Knowledge Management for Administrative Knowledge†

Chandra S. Amaravadi

Department of Information Management and Decision Sciences

College of Business and Technology

Stipes Hall 435

Western Illinois University

Macomb, IL 61455

Ph:309-298-2034

Email:

Paper Submitted to

IEEE Intelligent Systems

July 2003

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†A previous version of this paper was Presented at the Second European Conference on Knowledge Management, Bled, Slovenia, Nov 8th-9th, 2001


Knowledge Management for Administrative Knowledge

Abstract – Administrative knowledge or office knowledge is the knowledge used in conjunction with the support operations in an organization. Systems managing this knowledge are referred to as Extended Office Systems (EOS). EOS will be used to support knowledge exchanges in organizations. The types of knowledge handled by EOS are illustrated and their characteristics highlighted. Based on these characteristics, a formalism is proposed, which utilizes structural and descriptive links to achieve an extensible, open-ended representation. A prototype system using the representation and incorporating approximately two hundred items of knowledge has been developed and can answer questions about a software engineering company.

Index Terms: knowledge management, knowledge management systems, knowledge management models, office information systems, administrative support, administrative knowledge.

I. INTRODUCTION

The explicit management of organizational knowledge or knowledge management (KM) is increasingly a competitive response in many organizations. Knowledge can exist implicitly in the form of mental schemas, shared metaphors and experiences or explicitly in the form of documents, procedures and job descriptions. Much of the KM literature has focused on the methods to manage the implicit type of knowledge, and includes case studies, assessment techniques and organizational processes (Hackbarth and Grover, ’99, Mann et al. ’97, Martiny ’98). Comparatively little attention has been given to explicit knowledge and the technical problems of organizing it. Further, the emphasis in the KM literature has been on strategic/professional type of knowledge rather than on operational or administrative knowledge. We define operational or administrative knowledge as the knowledge used in conjunction with the support operations in an organization such as administering benefits, or troubleshooting problem accounts (Garvin ‘97). Case studies suggest that employees spend a significant amount of time in obtaining administrative knowledge. The lack of such knowledge can seriously hinder office operations. In one consulting firm for instance[i], knowledge of project costing is embedded in the minds of its project managers, preventing the company from bidding for projects in their absence. We will refer to systems managing operational knowledge as Extended Office Systems (EOS) to denote the fact that this variant of KM systems will be based on extending existing office technologies.

II. EOS AS A KNOWLEDGE EXCHANGE

In the knowledge management literature, a number of metaphors have been advanced to characterize the acquisition, storage and retrieval of knowledge. Hackbarth and Grover (1999), propose accumulating organizational memory in a knowledge repository based on the framework proposed by Walsh and Ungson. In their conception, the repository consists of six bins, the individual, information, transformation, structure, culture and ecology which are organized to facilitate retrieval. Thus they rely on the metaphor of a Knowledge Repository. Similarly, Glance et al. (1998), introduce the concept of a Knowledge Pump, which collects and distributes knowledge to employees in an organization. We will use the Knowledge Exchange metaphor to characterize a similar idea. EOS will function as exchanges to collect knowledge from office workers and to distribute it to those who need it. Such systems will be based on the collective knowledge of all office workers, implying that they will actively contribute to the system; there are unfortunately no tests of completeness due to the amorphous nature of the commodity. At the same time, as there are no upper limits to the knowledge, the system must be robust and designed for volume. It is anticipated that for large organizations an EOS system will handle at least 10,000 items of knowledge. The participants of an EOS will be both producers and consumers of knowledge and the system must accommodate this dual role. Since the success of the system will depend upon the extent to which employees contribute their knowledge, the cost of participation must be minimal. Correspondingly, the interfaces must be unobstrusive but ubiquitous. Each transaction with the participant involves either collection or dissemination of knowledge. Unlike in systems, like EPRINET (Mann et al. ’97) which rely on specialists to enter and manage the knowledge base, knowledge in an EOS will be entered and updated by office workers. Consequently the system design must accommodate knowledge inputs in the raw form, viz. natural language. Given the current limitations in natural language processing we will relax this requirement in this paper and impose a transaction-overhead on users in the form of manual knowledge editing. The system should also handle incomplete knowledge since organizational knowledge is often fragmented. Thus in a software engineering context, a company might know only the budget, the project mission and the scheduled due date of a particular project. Other details such as the target environment and team composition may be known later. When accepting new knowledge, the system needs to examine the stored knowledge to ensure there are no contradictions. We will also relax this requirement in this paper, as our focus is simply a robust design for administrative knowledge, that can serve as a foundation for EOS. The principles of an EOS are summarized below:

