Designing ERM Ontology to Evaluate Records Management System

Transcription

1 Designing ERM Ontology to Evaluate Records Management System Jaffar Alalwan Virginia Commonwealth University Manoj Thomas Virginia Commonwealth University Abstract Organizations seek to improve their records management (RM) system to have better efficiency and to meet legislative requirements. For achieving these two goals, evaluation of RM, which is normally done manually, is a necessity for every firm. In this paper, we design and evaluate an ontology that will help in evaluating RM systems. Building the ontology will be the first step in developing an ontology-based RM evaluation system. We argue that the proposed ontology based RM evaluation system has promising features and benefits. Evaluation by using ontology will raise the efficiency of the evaluation process as well as facilitate sharing and communicating the results of evaluation. 1. Introduction Records are important to preserve information and facilitate planning and decision making. Organizations rely on records to improve transparency, accountability, and performance [16]. The International Standard ISO [14] defines records as information created, received, and maintained as evidence and information by an organization or person, in pursuance of legal obligations or in the transaction of business. Because records document what happens in the organization and why it happens, management of this resource is essential to organizations success. Records management (RM) is defined as "the field of management responsible for the efficient and systematic control of the creation, receipt, maintenance, use and disposition of records, including the processes for capturing and maintaining evidence of and information about business activities and transactions in the form of records" [14]. Shepherd and Yeo [8] claim that the most common form of record is paper-based. However, many organizations are increasingly using digital or electronic records, and these electronic records are distributed throughout organizations. National Archives of Australia Checkup Guidelines [22] assert that electronic records contain spreadsheets documents, multimedia files, , websites, and online transactions. Many private and public organizations share electronic records through e-government systems to conduct business-to-government and government-togovernment transactions. Electronic records management is essential to ensure business efficiency and effectiveness of e-government transactions [32]. In this age of extensive regulatory controls, exhaustive audits and high volumes of electronic transactions, implementing an effective record management system is of utmost importance to almost all organizations. In order to ensure that an organization has an effective RM system, an evaluation method, which acts like a benchmark policy, is necessary. Evaluation of records management is the general assessment of the condition of the agency s records management systems in order to identify major problems and setting priorities for system enhancement [22]. This evaluation process is essential for many reasons. First and foremost, it serves to determine shortcomings in the existing RM system. It helps to specify ways to improve the current system. Last but not least, evaluating RM is important to ensure that organizations meet the standard of governmental and legislative requirements. For instance, organizations that share electronic records through e-government system need to evaluate their records to ensure that they meet the standards specified by the federal government. Currently there exists no standardized process for the evaluation of RM. Many organizations have independently developed manuals, evaluation tools and questionnaire based guidelines for this purpose. For example, the National Archives of Australia has a guideline named Check-up that is used to assess the agencies RM system [22]. The U.S. National Archives and Records Administration has developed the Records Management Self-Evaluation Guide for evaluating RM [30]. If an agency wants to assess its RM systems, it needs to follow the guidelines and answer a set of detailed questionnaire. Currently, there also exists no system that can automate or semiautomate this process. Thus, the process of judging the

2 outcome of evaluating the records turns into a tedious and time consuming process. The objective of this paper is to propose an ontology-based evaluation tool for records management system that can help users in complex multi-criteria evaluation scenarios. For example, if an agency wants to evaluate its RM system to make sure that it follows the legislative standard and requirements, there are four major criteria that RM evaluation generally aims to assess. They are: The legislative requirements that the agency needs to follow The standards, policies, and procedures of records that are related to those specific requirements The training courses that may raise the efficiency of the employees The traditional approach would require the users to fill out a form based questionnaire, the answers to which are subsequently appraised to determine if the criteria have been satisfied. In this research paper we focus on the evaluation of RM in an e-government environment. We argue that utilizing an ontology based approach to assess the RM systems holds promises and benefits. The ontology design presented in this paper will serve as the cornerstone in building an ontology-based RM evaluation system that agencies can use to assess their RM environment. Evaluation using an ontology-based system will improve efficiency of the assessment process by reducing the time and the paper work required for evaluation, and assure more accurate