Automatic Keyword Extraction From Text Documents

Keyword indexing is the problem of assigning keywords to text documents. It is an important task as keywords play crucial roles in several information retrieval tasks. The problem is also challenging as the number of text documents is increasing, and such documents come in different forms (i.e., scientific papers, online news articles, and microblog posts). This chapter provides an overview of keyword indexing and elaborates on keyword extraction techniques. The authors provide the general motivations behind the supervised and the unsupervised keyword extraction and enumerate several pioneering and state-of-the-art techniques. Feature engineering, evaluation metrics, and benchmark datasets used to evaluate the performance of keyword extraction systems are also discussed.

Digital Technology Advancements in Knowledge Management in Domestic Tour Products in the Russian Federation

This chapter discusses the capabilities with problem-oriented digital twin avatars, supply chain, volumetric hybrid, and federated-consistent blockchain use to the nature of knowledge. The goal of this chapter is a theoretical study and practical implementation in the form of basic models and software modules and artificial intelligence algorithms in managing the life cycle of an internal Russian tour product. A laboratory for digitization and management, using multi-agent models of intelligent digital twins-avatars, is created. The purpose of these studies is to solve a scientific problem.

Big Data Analytics in Developing Economies

In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and energy to defense, big data has given new insights into the huge volume of data accumulated over the past few decades that is helping reshape our knowledge and understanding of these sectors. Unfortunately, the same cannot be said about the state of big data in the developing world, where investments in IT infrastructure are dangerously low, keeping huge proportions of the population offline. This chapter discussed the challenges that exist in developing countries, which affect the smooth take-off of big data and data science as well as recommendations as to how countries and companies in the developing world can overcome these challenges to harness the benefits and opportunities presented by this technology.

Knowledge Management is Why E-Government Exists

There are many countries in the world where e-government services are underdeveloped. In e-government literature, numerous reasons are attributed to the failures in the implementation of e-government services. A reason often overlooked is the fact that government agencies may not see the value of existing ICTs to the current knowledge management processes supporting the delivery of government services. In this chapter, the Mobilization-Decision theory is used to explain how the perceived knowledge management value that can be enabled using information and communication technologies resulted in the implementation of e-government services in Europe.

Knowledge Management in Human-Robot Interaction Approaches and Processes

Cognitive robots, exhibiting cognitive characteristics and synthesizing knowledge to perform tasks and interacting with humans in both industrial and social settings, have become a big part of modern societies. In this chapter, the authors review the processes and approaches to knowledge management in cognitive robot agents for effective human robot interaction. They present the current state of the art in current robotics technology and human-robot interaction. They state current requirements of cognitive robot agents in human-robot interaction and examine the role of knowledge in human-robot interaction. They finally propose a knowledge management framework for cognitive robots that consist of three main stages: knowledge acquisition and grounding, knowledge representation and knowledge integration, and instantiation into robot architectures.

A Challenge in Organizations

The chapter intends to exemplify process innovation challenges and trends. A case is studied—stock management—at an important enterprise in Portugal. It involves the analysis and improvement of this process within a firm related with gas distribution. Stock management is critical to deliver value to other processes such as sales. This issue has led to a focus on improving the inventory process. As it involves sub-value chains, this work highlights a comparison between current process and its proposed redesign. DMAIC method (define-measure-analyze-improve-control) is systematically applied, and new data emerge from tests made in the ERP (enterprise resource planning system) of the company. The improved process tends to greatly reduce execution time, as well as the number of actors and amount of information circulating outside the system. Other aspects are studied in line with new trends in ERP platforms due to cloud computing.

Determining Appropriate Social Media Sites for Knowledge Sharing

The study aims at developing a fully developed and operational cloud-based published website for predicting appropriate social media for sharing knowledge during an outbreak. The media richness theory (MRT) is used in establishing the relationship between the richness of a social media platform, which is based on four criteria—ability to provide feedback, multiple cues, language variety, and personal focus—and the level of equivocality and uncertainty in the knowledge sharing task. A survey is used to gather data on the use of four social network sites (SNSs) (Facebook, Twitter, YouTube, and Instagram) that were mostly used for sharing knowledge during the COVID-19 pandemic. Data science techniques are used to analyze the data and develop a system for selecting appropriate social media for sharing knowledge.

Software Development Knowledge Management System Using Web Portal

Software development is a knowledge-intensive practice. Software development teams rely on human resources and systematic approaches to share knowledge on system design. This collaborative knowledge sharing and preserving mechanism is known as “knowledge management” in software industries. In the software development process, coordination of system design functionalities requires knowledge-sharing infrastructure within the team members. Semantic web service computing (SWSC) provides opportunities and value-added service capabilities that global software development team requires to exchange information. This chapter describes the features of an ontology-based web portal framework, called CKIA (Collaborative Knowledge Integration Architecture), for integrating distributed knowledge in a global software development project. The CKIA framework uses a hybrid knowledge-based system consisting of Structural Case-Based Reasoning (S-CBR), Rule-Based Reasoning(RBR), and an ontology-based concept similarity assessment mechanism. A business scenario is used to present some functionalities of the framework.

An Assessment of the Quality of Data From the EPI for Knowledge Management in Healthcare in Ghana

Quality data from the Expanded Immunization Programme (EPI), which is pivotal in reducing infant mortalities globally, is critical for knowledge management on the EPI. This chapter assesses the quality of data from the EPI for the six childhood killer diseases from the EPI tally books, monthly reports, and the District Health Information Management System (DHIMS II) using the Data Quality Self-Assessment (DQS) tool of WHO. The study found high availability and completeness of data in the EPI tally books and the monthly EPI reports. The accuracy and currency of data on all antigens from EPI tally books compared to reported number issued were comparatively low. The composite quality index of the data from the EPI is thus low, an indication poor supervision of the EPI programme in the health facilities. There is therefore, the need for effective monitoring and data validation at the point of collection and entry to improve the data quality for knowledge management on the EPI programme.

A Business Merger and Acquisition Knowledge Management System Using Artificial Intelligence Techniques

Knowledge is recognized as a strategic resource, with major key drivers being the need to cut time to market and gain the business opportunities in a global market with new products and services. This chapter presents a knowledge management system known as guidance for business merger and acquisition (GBMA) process. This application uses a hybrid knowledge-based system to place bidding on the target company, formulating a strategy, and modification of the initial strategy if necessary, for the business acquisition processes. Legal knowledge for GBMA is represented in two forms, as rules and cases. Besides distinguishing the two different forms of knowledge representation, the chapter outlines the actual use of these forms in a computational architecture that is designed to generate a suitable solution, for a given new business scenario, using different reasoning mechanisms (e.g., rule-based reasoning, case-based reasoning). Business scenarios are used to show the functionalities of the presented architecture.