6= b ∧ RIN (a) ∩ RIN (b) 6= Ø; a 6= b ∧ a ∈ Nhw (CKS1 ) ∧ b ∈ Nwt (CKS2 ) ∧ RIN (b) = ∅ ∧ ROU T (b) = ∅ or viceversa.
3.3 Using CKSs: a Retrieval Algorithm A sketch of the CKS–Retrieval algorithm designed and implemented in the CKS–Net framework is reported in Figure 6. The algorithm works on the Complex Knowledge Structure Memory, indicated by the acronym CKSBASE, and receives in input the representation of the problem to be solved in the UCKS form. The first step is the identification of UCKS critical nodes, put in the CRITU CKS set, according to the definition given above. Then, the IndexedCKS set is initialized to the empty set. This set will contain the past CKS which will be considered for being compared to the current UCKS. The first important operation of the CKS–Retrieval algorithm is the CKS–Base indexing: the critical node set of every CKS contained in CKSBASE is compared to CRITU CKS on the basis of equivalence definition previously given. All the equivalent nodes are added to an equivalent nodes’ set, called EquivalentN ODESi . If this set is different from the empty set, the current CKS is indexed by adding it to the IndexedCKS collection. Then, the similarity function comparing the UCKS in input and all the indexed CKSs is applied. A FUSION function finally allows to reuse the solutions of retrieved CKSs in order to build the solution to the problem represented by UCKS. The similarity function SIM (U CKS, IndexedCKSi is defined by the equation 1.
Algorithm CKS–Retrieval(IN: UCKS) CRITU CKS = {n ∈ NU CKS | n is critical} IndexedCKS = ∅ dimCKSBASE = Card(CKSBASE) i=0 While (i ≤ dimCKSBASE ) Do CKS = CKSBASEi CRITCKS = {m ∈ NCKS | m is critical} EquivalentN ODESi = {n ∈ CRITU CKS m ∈ CRITCKS | n, m are equivalent} If (EquivalentN ODESi 6= ∅) Then IndexedCKS = IndexedCKS ∪ CKS End If i=i+1 End While For (i = 1; i ≤ Card(IndexedCKS ); i = i + 1) SIM = SIM (U CKS, IndexedCKSi If (SIM > 50) Then Fusion (UCKS, IndexedCKSi ) End If End For End Algorithm
Fig. 6. The pseudo–code for the CKS–Net retrieval algorithm.
SIM =
n
) wy ( Card(EN Ncc +N c p ∗ 100) + ( Nwc +Nwp ∗ 100)
2
(1)
where – – – –
Card(EN ) is the cardinality of the equivalent node set; Ncc is the number of critical nodes of the current UCKS; Ncp is the number of critical nodes of the CKS; nwy is the number of WHY–Nodes that the current UCKS shares with the CKS; – Nwc is the number of WHY–Nodes nodes of the current UCKS; – Nwp is the number of WHY–Nodes nodes of the CKS. The similarity function is divided into two parts, the one devoted to calculate how many critical nodes the current UCKS shares has in common with the already solved CKS, the latter devoted to determine how many objectives of the current UCKS were already achieved in the negotiation–reification process represented by the current Complex Knowledge Structure. The CKS is evaluated for the FUSION step if and only if the total similarity degree reaches a value of 50%. This value is a first attempt to find a good treshold for considering similar a past CKS to a UCKS. Since the similarity function
is splat into two parts, the one devoted to capture all the features of the structure (i.e. description, solution and outcome), the latter focused only on outcome issues, it is reasonable to suppose that each of them can contribute at most for the 50% of the total similarity degree. Thus, the algorithm discards a CKS from the retrieved CKS set if at least one of the following two conditions is satisfied: – the equivalent node set contains a number of critical nodes that is less than the half of the total critical node quantity (i.e. Ncc + Ncp – the equivalent node set contains a number of critical WHY–Nodes that is less than the half of the total critical WHY–Nodes’ quantity (i.e. Nwc + Nwp In this way, the probability to obtain a good solution for the UCKS through the Fusion step increases. 