results. Using ontology as the semantic underpinning offers other additional advantages to the RM evaluation system. It establishes a common foundation for sharing contextual knowledge across the myriad users and participating agents. It facilitates the common understanding of the domain which in turn will help to enhance the accuracy and precision of responses to the complex information gathering and assessment exercise. An organization s records are a subset of organizational domain, and the quality of the records and information can be significantly improved when the domain knowledge is represented in a shared ontology. Using ontology as the structuring model for information will therefore help to improve information consistency, reusability, interoperability, and knowledge sharing [33]. To the best of our knowledge, designing an ontology to evaluate records management system is a novel idea that has not been discussed before. The rest of the paper is arranged as follows. In section 2 we provide a brief background on the various existing RM models, their capabilities and differences. In this section, we also discuss the role of ontologies, their application areas and justify the design approach for building the RM ontology. We explain our proposed ERM ontology in section 3. In section 4 we briefly discuss the implementation architecture of the ontology-based RM evaluation system. We outline the limitations and future research directions in section 5. We then conclude the paper in section 6 by summarizing the strengths and weaknesses of the proposed ontology model. 2. Literature review and background Literature review shows numerous electronic RM models. Some of the popular RM models are [11]: the record life-cycle model, the records continuum model, the international council on archives, the British Evaluation Workbook and Methodology, and the national archives of Australia digital recordkeeping guidelines. Among RM experts, records continuum model and records life-cycle model are considered the leaders [2] [8]. Based on the records-lifecycle model, records have lifecycle that starts with birth and end with death similar to biological creatures [17]. A detailed analysis of the literature also reveals a few important concepts that are common to all RM models. All discussions on RM stipulate the role of capacity which is the volume of records that can be stored on the medium. McLeod et al. [15] provides a toolkit to assess the capacity of RM. In addition, personnel and human resources play an important role in RM with specific emphasis on user training. The International Records Management Trust/World Bank [13] recommends that training should include the management of electronic and paper records. The International Standards manifest on Information and Documentation Records Management (ISO ) [14], requires that any organization that seeks ISO should have a specific focus on training. The ISO standards states that Programs for training in requirements for records management and specific practices should encompass the roles and responsibilities of, and be addressed to, all members of management, employees, contractors, volunteers and any other individuals responsible for the whole or part of a business activity of an organization in making records during their work and in capturing those records into records systems. [14]. Policies of records management are another concept that is consistently mentioned by researchers and practitioners conversant on RM models. Horsman [24] discusses the importance of updating policies on digital records to match technological development. He argues that electronic records are facing challenges that require best practice solutions that need to be adopted by public and private organizations.

3 In this research, we rely on ontologies to capture the main concepts that characterize the domain of RM. The ontology will help to foster knowledge sharing among users with different roles and different organizational functions. Gruber [28] defines an ontology as a formal, explicit specification of a shared conceptualization. - conceptualization refers to an abstract model of phenomena in the world by having identified the relevant concepts of those phenomena, explicit means that the type of concepts used, and the constraints on their use are explicitly defined, formal refers to the fact that the ontology is machine readable and shared implies that the ontology should capture consensual knowledge accepted by the communities. Ontologies expressed using the Ontology Web Language (OWL) can provide context representation, meta-language definitions, and semantic reasoning capabilities [21]. OWL is developed as part of the Semantic Web initiatives from W3C and has evolved as the popular open standard for semantic knowledge representation. As a knowledge representation language for expressing the domain knowledge in a machine readable format, OWL describes concepts (class), properties of concepts (attributes in the class) and restrictions on properties using normative RDF/XML format. Ontologies are now extensively used in application domains ranging from collaborative knowledge acquisition systems [31] to Geographic Information Systems [29] and semantic information search and retrieval. A review of the ontology knowledge base and repositories did not reveal any work or literature that deals with the RM domain. However, there are ontologies developed for other application domains that may be utilized in this research. For example, the security ontology developed by Tsoumas and Gritzalis [5] is of interest to extend further the ontology proposed in this paper. Similarly, the privacy ontology [18] [7] targeted to support the privacy of e-commerce applications and transactions