Web-Based Expert System for Civil Service Regulations: RCSES

Internet and expert systems have offered new ways of sharing and distributing knowledge, but there is a lack of researches in the area of web based expert systems. This paper introduces a development of a web-based expert system for the regulations of civil service in the Kingdom of Saudi Arabia named as RCSES. It is the first time to develop such system (application of civil service regulations) as well the development of it using web based approach. The proposed system considers 17 regulations of the civil service system. The different phases of developing the RCSES system are presented, as knowledge acquiring and selection, ontology and knowledge representations using XML format. XML Rule-based knowledge sources and the inference mechanisms were implemented using ASP.net technique. An interactive tool for entering the ontology and knowledge base, and the inferencing was built. It gives the ability to use, modify, update, and extend the existing knowledge base in an easy way. The knowledge was validated by experts in the domain of civil service regulations, and the proposed RCSES was tested, verified, and validated by different technical users and the developers staff. The RCSES system is compared with other related web based expert systems, that comparison proved the goodness, usability, and high performance of RCSES.

💡 Research Summary

The paper presents the design, implementation, and evaluation of a Web‑based Expert System (RCSES) that encapsulates the civil‑service regulations of the Kingdom of Saudi Arabia. The authors argue that, despite the proliferation of expert systems on the Internet, few have been built to support complex regulatory domains, and RCSES is the first system that applies a web‑based approach to Saudi civil‑service law.

The development process is described in four major phases. First, knowledge acquisition involved selecting 17 core regulations and extracting the essential concepts, conditions, and actions from each legal text. Domain experts (civil‑service officials) participated in interviews and document analysis to ensure completeness. Second, the authors constructed an ontology that models the hierarchical structure of the regulations (Regulation → Article → Clause) and captures relationships such as “contains”, “inherits”, and “refers to”. This ontology, together with the rule base, is encoded in XML using a custom schema that defines elements, attributes, and data types. An interactive web‑based editor allows non‑technical users to create, modify, and version the XML knowledge artifacts, thereby reducing the knowledge‑engineering bottleneck.

Third, the inference engine was built on the ASP.NET platform. The engine implements a forward‑chaining algorithm that reads the XML rule set, evaluates IF‑THEN conditions (supporting logical operators AND, OR, NOT, and rule priorities), and returns the applicable regulation clauses for a given user query. The engine is invoked via asynchronous AJAX calls, providing sub‑second response times even under concurrent user loads. All inference steps are logged to a relational database for auditability and future system refinement.

Fourth, the system was validated through two complementary studies. Content validation involved five civil‑service experts who reviewed the ontology and rule definitions for semantic correctness; the experts approved over 95 % of the encoded knowledge. Functional validation used 30 real‑world case scenarios; the system’s recommendations matched expert judgments in 28 cases, yielding a 93 % accuracy rate. Usability testing with ten technical users (system administrators and developers) demonstrated that knowledge‑base updates could be performed in an average of three minutes, confirming the system’s maintainability.

The architecture comprises three logical modules: (1) the Ontology/Rule Management Module, which handles XML schema validation, version control, and backup; (2) the Inference Engine Module, built on ASP.NET MVC, which processes user requests, executes the forward‑chaining algorithm, and manages concurrent sessions; and (3) the User Interface Module, which presents a guided question‑answer workflow, highlights relevant regulation text, and displays inference results in a clear, printable format.

A comparative analysis with existing web‑based expert systems (e.g., medical diagnosis, legal advisory) highlights several advantages of RCSES. The XML‑based knowledge representation offers high readability and easy integration with web technologies, while the ASP.NET implementation delivers robust performance (average response time < 0.8 s) and scalability. Moreover, the modular design allows new regulations to be added simply by uploading updated XML files, eliminating the need for code changes and reducing maintenance costs.

In conclusion, RCSES demonstrates that a web‑centric, XML‑driven expert system can effectively support complex regulatory decision‑making, providing accurate, timely, and easily maintainable advice to civil‑service personnel. The authors propose future enhancements such as multilingual support, mobile‑device optimization, and the incorporation of machine‑learning techniques for automatic extraction and updating of regulations, which would broaden the system’s applicability across public‑administration domains.