3.4 Reusing Solutions in CKS–Net The Fusion operation (see Figure 7) allows to reproduce HOW–Rels and WHY– Rels of retrieved CKS on the current UCKS. Fusion is an instance of the REUSE phase of CBR 4’Rs cycle, designed as an iterative algorithm that takes care of all CKSs which has overcome the similarity threshold. In this way, more than one HOW–Rel can be shown that allow to reach the same goal. Moreover, it is possible that all the aims of current UCKS are achieved by a fusion of all the solutions adopted in the past: in fact, by reusing only the HOW–Rels concerning the most similar past CKS, as well as during the retain phase of a CBR system, it is possible that some new goal don’t find a corresponding outcome in the retrieved CKS representation. To take care of all the possible candidates in the retrieved CKSs’ set increases the probability to cover all the requirements specified by the UCKS representation. Building a solution for the current UCKS starts with the comparison of its critical nodes with the ones of the retrieved CKS. This is accomplished in the first part of the Fusion function, where each critical node n belonging to the WHAT–Node set of the CKS in input is tested for equivalence with any critical node m of UCKS. In case of success, the process of building a solution starting from the node m begins. Since the relationships are not bidirectional, the only way to accomplish this task in CKS–Net is build a set of all possible HOW–Nodes, HOW–Rels and WHY–Rels in order to link the hugest amount of WHY–Nodes. For this reason, the initially empty Rhw (U CKS) and Nhw (U CKS) sets are added with all how relationships having n as the outgoing node and all the nodes belonging to Nhw (CKS) which are linked to n by a HOW–Rel. Then, WHY–Nodes and WHY–Rels are considered: if there are two equivalent WHY– Nodes w and y in the representation of current CKS and UCKS respectively, the link between w and a node of Nhw (CKS) is reproduced in UKCS. At the end of this step, it could be possible that some How–Nodes Nhw (U CKS) haven’t outgoing WHY–Rels: these nodes are collected in the DelN odehw set since they represent pieces of the retrieved solution which cannot be used to reach current goals. Moreover, the How–Rels linking the m node to each element of
Fig. 7. The pseudo–code for the Fusion operation of the CKS retrieval algorithm.
DelN odehw are added to the DelRelhw set. Finally, the latter sets are subtracted from the initial Nhw (UCKS ) and Rhw (U CKS) sets respectively.
4 An Example As an example of how the CKS–Net framework can be used to develop Knowledge Management Systems, the P–Truck Tuning system, that is a component of a wider project named P–Truck, will be briefly described in this section. The P–Truck Project aims to support the decision making process of experts involved in the truck tire production at the Business Unit Truck of Pirelli Tires (Pirelli Tires is the leader producer of tires in Italy). A truck tire is a very complex product and its life–cycle can be divided into the following main phases: – Design of rubber compounds: a rubber compound is a blend of different ingredients, both natural (e.g. natural rubber, resins) and synthetic (e.g. carbon blacks, oils). This design phase has to decide the blend composition, identifying a set of ingredients and their amount, in order to achieve the performances that are required for the blend and for the tire (e.g. tensile strength, resistance to fatigue). – Mixing: the ingredients must be suitably mixed in order to obtain a homogeneous blend with the required viscosity (again, related to rubber compound and tire properties).
– Semi–manufactured production: reinforcements are added to rubber compounds, producing the different parts that will compose the tire. – Assembly: semi–manufactured parts are assembled into a semi–finished product (i.e. green–tire, in the tire jargon). – Vulcanization (Curing): the green tire is processed in order to give it the required thermal–mechanical features. The above summarized life–cycle is sufficiently general to characterize all tire production realities, where different specific way to perform it can be possible. In the specific case of the Business Unit Truck of Pirelli Tires, a knowledge acquisition campaign has revealed that each of the above summarized phases is accomplished by a specific Community of Practice (CoP). – Compounding, a rule–based system that supports the CoP of compound designers; – Mixing, a rule–based system that supports the CoP of Mixing technologists; – Curing, a case–based system that supports the CoP of Vulcanization technologists; – Tuning, a case–based system that supports a CoP whose aim is to handle possible anomalies that may occur during the production phases. Members of this CoP come from other design CoPs and, when needed, they negotiate to solve the encountered anomaly.
