Towards MKM in the Large: Modular Representation and Scalable Software Architecture

MKM has been defined as the quest for technologies to manage mathematical knowledge. MKM "in the small" is well-studied, so the real problem is to scale up to large, highly interconnected corpora: "MKM in the large". We contend that advances in two areas are needed to reach this goal. We need representation languages that support incremental processing of all primitive MKM operations, and we need software architectures and implementations that implement these operations scalably on large knowledge bases. We present instances of both in this paper: the MMT framework for modular theory-graphs that integrates meta-logical foundations, which forms the base of the next OMDoc version; and TNTBase, a versioned storage system for XML-based document formats. TNTBase becomes an MMT database by instantiating it with special MKM operations for MMT.Comment: To appear in The 9th International Conference on Mathematical Knowledge Management: MKM 201

A Knowledge Management Perspective of Generative Artificial Intelligence

In this editorial, revisiting Alavi and Leidner (2001) as a conceptual lens, we consider the organizational implications of generative artificial intelligence (GenAI) from a knowledge management (KM) perspective. We examine how GenAI impacts the processes of knowledge creation, storage, transfer, and application, highlighting both the opportunities and challenges this technology presents. In knowledge creation, GenAI enhances information processing and cognitive functions, fostering individual and organizational learning. However, it also introduces risks like AI bias and reduced human socialization, potentially marginalizing junior knowledge workers. For knowledge storage and retrieval, GenAI’s ability to quickly access vast knowledge bases significantly changes employee interactions with KM systems. This raises questions about balancing human-derived tacit knowledge with AI-generated explicit knowledge. The paper also explores GenAI’s role in knowledge transfer, particularly in training and cultivating a learning culture. Challenges include an overreliance on AI and risks in disseminating sensitive information. In terms of knowledge application, GenAI is seen as a tool to boost productivity and innovation, but issues like knowledge misapplication, intellectual property, and ethical considerations are critical. Conclusively, the paper argues for a balanced approach to integrating GenAI into KM processes. It advocates for harmonizing GenAI’s capabilities with human insights to effectively manage knowledge in contemporary organizations, ensuring both technological advances and ethical responsibility

Formulating Strategic Directions for Indigenous Knowledge Management Systems

In modern organisational structures knowledge management practices consist of knowledge generation, capture, sharing and application. The organisations emphasize on codification and documentation of implicit knowledge and transform it to explicit form. Indigenous communities however have much less codified knowledge relying mainly on oral and tacit form. The communities have their own processes of storage, leveraging, sharing and applying knowledge which is different from knowledge management processes of corporations and research organizations due to the oral and tacit structures of these processes. In this paper we present a model for formulating strategic directions for an indigenous knowledge management system. We have designed a knowledge management assessment tool for Indigenous Knowledge Management Systems (IKMS) which has been tested in remote community in Bario, Sarawak. On the bases of our assessment of IKMS, community capacity and resources, we have developed a strategic map for IKMS in Bario. This work serves as an extension to the previous literature on designing the Balanced Scorecard for IKMS

Organization, Management and Engineering of Knowledge: Rivals or Complements?

[Abstract] Knowledge Organization is a discipline that has its origin in the library field and was extended by new documentation and information tasks. Thought it claims to encompass all kinds and aspects of knowledge storage and retrieval it is bound more or less to the idea to express the structure of knowledge which is behind a scientific collection of objects and their descriptions. Its aim is to facilitate the exchange between scientists and their knowledge. Knowledge Management instead deals with the elicitation, processing and diffusion of economically important information. Knowledge gets here the main notion of competitive intelligence for a limited target and community. Knowledge Engineering is the technique of making cognitive units and links machine readable and processable. It achieves its advantage over human interaction and understanding with the growth of the data bases and the speed of numerical based decisions. Though rather surprising information mining might be possible by Knowledge Engineering a qualitative or ethical inference remains nearly unsolved. If one contrasts Knowledge Organization, Knowledge Management and Knowledge Engineering to each other these knowledge disciplines get a clearer shape and their special claims, contributions and limitations have to be taken into account. On the other hand it becomes obvious that facing the typical problems and solutions of all knowledge disciplines will result in better outcome in each. Thus practical solutions will always have to take into account these three aspects of knowledge at least

Embedding the Library into Scientific and Scholarly Communication through Knowledge Management

Knowledge management is a new role for academic research libraries that has the potential to integrate the library into scholarly and scientific communication in a significant way. Work in knowledge management is advancing in both the sciences and humanities. The Genome Data Base at the Johns Hopkins University is currently the most advanced knowledge management prototype. As part of its new Center for Knowledge Management, the University of California, San Francisco is undertaking several initiatives to create a campuswide knowledge management environment.published or submitted for publicatio

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

KOMBASE - a knowledge representation system with frames for an object-oriented knowledge base

Knowledge representation is an important area of research which is currently being done in the field of Artificial Intelligence (AI). In order to manipulate the wealth of information available in a typical AI application, mechanisms must be provided to represent and to reason with knowledge at a high level of abstraction. Knowledge representation with frames is a structured and object-oriented approach to this problem. KOMBASE is a prototype to a frame-based system containing organizational information of companies and other corporate bodies. This paper describes the approach adopted in the development of KOMBASE and discusses its implementation, particularly from a knowledge representational perspective