is also applicable to this research. We tap into some of these established ontologies to strengthen our model as described later in the paper. Evaluation of record management systems holds many strategic and regulatory benefits. Electronic records management helps to reduce operating costs, improve collaboration, and preserve tactic knowledge [9]. It also helps to ensure that the agencies are in compliance with the legislative standards and requirements. According to National Archives of Australia [22], governmental agencies in Australia need to follow three complementary laws that apply to RM: the Archives Act 1983, the Freedom of Information Act 1982, and the Privacy Act To identify and extract the main concepts of RM assessment domain, we conducted a detailed review of three most popular RM evaluation guidelines. We first reviewed the Australian assessment tool: Check-up [22]. We then analyzed the British Evaluation Workbook and Methodology [26]. The analysis of these two tools revealed numerous redundant concepts. This may be attributable to the comprehensive human involvement commanded in the interpretation and use of the guidelines and procedures. A major hurdle in operationalizing the guidelines is that the data collection is entirely a manual process that requires the interpreter to develop, organize and administer the questionnaire. This process can be time consuming, tedious to execute and painstaking to synthesize the results. Removing all redundant concepts and keeping only the related and unique ones is the first step we adopted in constructing the ontology. The third evaluation system that was reviewed was the Records Management Self-Evaluation Guide [30]. This evaluation guide is comparatively shorter than the first two. Detailed analysis of this guide also revealed redundant concepts but not as many as encountered in the former. The systemic review affirmed an important postulate. Similarities in the same concepts from the three different sources confirm that the semantic model is representative of the domain of interest i.e. the evaluation of RM in an e-governance domain. The three tools provide a uniform representation for similar domain concepts that have the same attributes. This further reinforced the use of ontologies as the right means for modeling domains where commonalities overlap and concept relationships are similar. We call this the Electronic Records Management (ERM) Ontology. Implementing an information system centered on the ERM ontology will be useful for building applications whereby automated or semiautomated inferences may be drawn on the assessment of RM in an e-governance domain. 3. The ERM ontology Ontology development is an iterative process and requires the researcher to gain a thorough understanding of the problem domain. It is a common practice to verify the suitability of existing ontologies by testing against well-defined use cases [10] [1]. Available ontologies can also be extended or new ontologies can be created. After gaining a thorough understanding of the domain and the scope of the RM ontology, an exhaustive search of the ontology repositories and related literature was conducted to identify any existing

4 ontologies that could be used or extended for this research. Although no ontologies were identified that specifically addressed the research problem, the exercise was fruitful in helping identify other useful ontologies, specifically the security ontology [5] and the privacy ontology [18], that could be used to augment the ERM ontology. The Protégé Knowledge Acquisition System [25] was used to model and build the ERM ontology [19] Two domain experts and practitioners familiar with RM were consulted to verify the concepts definitions, logical axiom, and the relationships between the concepts identified from the three RM assessment guidelines. They also helped to fill in details and to condition the ontology for applicability and extensibility. The choice of the class names and relationships used in the ontology directly correlates to the concept terminology defined in the three evaluation guidelines and the collective acumen of the domain experts overlooking the development process. Although changes to the base ontology were rare once the knowledge experts agreed on the final design choice, fine adjustments were made throughout the development process as scenario based testing (refer section 4.1.1) revealed minor weaknesses and boxes in figure 1). In the interest of brevity and space, only the most relevant concepts are shown in figure 1 and described in the following section Records management system The Record Management System concept is at the heart of the ERM ontology. It has Type of Records concept which identifies all organization records that are classified as paper-based, electronic-based, and online records. Each of these categories is a subconcept of Type of Records concept and each one of them describes two types of records. The first one is the permanent records that contain the daily regular records and the vital records that are essential to continue the business processes under abnormal conditions. The second type is the temporary records that are kept in the system for short duration. One essential aspect of the records management system is the Record Guidance subconcept. This subconcept captures the Record Cycle rules, specifically the rules of Creation, Maintenance, and Disposition. In addition, this subconcept also includes the records Standards, which determine the standards for classification, retrieving, indexing, and Figure 1: The concepts relations of the ERM ontology concerns. Based on our evaluation of the three RM assessment tools, we identified three main upper-level concepts. They are: Records Management System, Training, and Personnel (represented by the blue solid filing. Furthermore, this subconcept also identifies the Legal Requirements that contain the legislative acts and policies. Figure 2 shows the OWL snippet for a relationship named hasstandards between Legal