Fastener
Other elements
Carbon Black MADE OF
IS
MADE OF
MADE OF Rubber Compound
CB0
MADE OF
Natural Rubber
Fig. 8. A sketch of a rubber compound recipe described in the CKS–Net framework by means of WHAT–Nodes and WHAT–Rels. In particular, the P–Truck Tuning module has been designed and implemented according to the CKS–Net framework: Anomalies are occasional events which can occur during each of the truck tire production phases, causing a
block of the process and, consequently, loss of time and money. The most important difficulty that experts have to tackle is that there is no possibility to prevent anomalies: although there are some typical situations which can be scheduled and managed (e.g. machineries maintenance or lack of ingredients for rubber compounds) most events are completely out of control by human beings (e.g. machineries breakdown). For this reason, no standard ways to approach this kinds of problems can be modelled. Since anomalies are typically detected during the manufacturing of the product, who launches a warning about them are generally mixing designer and curing technologists CoPs. They receive feedbacks from the production plant about the reasons which blocks the process and start the tuning step. In this Section it will be supposed that compound designer has already produced a rubber compound recipe, and anomalies can only occur during mixing or curing phases. Figure 8 shows a sketch of the recipe taking care only of the most important ingredients (i.e. natural rubber, carbon black, and fasteners), while the other element node is used to indicate that rubber compounds are made of less important elements too. When the recipe is received by Mixing designer, he/she notices that the adopted carbon black CB0 is characterized by high value of the superficial area parameter. This could cause rubber compound viscosity becomes too high with problems from the mixing ingredients point of view. For this reason, it enriches the representation of rubber compound recipe in Figure 8 with an additional part that explains what is its problem and what kind of objectives he/she wants to obtain through an eventual solution. The result is the Unsolved Complex Knowledge Structure shown in Figure 9.
Fastener
LACKS
Other elements
Carbon Black MADE OF
MADE OF Rubber Compound
TO OBTAIN
Suggestion
ASK MADE OF
IS IS
Compound Designer
Carbon Black MADE OF
Rubber Compound
TOO VISCOUS
TOO VISCOUS
CB2 CAUSES
Lower Viscosity CB1
CB0
TO OBTAIN
MADE OF
HIGH SUPERFICIAL AREA
IS SUBSTITUTED BY Natural Rubber
Lower Viscosity PART A
PART B
Fig. 9. PART A: An UCKS representing the problem that the chosen carbon black type causes too high viscosity of the rubber compound to be mixed. PART B: A CKS representing a solution adopted in the past to solve the problem described in PART A.
Then the retrieval algorithm can be applied. The first algorithm step is the definition of the UCKS critical node set: CRITU CKS = {CB, CB0, RC, LV }
where CB, CB0, RC and LV stands for Carbon Black, CB0, Rubber Compound and Lower Viscosity nodes in Figure 9 respectively. Then, the retrieval algorithm scans the CKS–Base looking for most similar past Complex Knowledge Structures to the current UCKS. In order to give a demonstration of how it works, the Complex Knowledge Structure drawn n the PART B of Figure 9 will be considered. First, the algorithm builds the set of indexed CKSs. To this aim, it builds the critical node set of each CKS in CKS– Base and checks that its intersection with CRITU CKS is not empty, by building the set of equivalent critical nodes. In the case of Figure 9, the algorithm will return the following set of critical nodes CRITCKS = {CB1, CB2, SU, LV } where CB1, CB2, SU and LV stands for CB1, CB2, Suggestion and Lower Viscosity nodes in Figure 9 respectively. Then, the set of equivalent critical nodes of UCKS and CKS is not empty: EquivalentN ODESi = {CB, CB1, CB0, LV } where CB belongs to the set due to the second condition of definition 16, CB1 and CB0 because of the third condition of definition 16 and LV as it satisfies the first condition of definition 16. For this reason, the CKS shown in Figure 9 can be considered for the calculus of similarity with the current UCKS. According to the equation 1 the similarity value between the current UCKS and the indexed CKS is µ 4 ¶ ( 8 ∗ 100) + ( 23 ∗ 100) SIM = = 58.5% 2 Thus, the CKS of Figure 9 is a good candidate for the FUSION operation. The result is the Complex Knowledge Structure described in Figure 10.
