Wai Ming Wang

A multi-perspective knowledge-based system for customer service management

Abstract The e-business arena is a dynamic, complex and demanding environment. It is essential to make optimal reuse of knowledge of customer services across various functional units of the enterprise. On the other hand, it is also important to ensure that the customer service staff can access and be trained up with dynamically updated knowledge that meets the changing business environment of an enterprise in customer services. However, conventional way of customer service management (CSM) is inadequate to achieve the multi-perspective of an enterprise for achieving knowledge acquisition, knowledge diffusion, business automation and business performance measurement so as to drive the continuous improvement of the customer service quality. In this paper, a multi-perspective knowledge-based system (MPKBS) is proposed for CSM. The MPKBS incorporates various artificial intelligence technologies such as case-based reasoning (CBR) and adaptive time-series model which are used for decision analysis, performance measurement and monitoring. A prototype customer service portal has been built based on the MPKBS and implemented successfully in a consultancy business.

Mining knowledge from natural language texts using fuzzy associated concept mapping

Natural Language Processing (NLP) techniques have been successfully used to automatically extract information from unstructured text through a detailed analysis of their content, often to satisfy particular information needs. In this paper, an automatic concept map construction technique, Fuzzy Association Concept Mapping (FACM), is proposed for the conversion of abstracted short texts into concept maps. The approach consists of a linguistic module and a recommendation module. The linguistic module is a text mining method that does not require the use to have any prior knowledge about using NLP techniques. It incorporates rule-based reasoning (RBR) and case based reasoning (CBR) for anaphoric resolution. It aims at extracting the propositions in text so as to construct a concept map automatically. The recommendation module is arrived at by adopting fuzzy set theories. It is an interactive process which provides suggestions of propositions for further human refinement of the automatically generated concept maps. The suggested propositions are relationships among the concepts which are not explicitly found in the paragraphs. This technique helps to stimulate individual reflection and generate new knowledge. Evaluation was carried out by using the Science Citation Index (SCI) abstract database and CNET News as test data, which are well known databases and the quality of the text is assured. Experimental results show that the automatically generated concept maps conform to the outputs generated manually by domain experts, since the degree of difference between them is proportionally small. The method provides users with the ability to convert scientific and short texts into a structured format which can be easily processed by computer. Moreover, it provides knowledge workers with extra time to re-think their written text and to view their knowledge from another angle.

Self-associated concept mapping for representation, elicitation and inference of knowledge

Concept maps have been widely put to educational uses. They possess a number of appealing features which make them a promising tool for teaching, learning, evaluation, and curriculum planning. This paper presents self-associated concept mapping (SACM) which extends the use of concept mapping by proposing the idea of self-construction and automatic problem solving to traditional concept maps. The SACM can be automatically constructed and dynamic updated. A Constrained Fuzzy Spreading Activation (CFSA) model is proposed to SACM for supporting rapid and automatic decisions. With the successful development of the SACM, the capability of Knowledge-based systems (KBS) can be enhanced. The concept and operational feasibility of the SACM is realized through a case study in a consultancy business. The theoretical results are found to agree well with the experimental results.

RACER: Rule-Associated CasE-based Reasoning for supporting General Practitioners in prescription making

Prescription is an important element in the medical practice. An appropriate drug therapy is complex in which the decision of prescribing is influenced by many factors. Any discrepancy in the prescription making process can lead to serious consequences. In particular, the General Practitioners (GPs), who need to diagnose and treat a wide range of health conditions and diseases, must be knowledgeable enough in deciding what type of medicines should be given to the patients. With the widespread computerization of medical records, GPs now can make use of accumulated historic clinical data in retrieving similar decisions in therapeutic treatment for treating the new situation. However, the applications of decision support tools are rarely found in the prescription domain due to the complex nature of the domain and limitations of the existing tools. It was argued that existing tools can only solve a small amount of the cases on the real world dataset. This paper proposes a new revised Case-based Reasoning (CBR) mechanism, named Rule-Associated CasE-based Reasoning (RACER), which integrates CBR and association rules mining for supporting GPs prescription. It aims at leveraging the two most common techniques in the field and dealing with the complex multiple values solution. Eight hundred real cases from a medical organization are collected and used for evaluating the performance of RACER. The proposed method was also compared with CBR and association rules mining for testing. The results demonstrate that the combination leads to increased in both recall and precision in various settings of parameters. The performance of RACER remains stable by using different sets of parameters, which shows that the most important element of the mechanism is self-determined.

