Unstructured Knowledge Research Articles

Personal knowledge management for knowledge workers using social semantic technologies

Knowledge workers have different applications and resources in heterogeneous environments for doing their knowledge tasks and they often need to solve a problem through combining several resources. Typical personal knowledge management (PKM) systems do not provide effective ways for representing knowledge worker's unstructured knowledge or idea. In order to provide better knowledge activity for them, we implement Wiki-based social Network Thin client (WANT) that is a wiki-based semantic tagging system for collaborative and communicative knowledge creation and maintenance for a knowledge worker. And also, we suggest the social semantic cloud of tags (SCOT) ontology to represent tag data at a semantic level and combine this ontology in WANT. WANT supports a wide scope of social activities through online mash-up services and interlink resources with desktop and web environments. Our approach provides basic functionalities such as creating, organising and searching knowledge at individual level, as well as enhances social connections among knowledge workers based on their activities.

PLM, process, practice and provenance: knowledge provenance in support of business practices in Product Lifecycle Management

Structured information supports processes, but practices require unstructured, contextual and tacit knowledge. However, acquiring information system based tacit knowledge used in decision-making has been a major challenge in the last three decades. This task became even harder as technology enabled firms, especially manufacturing began to distribute their operations globally and in turn also globally distributed their decision makers. Product Lifecycle Management (PLM) involves many practices as well as processes. Singularity, cohesion and cued availability are some of the unique characteristics of these practices. Start and smart parts and Virtual Collaboration Rooms are some prevalent approaches used to address these practices. In this article, a knowledge provenance framework is used to develop a broader approach to elicit and structure some of the knowledge associated with these practices and is illustrated with an example.

A frame knowledge system for managing financial decision knowledge

Managing decision knowledge or expertise from domain experts is one of the most exciting challenges in today's knowledge management field. The nature of decision knowledge in determining a firm's financial health is context-dependent, intangible, and tacit in nature. Knowledge-based systems (KBS) have been recognized as a successful paradigm for managing financial decision knowledge attributed to possessing capabilities of reasoning and enhancing the consistency of decision-making. However, most present KBS adopt rules as the knowledge representation scheme, which cannot express the expert's knowledge construct systematically when dealing with more numerous and disordered knowledge connotations. In addition, the standalone nature of the systems hinders them from deploying onto heterogeneous platforms and cannot accommodate to the emerging Web-enabled environment. To reduce these flaws, this study proposes a frame knowledge system in which the structural and procedural decision knowledge is encapsulated so that unnecessary interference can be avoided. A protocol analysis, before encapsulation, is conducted to elicit the tacit and unstructured knowledge from a senior CPA we cooperated with. The deployment and Web enabling issue is tackled by using Jess and Java interoperable computing. With these combined, it is possible to prompt the understandability, accessibility, and reusability of KBS. The effectiveness of the proposed system is validated in supporting the expert's decision-making by conducting an empirical experimentation on 537 companies listed in the Taiwan Stock Exchange Corporation.

An approach to the development of commonsense knowledge modeling systems for disaster management

Knowledge is the fundamental resource that allows us to function intelligently. Similarly, organizations typically use different types of knowledge to enhance their performance. Commonsense knowledge that is not well formalized modeling is the key to disaster management in the process of information gathering into a formalized way. Modeling commonsense knowledge is crucial for classifying and presenting of unstructured knowledge. This paper suggests an approach to achieving this objective, by proposing a three-phase knowledge modeling approach. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. We have implemented the system using FLEX expert system shell, SPSS, XML, and VB. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy.

I-Dialogue: information extraction from informal discourse

Speech is a fundamental means of human communication. Design and construction are social activities. We argue that designers and builders generate and develop concepts through dialogue. These communicative events are typically not captured. Consequently, knowledge transfer and reuse opportunities are missed. Our objective is to capture and mine rich, contextual, social communicative events for further knowledge reuse. We present a methodology and prototype called I-Dialogue that: (1) captures the knowledge generated during informal communicative events through dialogue, sketching and gestures in the form of unstructured digital design knowledge corpus; (2) adds structure to the unstructured digital knowledge corpus; and (3) processes the corpus using an innovative notion disambiguation algorithm in support of knowledge retrieval. The paper concludes with results of evaluation experiments with I-Dialogue using a testbed of design–construction projects.