Fastener
Other elements
Carbon Black CB2 MADE OF
IS
MADE OF
MADE OF
TOO VISCOUS
Rubber Compound
TO OBTAIN
Lower Viscosity
MADE OF CB0
HIGH SUPERFICIAL AREA
Natural Rubber
IS SUBSTITUED BY
Fig. 10. The result of FUSION operation between the current UCKS and the retrieved CKS.
5 Discussion CKS–Net is a framework for the development of case–based applications that aims to become an effective support tool for Communities of Practice. Thus it adopts a graph–based representation of the case (i.e. the CKS). Graphs are probably the most flexible kind of structures to properly describe the negotiation– reification process but are extremely difficult to be managed. With respect to other kinds of approaches (see e.g. [4] and [6]) CKS–Net allows to represent in a uniform way different kinds of knowledge and information. Moreover, the adoption of critical nodes as representatives of the whole graphical representation of a CKS has allowed to design a simpler and more general retrieval algorithm than others: anyway, the CKS–Retrieval Algorithm proposed in this paper is not particularly efficient, since its performance can be evaluated as an O(n7 ) in the worst case, where n is the number of nodes of the widest retrieved CKS. Indeed, this evaluation doesn’t make CKS–Retrieval Algorithm comparable to other approaches (see e.g. [3]) from the timing performance perspective at the moment. Another important feature of CKS–Net is the use of WHY–Nodes in the calculus of total similarity between an Unsolved Complex Knowledge Structure and a past Complex Knowledge Structure. This is necessary to completely support the negotiation–reification process among CoPs’ members, since the goal to be achieved is one of the most important reasons to start it. From the CBR point of view, taking care of WHY–Nodes is equivalent to consider the outcome part of a case–structure during the similarity evaluation. This is not a novelty in the CBR area (see e.g. [11]); anyway, the notion of equivalence among critical WHY–Nodes allows to discard from the similarity calculus secondary goals in an automatic way, as well as to take care of both positive and negative outcomes without any direct intervention by the user. The main drawback of the approach is its complexity, due to the presence of three different kinds of nodes and relationships. Although they allow to make clear all the steps of a negotiation–reification process inside a CoP, people belonging a CoP must learn how a problem description, solution and outcome must be designed, implemented and linked the one to each other. This task is made more difficult by the absence of a graphical support tool.
6 Conclusions and Future Works This paper has presented CKS–Net, a conceptual and computational framework for the acquisition, representation and use of Complex Knowledge Structures involved in the CoP negotiation–reification process. One of the more interesting characteristic of CoPs is that they are able to find solutions to problems through the sharing of experiences acquired by their members in the past and the usage of them to tackle challenging situations. In order to favorite the storage of all the knowledge involved in the decision making process of a CoP, the following choices have been taken:
– a Complex Knowledge Structure is a graph, with two kinds of nodes and three kinds of relationships among them; – a WHAT–Node is used to identify an entity necessary to describe the problem; – a HOW–Node allows to identify a step of the problem solving strategy; – a WHY–Node is used to define a goal of the negotiation–reification process; – a WHAT Relationship is used to describe the nature of link between two WHAT–Nodes; – a HOW Relationship is used to illustrate a step in the reasoning process to solve a new problem; – a WHY Relationship is used to show the benefits of a problem solving step represented by a How relationship.
Fig. 11. A UML scheme of the package for the definition of CKS structure in CKS–Net From the knowledge use point of view, a similarity metric based on structural properties of the graph has been designed in order to compare Complex Knowledge Structures and adopt solutions to past problems as starting points for solving new ones. This metric works on critical nodes, which have been defined as important nodes for the preservation of Complex Knowledge Structure morphology. Two distinct methods for calculating the criticality of a node have
been given, the first one devoted to identify critical nodes for CKSs representing solved problems, the latter devoted to identify critical nodes for unsolved problems. The design of a similarity metric and the interpretation of CKSs as cases make CKS–Net a natural framework for the development of case–based applications to support Communities of Practice. The framework has been applied to develop P–Truck Tuning, a module of the P–Truck system [2] for supporting different Communities of Practice involved in the truck tire production at Pirelli Tires in solving possible process anomalies. The system is currently working and has been integrated into the general architecture of the whole system.