Principle 1: An EOS will function as a knowledge exchange, collecting, storing and disseminating knowledge. It will have the necessary facilities to support this function.

Principle 2: The scope of an EOS will be defined by its use. Thus the greater the usage the greater its scope. An EOS will be restricted to administrative knowledge.

Principle3: The exchange facilities must be ubiquitously accessible.

Principle4: The efficiency of the exchange facilities must be independent of volume of

usage.

Principle5: Participants in the exchange will be both producers and consumers of

knowledge.

Principle6: The cost of participation in an EOS i.e. the “transaction cost” must be

minimal.

Principle7: The EOS must accept transactions in their raw form viz. natural language.

Principle8: The exchange facilities must support complete knowledge transactions whenever possible.

Principle9: The facilities must be designed with maintenance in mind, thus they must be simple, flexible and robust.

Principle10: The exchange must be implemented with current and widely available technologies.

One can readily visualize the external aspects of the system. The EOS will take the form of a knowledge server with a graphical interface that can be ubiquitously accessed from various applications, including word processing, email and web browsers. The interface will allow users to access, update and visualize organizational knowledge. Knowledge exchanges will ideally take place in natural language.

III. THE NATURE OF ADMINISTRATIVE KNOWLEDGE

As important as the topic is, there is a paucity of literature concerning the nature of administrative knowledge. Lacking empirical evidence, we can infer characteristics based on the representative examples illustrated[ii] in Table 1. The knowledge is routine, diverse, fragmented, open ended, dynamic, implicit and potentially contradictory. As evidenced from Table 1, the number of concepts even within the span of few examples is rather large and diverse viz. projects, training programs, company visits, van schedules, new recruits, software tools, ISO-9000, travel arrangements etc. The nature of the knowledge concerns assertions about these concepts. Yet the knowledge is fairly routine, dealing with day to day issues of office life. There are interrelationships among the concepts. For instance, item#6, “Shank arranges the induction program” is related to the concept of training program (in item #5). The knowledge is incomplete since we do not know the other functions carried out by Shank. Moreover, the knowledge is subject to change. Van schedules, company visits and task assignments can potentially change. New concepts can also be introduced such as for instance, project managers going “on-site.” When a new item of knowledge is introduced, it can potentially conflict with existing knowledge. At the present time we are not examining the issues introduced by such conflicts.

Extended Office Systems are intended to fulfill the ever present need of office workers for operational knowledge of the type illustrated in Table 1. The primary goal of this research is to provide a viable representation scheme for such knowledge without being concerned with advanced issues such as tense, modality and anaphora. Following the characteristics of knowledge identified, the scheme will suitably be declarative, relationship oriented, extensible, open ended and flexible. Popular schemes such as rules and frames will not be suitable given these characteristics. Frames will be restrictive and will impose an overhead in terms of unused slots (due to the knowledge being incomplete), while rules lack the flexibility and the declarativeness necessary for the application. Given these constraints, we favor the use of semantic networks.

Table 1: Illustrative Examples of Administrative Knowledge

No. / Example
1. / Every project has a Business Development Manager and a Project Manager.
2. / BSS cannot own fixed assets.
3. / The Van leaves BSS at 11:00 AM.
4. / A project can be initiated by a CEO or by a PM.
5. / Induction program is a two day training program for new recruits.
6. / Shank arranges the induction program.
7. / Mary White, manager of Manugistics, USA, will be visiting BSS on May 3rd.
8. / Rangarajan of Man M/C systems is the best C++ instructor.
9. / Qualify is an in-house tool to support ISO-9000 procedures.
10. / Travel arrangements are made by Allison.
11. / The travel agency for BSS is Vacation Travels.