A multi‐facet taxonomy system with applications in unstructured knowledge management

PurposeUnstructured knowledge management (UKM) becomes indispensable for the support of knowledge work. However, unstructured knowledge is inconvenient and difficult for sharing, organizing and acquisition. This paper seeks to present the development and implementation of a multi‐facet taxonomy system (MTS) for effective management of unstructured knowledge.Design/methodology/approachMulti‐facet taxonomy is a multi‐dimensional taxonomy which allows the classification of knowledge assets under multiple concepts at any levels of abstraction. The MTS system is based on five components: multi‐dimensional taxonomy structure, thesaurus model, automatic classification mechanism, intelligent searching, and self‐maintenance of taxonomy, respectively. Artificial intelligence (AI) and natural language process (NLP) technologies are used in the development of the MTS.FindingsWith the successful development of the MTS, the accuracy of categorization of unstructured knowledge is significantly improved. It also allows an organization to capture the valuable tacit knowledge embedded in the unstructured knowledge assets. This helps an organization to explore business opportunities for continuous business improvement.Practical implicationsThe implementation of the MTS system not only dramatically reduces the human effort, time and cost for UKM but also allows an organization to capture valuable knowledge embedded in unstructured knowledge assets.Originality/valueAs the knowledge work and task become more complex and are dynamically changing with time and involve multiple concepts, the MTS addresses the inadequacy of conventional single dimensional taxonomy for managing unstructured knowledge. The self‐maintenance capability of the MTS ensures that the taxonomy is up‐to‐date and new knowledge is classified automatically for better knowledge sharing and acquisition.

Structural representations of unstructured knowledge

Structural representations of unstructured knowledge

Design and implementation of a case-based reasoning system for marketing plans

Unstructured intangible experiences and knowledge are usually difficult to represent and instantiate, which engenders the hardship of knowledge transfer and sharing. Past marketing plans are such valuable documents containing strategic planning knowledge and experiences. Case-Based Reasoning (CBR), which consists of retrieving, reusing, revising, and retaining cases, has been proved effective in retrieving information and knowledge from prior situations and being widely researched and applied in a great variety of problem territories. This paper targets at designing a CBR architecture and a method that facilitate the sharing and retrieving of cases of great concern to the marketing personnel. After an intensive survey of CBR methods and applications, a CBR system embedding multi-attribute decision making method, which provides both overall similarity level and similarity level of each selected attribute, is proposed to enhance the adaptation of a new marketing plan. In addition, a multi-attribute gap analysis diagram is developed to visualize the similarity along with the gap between candidate and target cases, so as to better support interaction and group decision making in the process of strategically formulating a new marketing plan. The CBR system was implemented and successfully demonstrated on case retrieval of a telecommunication company.

Recognition of Prior Learning as an integral component of competence-based assessment in South Africa

The concept of recognising and accrediting - what people already know and can do - is having a significant impact on many of the education and training programmes currently being developed. This is irrespective of whether that learning has been acquired through unstructured learning, performance development, off-the-job assessment, or skills and knowledge that meet workplace needs but have been gained through various previous learning experiences. The concept Recognition of Prior Learning (RPL) is understood by most in the area of adult education as the method of assessing relevant competences gained by adults through work and life experiences, which can then be counted towards qualifications or for promotion in the workplace by using a systematic set of procedures. A practical elaboration of RPL policy and practice in the current South African education and training sector is urgently needed. Administered carefully, and supported by explicitly anti-discriminatory policies and practices, RPL can indeed contribute to movements for greater casual mobility, thus optimising South Africa's under-used skills and expertise. In doing so, this country can embark upon a realistic and attainable strategy towards becoming a winning nation.

Characteristics of human-triggered avalanches

In order to find characteristics of human-triggered dry snow slab avalanches, 10 years of avalanche occurrence data from the Swiss Alps have been analysed. Avalanche release and snowpack patterns were studied. Avalanches triggered by recreationists contribute to about 90% of the avalanche fatalities in Switzerland. Nearly exclusively dry snow slab avalanches were triggered. The slab detached of a human-triggered slab avalanche is (median values given) 50 m wide, and 80 m long; the overall avalanche (path) length is 150 m. The fracture depth is 45 cm, and the inclination in the starting zone is 38°. The slab failure is at the boundary between storm snow and old snow in only 38% of the cases. The other failures are due to weak layers or interfaces within the old snowpack. A weak layer was found in 50% of the cases. In all other cases, the failure was between two adjacent layers, a so-called interface failure. The thin (1 cm) weak layer is usually soft, found between one or two harder layers (above and below) and consists primarily of large crystals (≥2 mm) with plane faces: surface hoar, faceted crystals and depth hoar. The analysis of the 90 profiles available did support most of the mainly unstructured knowledge used in stability evaluation based on snow profiles and supports the simple model of skier triggering.