Fig. 12. A UML scheme of the package for the implementation of retrieval strategy in CKS–Net To make more simple the design of case–based systems exploiting CKS–Net, an implementation of this framework has been recently realized based on J2EE technology. Figures 11 and 12 show two UML packages used to design the application: – the package CaseDef contains the classes necessary to represent the case structure as an aggregation of Elements (i.e. the nodes of a Complex Knowledge Structure) and Relations among them. Each Element is linked to OutRel and InRel classes, whose instances are a list of all the relation outgoing and ingoing respectively and are useful in the determination of critical nodes; – the package CaseBase contains all the classes necessary to implement the retrieval strategy of CKS–Net. In particular, the class Analyzer establishes what are the critical nodes of a case, according to definitions of criticality measurement previously introduced and the class Retriever is responsible for the retrieval of past cases similar to the current one according to the similarity metric defined by equation 1.
Future works about CKS–Net will be devoted to refine the methods for the comparison of Complex Knowledge Structures, in an attempt to build a library of similarity metrics each one devoted to be used according to the context. Moreover, it will be completed the implementation of the framework that is at a prototypal state at the moment.
References 1. Aha, D., et Al., Knowledge Based Project Planning, In R. Dieng-Kuntz, N.Matta: Knowledge Management and Organizational Memeories. Kluwer Academic Publisher, 2002 2. Bandini, S., Manzoni, S. and Sartori, F. Knowledge Maintenance and Sharing in the KM Context: the Case of P–Truck, AI*IA 2003: Advances in Artificial Intelligence, LNAI 2829, Springer-Verlag, Berlin Heidelberg New York, pp. 499-510 , 2003. 3. Bunke, H. and Messmer, B.T., Subgraph Isomorphism in Polynomial Time, Technical Report IAM 95-003, University of Bern, Institute of Computer Science and Applied Mathematics, Bern, Switzerland, 1995. 4. P. A. Champin and C. Solnon. Measuring the similarity of labeled graphs,iIn K. D. Ashley and D. G. Bridge, editors, Advances in Case-Based Reasoning, LNCS 2689, pages 80–95. Springer, 2003. 5. Davenport, T., Prusack, L., Working Knowledge - How Organizations Manage What They Know, HBS Press, 1998. 6. L. Macedo and A. Cardoso, Nested graph-structured representations for cases, In Barry Smyth and Padraig Cunningham, editors, Advances in Case-Based Reasoning, volume 1488 of Lecture Notes in Computer Science, pages 1–12. Springer, 1998. ISBN 3- 540-64990-5. 7. Nonaka, I. and Takeuchi, H., The knowledge-creating company: how Japanese companies create the dynamics of innovation, Oxford University Press, New York, NY, 1995. 8. Finnie, G., Sun, Z. Similarity and Metrics in Case-based Reasoning, Intelligent Systems, 17(3) 2002 pp 273-285 9. Hannemann, R. A., Introduction to Social Network Methods, Department of Sociology, University of California - Riverside, 2001. 10. Kolodner, J. Case-Based Reasoning, Morgan Kaufmann, San Mateo (CA), 1993. 11. Ricci, F. and Del Missier, F, Supporting Travel Decision Making through Personalized Recommendation, In Clare-Marie Karat, Jan Blom, and John Karat (eds.), Designing Personalized User Experiences for eCommerce, pp. 221-251, Kluwer Academic Publisher, 2004. 12. Robertson, J., 2002, Benefits of a KM Framework, Intranet Journal, available at http://www.intranetjournal.com/articles/200207/pse 07 31 02a.html 13. Santosus, M., Surmacz, J., The ABCs of Knowledge Management, available at http://www.cio.com/research/knowledge/edit/kmabcs.html, 2002. 14. Watson, I., A Knowledge Management Initiative by UK Local Government, In R. DiengKuntz, N.Matta: Knowledge Management and Organizational Memeories. Kluwer Academic Publisher, 2002 15. Watson, I., Applying Knowledge Management: Techinques for Building Organizational Memories, In Craw, S., Preece, A., (eds.), Advances in Case Based Reasoning, LNAI 2416, Berlin, Springer-Verlag, 2002 16. Wenger, E., Community of Practice: Learning, meaning and identity, Cambridge University Press, Cambridge, MA, 1998.
