Related papers: Sources of Change for Modern Knowledge Organizatio…
This paper describes a new technique, called "knowledge patterns", for helping construct axiom-rich, formal ontologies, based on identifying and explicitly representing recurring patterns of knowledge (theory schemata) in the ontology, and…
Finding reliable data to inform decisions about technology for global development remains a challenge. Easily accessible "Knowledge platforms" are a way to curate and standardize information about technology for development. Three…
Modern knowledge base systems frequently need to combine a collection of databases in different formats: e.g., relational databases, XML databases, rule bases, ontologies, etc. In the deductive database system DDBASE, we can manage these…
Ontologies have been known for their semantic representation of knowledge. ontologies cannot automatically evolve to reflect updates that occur in respective domains. To address this limitation, researchers have called for automatic…
Organized knowledge production can then be considered as the codification of communication. Communications leave traces that can be studied as indicators. Institutions can be considered as retention mechanisms functional for the…
Workflows are prevalent in today's computing infrastructures. The workflow model support various different domains, from machine learning to finance and from astronomy to chemistry. Different Quality-of-Service (QoS) requirements and other…
This article firstly reviews and summarizes the rapid development of information technology, characterized by the close combination of computer and network communication, which leads to a series of investigations, including the analyses of…
Although Information Systems and Information Technology (IS & IT) has become a major driving force for many of the current day organizations, the NGOs have not been able to utilize the benefits up to a satisfactory level. Most organizations…
The code base of software projects evolves essentially through inserting and removing information to and from the source code. We can measure this evolution via the elements of information - tokens, words, nodes - of the respective…
The development of a company often entails the emergence of autonomous data sources with different structural and technological organization. This can lead to the inability of data analysis at a high level and a violation of the integrity…
Machine Learning systems rely on data for training, input and ongoing feedback and validation. Data in the field can come from varied sources, often anonymous or unknown to the ultimate users of the data. Whenever data is sourced and used,…
Creation of the information systems and tools for scientific research and development support has always been one of the central directions of the development of computer science. The main features of the modern evolution of scientific…
Although knowledge is one of the most valuable resource of enterprises and an important production and competition factor, this intellectual potential is often used (or maintained) only inadequate by the enterprises. Therefore, in a…
Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality…
Open Source Software (OSS) has been recognized by the software development community as an effective way to deliver software. Unlike traditional software development, OSS development is driven by collaboration among developers spread…
In the era of joint ventures (JV) and mergers & acquisitions (M&A), dynamic and large business processes can emerge to achieve broader business goals and are often formed from business processes of distinct organizations. Software systems…
Climate change impacts a broad spectrum of human resources and activities, necessitating the use of climate models to project long-term effects and inform mitigation and adaptation strategies. These models generate multiple datasets by…
Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata…
The data warehouse (DW) technology was developed to integrate heterogeneous information sources for analysis purposes. Information sources are more and more autonomous and they often change their content due to perpetual transactions (data…
The sharing of ontologies between diverse communities of discourse allows them to compare their own information structures with that of other communities that share a common terminology and semantics - ontology sharing facilitates…