SEAL-II - The Soft Spot between Richly Structured and Unstructured Knowledge

SEAL-II - The Soft Spot between Richly Structured and Unstructured Knowledge

Intellectual property rights: mothering innovations to markets

This paper examines innovation and knowledge generation processes and the supporting role of intellectual property rights (IPR) both for systematized investigative science and traditional community led activities. Options for protection of traditional knowledge and life materials and processes are suggested. It is suggested that the systematic and logical merging of ideas from conventions like TRIPs, convention of biodiversity (CBD), and union pour la protection des obtentitous vegetales (UPOV) can lead to the creation of harmonized provisions that could satisfy basic and minimum standards of IPR and societal ethics. The need for the unambiguous definition of discovery and invention in the granting of patents for biotechnological investigations is explored, as well as the issues of the establishment of prior art from unstructured traditional knowledge, identifying legal owners of traditional knowledge and evaluating prior art in this domain. The imperative task of creating a structured knowledge database of traditional practices and linking them through global networks is highlighted. The author also points to the fact that rigorous examination and search of patents in biotechnology is demanding ever greater levels of technical expertise and ultrahigh speed computing. Four case studies are presented to illustrate issues related to: • the erroneous granting of a patent and the role of documented community prior art in its revocation, • equitable sharing of benefits with indigenous tribes, • sharing of benefits with the community and • integrating indigenous knowledge, modern science and reciprocity into novel drug discovery.

Managing Design Knowledge: Active Document System

Sharing design knowledge within a company is critical to retain market competitiveness, especially when the company develops a family of products. The goal of this study is to develop a Web-based design knowledge management system, named Active Document System, that helps designers retrieve critical design information promptly. In order to manage unstructured knowledge such as design know-how systematically, Active Document System classifies design knowledge in accordance with product functions as well as product structures. This paper explains Active Document System and a method to deploy the product function. The concept is implemented and applied to the management of design knowledge for the refrigerator design process.

A strategy of knowledge representation for uncertain problems: modeling domain expert knowledge with patterns

Patterns as sets of somehow coherent information are feasible metaphors and tools for representation of weakly structured or unstructured knowledge in an open, infinite world. This view follows the evolutionary theory of mind representation. A practical computational formulation is given with pattern relationships based on the metapattern concept. The pattern space is mostly nonmetric due to the limited dimensional view of infinite dimensional objects. The faults of the metric suggest directions in exploring further knowledge and eliciting tacit knowledge of experts. Man-machine interaction has a special role in the process, explored by cognitive psychology methods and special graphic tools for input-output representation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

AXIS: A framework for interactively combining structured algorithms and knowledge based systems

This paper presents a method for combining Knowedge Based Expert Systems with algorithms that seek a “best” solution rather than a satisfying one. Existing combinations have featured sequential systems, utilizing first one of the two techniques to derive an intermediate solution, then the remaining technique to improve that solution. These systems stand alone, not following any consistent procedure. The proposed framework uses the two approaches interactively. Each solution method is used repeatedly, to generate intermediate solutions to the problem at hand. These intermediate solutions are passed between the methods until an acceptable solution is found. This framework can be adapted to a variety of domains and algorithms. The knowledge based heuristic solutions generated by this interactive method have the potential to be more accurate than pure heuristic solutions, arrived at more quickly than the pure algorithmic solutions, and more valid than either individual approach since structured and unstructured knowledge is used.

Knowledge Acquisition Using Structured Interviewing: An Empirical Investigation

The knowledge acquisition problem endures as a bottleneck in the construction of expert system knowledge bases. Despite the recent proliferation of techniques and the availability of more sophisticated methods for this task, the interview technique continues to be widely used, especially in business domains. This paper reports the results of an experiment conducted to compare the unstructured knowledge acquisition interview with a specific type of structured knowledge acqui- sition interview. Structure for the interview was provided by a domain model of the business decision-making activity that attempted to capture the subjective and quali- tative aspects of decision making. Senior managers from industry served as the subjects in the experiment The interview technique was evaluated along efficiency and effectiveness dimensions. Results indicate improved performance with the struc- tured interviewing method.

Awareness, the Origin of Thought, and the Role of Conscious Self-Deception in Resistance and Repression

Non-conscious processes involved in the formulation, organization, and expression of thought and consciousness are examined. It is argued that non-organized impulses and imageless non-linguistic knowledge exist prior to and result in the organization recognized as consciousness and thought. Hence consciousness is viewed as the developmental endpoint of a unitary process originating as non-activated sensations and knowledge, i.e., awareness. Because one can be aware of and have knowledge of “things” prior to being conscious of them, it is possible to know, yet not know, or rather not think about certain objectionable feelings or tacit ideas. These imageless, non-linguistic forms of knowledge are not unconscious; rather they are non-labeled and non-descriptive. Within this framework the Freudian conception of unconscious processes is examined and shown to support a model of self-deception, defined as a conscious refusal to attend to unstructured tacit knowledge of which one is simultaneously aware.