5 requirement and Standards subconcepts (refer [19] for a more detailed description of the OWL syntax). Figure 2: HasStandards relationship At the same time, the relationship between all parts of Record Guidance and the Types of Records subconcept can be described using OWL as shown below in figure 3: Figure 3: IsImplemented relationship Three important subconcepts are linked directly to the concept of RM system -'Backup, Privacy, and Security. Backup concept references all backup principles such as keeping an offsite copy of all organization records, continuity of operation and disaster recovery plans. Security and Privacy concepts are obviously important, and we are suggesting linking this part of our ontology to ontolgies that are designed for these purposes such as the ones developed in [5] [18]. Employees have access to the Record Guidance so they know the required rules to deal with the records. At the same time, employees should receive training, which is discussed in the next section. Extension to this will include linking our ontology to existing ontologies on roles and authority such as those in [20]. 3.3.Training The third main upper-level concept conceptualizes training. Employees need to have the required training to be able to handle records [13]. Training courses should match the Role and Authority for each employee. Employees should receive intensive training courses about different aspects of the record guidance such as procedures, standards, record cycle, and office automation applications. Training concept captures the level of training of an employee. By creating frames to classify the known facts (for example, the level of employee training), a reasoner in conjunction with the ontology can assert nonconforming cases and infer the requisite training that the employee has to undergo based on role and authority (refer section 4). The OWL code in figure 5 shows the Training concept taking its input from the Record Guidance concept through the isincluded relationship. 3.2.Personnel As the second main upper-level concept in the ERM ontology, Personnel describes how employees interact with the records. For the sake of simplicity, the levels of interaction are limited to an enumerated list comprising of three possible actions - view, modify or delete records. The role of each employee is identified by Role subconcept. The Authority subconcept describes the rights an employee has to view, modify, or delete the records. The hasrole relationship links the Role subconcept to the Personnel concept. The hasauthority links the Role and the Authority subconcept. The OWL code snippet depicting these relationships is shown in figure 4. Figure 4: HasAuthority and hasrole relationships Figure 5: IsIncluded relationship 4. Implementation architecture As depicted in figure 6, the first step in using the system involves the evaluator using a web interface to enter the baseline scenario for the RM system that needs to be evaluated. Information collected in this phase include legislative acts and their required record types, standards and procedures, employees roles and authorities and the training needs that match employee s role. These baseline entries are stored in a central repository. The repository could be a relational database or an application that translates the information in some XML compliant format. When the system is used, the data (known facts) in the repository is classified as asserted individuals by the reasoner. The reasoner also performs concept inconsistency checks, automated concept subsumption, and reclassification of ontology instances (OWL individuals). The asserted individuals are then loaded

6 to the fact base or the bridge. In addition to receiving the asserted individuals, the fact base contains the inference rules that are used for the reasoning process. These rules are interpreted by the Java Expert System Shell (JESS). JESS is a rule engine that uses a Rete algorithm based pattern matcher to deductively (forward chaining) or inductively (backward chaining) reason inferred facts from available data and inference rules [4] [6]. When the inference process is completed, the facts are transferred to the OWL knowledge base and the reasoner is called to perform consistency check and possible solutions [3]. Descriptive evaluation comprising of scenario based testing and functional analysis by domain experts were used to judge the quality of the concepts, utility of the concept relationships and usability in evaluating RM Scenario based testing Consider the case of an agency that uses the National Archives of Australia Check-up assessment guide. To demonstrate the proposed architecture, we Figure 6: Implementation architecture of the knowledge base. The inference rules are developed by the knowledge engineer and implemented in SWRL. Only those matching SWRL rules are fired by JESS to reason about OWL individuals that are semantically asserted as instances of OWL classes defined in the ontology. Using the SWRL rules and the RM ontology, the combination of the JESS rule engine and the reasoner serves as a form of semantic machine to infer the evaluation state of the RM system. The resulting inferred facts are then returned to the user via the web interface. The outcome of this comparison informs whether the evaluation of the RM system matches or mismatches the ideal RM scenario Evaluation The design aspiration for the ERM ontology propositioned in this research is targeted at capturing