A Semantic-based Intellectual Property Management System (SIPMS) for supporting patent analysis

Patent databases provide valuable information for technology management. However, the rapid growth of patent documents, the lengthy text and the rich of content in technical terminology, and the complicated relationships among the patents, make it taking a lot of human effort for conducting analyses. As a result, an automated system for assisting the inventors in patent analysis as well as providing support in technological innovation is in great demand. In this paper, a Semantic-based Intellectual Property Management System (SIPMS) has been developed for supporting the management of intellectual properties (IP). It incorporates semantic analysis and text mining techniques for processing and analyzing the patent documents. The method differentiates itself from the traditional technological management tools in its knowledge base. Instead of eliciting knowledge from domain experts, the proposed method adopts global patent databases as sources of knowledge. The system enables users to search for existing patent documents or relevant IP documents which are related to a potential new invention and to support invention by providing the relationships and patterns among a group of IP documents. The method has been evaluated by benchmarking with the performance against traditional text mining technique and has successfully been implemented at a selected reference site.

A knowledge extraction and representation system for narrative analysis in the construction industry

Abstract Many researchers advocate that the real-world narratives shared by experts or knowledge workers are helpful in teaching and educating novices to learn new knowledge and skills. Narrative analysis is a useful method for experts to understand narratives. However, it does not produce any clear or explicit layouts. This is not easy for a new learner without prior knowledge to glean the right messages from narratives within a short time. In this paper, a narrative knowledge extraction and representation system (NKERS) is presented to extract and represent narrative knowledge in an effective manner. The NKERS is composed of a narrative knowledge element extraction algorithm, a narrative knowledge representation method and a narrative knowledge database. A prototype system has been built and trial implemented in the construction industry. The results show that the domain experts agree that the narrative maps generated by the NKERS can effectively represent narrative elements and flows. Three-quarters of respondents expressed that they will use the produced narrative maps in their training courses to facilitate students’ learning.

A multi-faceted and automatic knowledge elicitation system (MAKES) for managing unstructured information

Research highlights? A multi-faceted and automatic knowledge elicitation system (MAKES) is presented to address the inadequacy of traditional approach for managing unstructured information. ? MAKES provides an important means for supporting the automation of the auditing of unstructured information through the integration of the processes of collecting data, classifying unstructured information, modelling knowledge flow and social network analysis. ? New knowledge can be uncovered, analyzed and updated continuously. Management of unstructured information, such as emails, is vital for supporting knowledge work in professional services. However, the conventional way for managing unstructured information is inadequate as the knowledge work and associated tasks are becoming more complex, are dynamically changing with time and involve multiple concepts. This paper attempts to address the inadequacy, deficiency and limitations of the methods presently used to elicit knowledge from masses of unstructured information. These methods rely heavily on manpower, are time consuming and costly. With the development of a multi-faceted and automatic knowledge elicitation system (MAKES) manpower, time and cost can be dramatically reduced. The MAKES integrates the processes of collecting data, classifying unstructured information, modelling knowledge flow and social network analysis, and makes all of these actions into a connected process to audit unstructured information automatically. This audit is based on specific search criteria, search keywords, and the user behaviours of the knowledge workers. The unstructured information is automatically organized, classified and presented in a multi-facet taxonomy map. New concepts and knowledge are uncovered, analyzed and updated continuously from the incoming unstructured information, using a purpose-built knowledge elicitation algorithm named self-associated concept mapping (SACM). The capability and advantages of the MAKES are demonstrated through a successful trial implementation and a verification test conducted in an electronics trading company. Encouraging results have been achieved and a number of potential advantages have been realized. The area of application in this first deployment is based on an email-intensive organization and the proposed study will contribute to the advancement of methods and tools for managing other kinds of unstructured information.