IV. KNOWLEDGE ENGINEERING FOR EOS

Semantic nets have been widely used as a vehicle for knowledge representation, particularly in connection with natural language processing. The semantic net was initially introduced into the AI literature by Ross M. Quillian, as a way to model associative memory. The nodes in Quillian’s network stood for word concepts which were pointer-linked to other word concepts mimicking the way in which humans stored related information. The purpose of the net was to simulate the human model of associative memory. Inferencing was carried out by propagating “markers” through the nodes to see if they shared a path in common. For e.g. in order to find if there is a physical path from “happy” node to a “wealth” node, markers are propagated through both nodes and if they intersect i.e. visit common nodes (the “state” of a person), these concepts are considered to be related. Semantic nets are often preferred as a representation scheme due to the declarativeness of the representation, their suitability to model concepts and associations and the ease with which inferencing can be carried out.

Despite their appeal there are several major problems with semantic nets. The first of these concerns the semantics of the structure itself. Although all semantic nets shared a node plus link structure in common, they varied greatly in the meanings (semantics) assigned to these constructs. This is due in part to the different paradigms which were brought to bear on the problem: Implementational, Linguistic, Logical, Conceptual and Epistemic (Brachman ‘79).

The earliest nets were implementational and linguistic level networks. The former type networks are simply data structures implemented with pointers, with no semantics as such. In linguistic nets, the nodes represented word concepts and the links were sometimes logical operators and in some cases, merely pointers. Some types of linguistic nets used links to denote any arbitrary relationship; these were restricted to binary relationships (Woods ‘85).

In logical networks, nodes represent predicates and propositions (“father-of”, “loves”) while links represent logical operators such as conjunction, disjunction, subset etc. Logical notations can be effective but at the expense of notational clarity. For instance, the inclusion of functions (is-greater-than, father-of) and the special way of handling multi-way predicates add to the expressive-ness of the net but detract from its comprehensibility. Conceptual-level networks relied on the case frame approach for representing knowledge. The nodes stood for verb concepts (such as “buy,” “interview” etc.) while the links stood for the verb cases (such as object, instrument, recipient etc.). These type of nets assumed a set of primitives thought to be sufficient to represent natural language but experience has proved that unconstrained natural language is far too rich to lend itself to any set of primitives (Woods ‘85).

In addition to the variation in the notation of semantic nets, there were other fundamental problems in the early networks (Woods ‘85). One is a failure to distinguish clearly between a class and an instance, thereby causing confusion on the part of the interpreter about where to anchor knowledge pertaining to a general type of object (class) and a specific example of an object (instance). A second problem was a failure to separate structural knowledge from assertional knowledge (Brachman ’79). For example, there is a difference in meaning between a wheel being part of a car and a wheel being punctured. In many of the early nets, both types of knowledge were captured with the same link, causing problems in network interpretation. These type of problems were addressed to some extent in Epistemic nets which are networks with knowledge-structuring primitives distinguishing between structural relationships and attributes. For instance, in a car, the engine, transmission etc. can be represented as “structural descriptions” while attributes such as color, make etc were treated as the “roles.” Inspite of these improvements, the focus of these early nets was so much on addressing semantic problems such as expressing beliefs, negations of beliefs, quantification etc. that they neglected an aspect critical to Extended Office Systems, namely that of network organization and appearance. Propositional semantic nets (which represented logical propositions), Partitioned nets (used set operators as basic constructs), procedural semantics (a network of goals), KRS (Marcke et. al. ‘87) and KL-One (a system based on epistemic nets) are all cases in point. In these cases, the profusion of network constructs, their inter-connections and the esoteric notations that were followed tended to render them intractable. These limitations of semantic nets in retrospect, can be attributed to a number of reasons, including: the rather ambitious goal of representing natural language in its entirety, which we are avoiding here; a failure to recognize the mechanisms which could simplify and structure the networks such as separating classes from instances, separating structural and descriptive assertions and assigning proper semantics to the links.