as much essence of the RM assessment as possible. The design and implementation of the artifact is in itself an experiment from which we learn about the nature of the problem, its utility in a given environment sketch two scenarios that may be encountered during RM evaluation. Figure 7 shows that the ontology has two individuals of Legal requirements subconcept: National Archives of Australia [22] and the Records Management Self-Evaluation Guide [30]. Figure 7: Legal requirements individuals In the Check-up assessment guide [22], it is mentioned that there are three complementary laws that should be applied to records management in every agency; we will implement one of them which is Archives Act 1983 [23]. First, the domain expert enters the baseline situation of requirements of this Act to the repository. For instance, Archives Act 1983 mentions the details of the regulations and arrangements relating to certain records. These regulations and arrangement will be entered to the repository. The reasoner will eventually classify this asserted fact as an

7 instance of the Standards and Procedures subconcepts. In addition, the Act has a section about dealing with commonwealth records that includes maintaining and disposing the records. Again the requirements of this part of the Act will be entered to the repository, and an OWL individual recognizing this known fact will be classified under the Record Cycle subconcept. Moreover, Part 3 of the Act, which is the director-general and staff of the archives, manages the personnel and their authorities will be captured in the ontology through Personnel, Role, and Authority concepts. Finally, the domain expert enters the training courses that are needed to deal with Archives Act After entering the baseline scenario of the Archives Act 1983, the evaluator enters the current situation of RM system. This phase includes entering data corresponding to the employees who deals with that Act, their designated roles and authorities, and the training courses related to that Act that are already taken by the employees. These entries will be also stored in the repository. It is the responsibility of the reasoner to classify the information in the repository as asserted individuals in the ontology. The asserted individuals are then transferred to the fact base where upon the matching SWRL rules in the rule base are then fired by JESS. For example, figure 8 shows a SWRL rule that indicates the record guidance (OWL individual z) that the personnel (OWL individual x), who has a specific role (OWL individual y), needs to be informed with. that is required to deal with vital records to the repository. In addition, the evaluator will determine the training needs that are important to deal with vital records. The reviewer also needs to enter the security, privacy, and backup levels that are ideal for vital records. After entering the baseline situation, the user inputs the current situation of the RM system, which will capture the current roles and authorities of the employees who deal with vital records, the training courses that have been taken by the employees who manage the vital records and the current level of security, privacy, and backup implemented in the existing RM system. As explained in the previous example, the proposed system will compare the baseline RM system to the current RM system. First, the reasoner will classify the entered information as asserted OWL individuals. Then the asserted individuals are loaded to the fact base. All matching rules in the fact base are fired by JESS. Figure 9 shows one example of a SWRL rule that may be triggered to determine the training (OWL individual z) needs that match the roles (OWL individual y) of each employee (OWL individual x) The inferred facts will be sent back to the user which the evaluator can use to determine whether the agency matches the requirements of the Self- Evaluation Guide. In fact, the agency can improve the effectiveness of the RM system by analyzing the comparison results. Figure 8: Record guidance SWRL rule The inferred facts (outcome of JESS rule execution) are stored in the repository and sent back to the user. Finally, the user interface will compare the baseline RM system to the current RM system, and outline the result of the evaluation of the RM system. The second example is from the Records Management Self-Evaluation Guide [30]. One major aspect of using this guide necessitates specifying the requirements that should be included in the vital records (the guide mentions that agencies must specify agency staff responsibilities. ) The domain expert will enter the baseline staff responsibilities to the repository (these responsibilities will be classified under the Personnel, Role and Authority concepts.)the guide recommends that agencies must appropriately inform all staff about vital records. The evaluator will enter the recommended record guidance Figure 9: Training needs SWRL rule Functional analysis by domain experts Developing the ontologies and the concept definitions therein is a complex iterative process. The ERM ontology is the outcome of subjective tradeoffs and objective goals. Subjective decisions focused on the degree of detail in the characterization of concepts. Objective goals addressed how the application can benefit most from using the ERM ontology. Since there exists no widely accepted formal methodology for ontology evaluation [12], inputs from domain experts were regularly solicited to verify the semantic correctness of the ontology. Seeking active involvement of domain experts is a commonly applied method to evaluate ontologies [27]. Open interviews and regular reviews served as an effective vehicle to raise competency questions and to identify weaknesses.