A computational narrative construction method with applications in organizational learning of social service organizations

Acquisition of knowledge must be interwoven with the process of applying it. However, traditional training methods which provide abstract knowledge have shown ineffective for gaining experience of the work. In order to solve this problem, more and more researchers have included narrative in simulation, which is known as narrative simulation. By providing the narratives, participants recognize the choices, decisions, and experience that lead to the consequences of those decisions. It has been proven that narrative simulation is very useful in facilitating in-depth learning and reflective learning. However, conventional methods of data collection and narrative construction for narrative simulation are labor intensive and time consuming. They make use of previous narratives manually and directly. They are inadequate to cope with the fast moving world where knowledge is changing rapidly. In order to provide a way for facilitating the construction of narrative simulation, a novel computational narrative construction method is proposed. By incorporating technologies of knowledge-based system (KBS), computational linguistics, and artificial intelligence (AI), the proposed method provides an efficient and effective way for collecting narratives and automating the construction of narratives. The method converts the unstructured narratives into a structural representation for abstraction and facilitating computing processing. Moreover, it constructs the narratives that combine multiple narratives into a single narrative by applying a forecasting algorithm. The proposed method was successfully implemented in early intervention in mental health care of a social service company in Hong Kong since the case records in that process have structural similarities to narrative. The accuracies of data conversion and predictive function were measured based on recall and precision and encouraging results were obtained. High recall and precision are achieved in the data conversion function, and high recall for the predictive function when new concepts are excluded. The results show that it is possible for converting multiple narratives into a single narrative automatically. Based on the approach, it helps to stimulate knowledge workers to explore new problem solving methods so as to increase the quality of their solutions.

Managing knowledge in the construction industry through computational generation of semi-fiction narratives

Purpose Narratives are useful to educate novices to learn from the past in a safe environment. For some high-risk industries, narratives for lessons learnt are costly and limited, as they are constructed from the occurrence of accidents. This paper aims to propose a new approach to facilitate narrative generation from existing narrative sources to support training and learning. Design/methodology/approach A computational narrative semi-fiction generation (CNSG) approach is proposed, and a case study was conducted in a statutory body in the construction industry in Hong Kong. Apart from measuring the learning outcomes gained by participants through the new narratives, domain experts were invited to evaluate the performance of the CNSG approach. Findings The performance of the CNSG approach is found to be effective in facilitating new narrative generation from existing narrative sources and to generate synthetic semi-fiction narratives to support and educate individuals to learn from past lessons. The new narratives generated by the CNSG approach help students learn and remember important things and learning points from the narratives. Domain experts agree that the validated narratives are useful for training and learning purposes. Originality/value This study presents a new narrative generation process for a high-risk industry, e.g. the construction industry. The CNSG approach incorporates the technologies of natural language processing and artificial intelligence to computationally identify narrative gaps in existing narrative sources and proposes narrative fragments to generate new semi-fiction narratives. Encouraging results were gained through the case study.

Computational narrative mapping for the acquisition and representation of lessons learned knowledge

Abstract Lessons learned knowledge is traditionally gained from trial and error or narratives describing past experiences. Learning from narratives is the preferred option to transfer lessons learned knowledge. However, learners with insufficient prior knowledge often experience difficulties in grasping the right information from narratives. This paper introduces an approach that uses narrative maps to represent lessons learned knowledge to help learners understand narratives. Since narrative mapping is a time-consuming, labor-intensive and knowledge-intensive process, the proposed approach is supported by a computational narrative mapping (CNM) method to automate the process. CNM incorporates advanced technologies, such as computational linguistics and artificial intelligence (AI), to identify and extract critical narrative elements from an unstructured, text-based narrative and organize them into a structured narrative map representation. This research uses a case study conducted in the construction industry to evaluate CNM performance in comparison with existing paragraph and concept mapping approaches. Among the results, over 90% of respondents asserted that CNM enhanced their understanding of the lessons learned. CNM’s performance in identifying and extracting narrative elements was evaluated through an experiment using real-life narratives from a reminiscence study. The experiment recorded a precision and recall rate of over 75%.