8 The domain experts verified the validity of ontology characteristics such as consistency, clarity, coherence, generality and minimal bias [12] [27].To ensure that the ontology concepts are accurate representations of real world, scenario based testing was conducted by the domain experts. The reasoners classified the asserted facts (known data) accurately and the results inferred by the JESS expert system demonstrated that the ontology concepts were accurate representations of the real world domain of RM evaluation in an e- government setting. 5. Limitations and future research The proposed ontology design is not without limitations. First, the proposed design is based on the analysis of only three records evaluation guidlines of government organizations. We would like to investigate additional national-level evaluation guidelines to further confirm the concept generality. Currently, the proposed model may not be of practical utility in private organizations, as none of the reviewed guidelines looked at this type of organizational documentation. Furthermore, a discussion on how we integrate the security, privacy, and backup concepts have not been included here. Future work will focus on an extended manuscript to elaborate the particulars of linking the ERM ontology to those specifically developed for this purpose such as the security [5] and the privacy [18] ontologies. 6. Conclusion In this paper, we have discussed the importance of evaluating records management system and the drawbacks of the traditional evaluation method. We mentioned that evaluation of RM is essential to improve the efficiency of records management (RM) system and to meet legislative standards. We briefly discussed the implementation architecture, and evaluated the ERM ontology by two methods. Both evaluation methods exhibited encouraging results. By focusing mainly on e-government projects, we have proposed a new semantic evaluation method using the ERM ontology that can help agencies in assessing their RM system. We believe that the ERM ontology proposed in this paper offers many advantages. The ontology-based system can help in sharing contextual knowledge among users and agents, and facilitate the common understanding of the RM domain. This in turn will help in improving the consistency and precision essential in the complex process of information gathering associated with the RM assessment exercise. In addition, the proposed method can help the agency in following the legislative requirements. It helps in semantically linking data (exact standards, policies, procedures of records, etc.) to concepts (differentiating legal, training or regulatory requirements). Furthermore, it can help in raising the efficiency of records employees by determining the current and future training needs. The ontology based RM evaluation system presented in this paper may not be viewed as a fully automated approach. In comparison to approaches that are currently in place, the RM evaluation system that we present here serves as an improvement to ensure a more reliable, informed and educated alternative to analyze record management and evaluation. 6. References [1] A. Firat, S.E. Madnick and B.Grosof, Contextual Alignment of Ontologies For Semantic Interoperability, 2004, Social Science Research Network Electronic Paper Collection, available: ct_id=612472, accessed on August 27, 2010 [2] A. Tough, and M. Moss, Record Keeping in a Hybrid Environment: Managing the Creation, Use, Preservation and Disposal of Unpublished Information Objects and in Context, Oxford: Chandos Publishing, 2006 [3] A.R. Hevner, S. T. March, J. Park and S. Ram Design Science in Information Systems Research, MIS Quarterly, 28:1, 2004, pp [4] B. Peuschel, and W. Schafer, "Concepts and Implementation of a Rule-based Process Engine," in the Proceedings of the 14th International Conference on Software Engineering, Melbourne, Australia, 1992, pp [5] B. Tsoumas, D. Gritzalis, "Towards an Ontologybased Security Management", in the Proceedings of the 20th International Conference on Advanced Information Networking and Application, 2006 [6] C. L. Forgy, "Rete: A Fast Algorithm for the Many Pattern/ Many Object Pattern Match Problem", Artificial Intelligence, 19, 1982, pp [7] D. Jutla, and L. Xu, Privacy Agents and Ontology for the Semantic Web, in the Proceedings of the Americas Conference on Information Systems, 2004 [8] E. Shepherd, and G. Yeo, Managing Records: A Handbook of Principles and Practice, Facet Publishing, London, 2003

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