Special Issue "Crossing “Data, Information, Knowledge, and Wisdom” Models—Challenges, Solutions, and Recommendations"
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 15981
Interests: information security; artificial intelligence; big data; software engineering
Interests: e-commerce architectures; interoperability; social computing; decision support systems; IoT
Currently, most AI techniques and systems are built on hypotheses and assumptions of learning data distribution probabilities, information completeness, or logical consistency of knowledge systems, separately. However, it is hard to guarantee that learning data distribution will be as “big” as Big Data. Static data distribution is even more difficult in terms of modeling dynamics of data sets. Information completeness relies on not only various objective presentations of information but also the subjective purpose side inside human minds. Experience, common sense, and knowledge need coordination to keep conforming to the value of wisdom.
Data, information, knowledge, and wisdom (DIKW) have been used widely as natural language marking terms in various domains for the purposes of expressing understanding. However, there is a lack of common understanding over the meaning of DIKW concepts whether taken separately or combined. Therefore, there have been proposals and models of DIKW such as “layered hierarchy”, “architecture”, “framework”, “network”, “thinking mode”, “style”, “pattern”, “theory”, “methodology”, “model”, “graph”, etc. The more hypotheses and assumptions on the current uses of data, information, knowledge, and wisdom resources emerge, the less they can be used effectively and efficiently. These hypotheses and assumptions also mean a higher cost to collect, accumulate, and process relative resources.
Toward a more general AI landscape, which maps to real situations where we only have small or insufficient data, partial information, and diversified knowledge under a vague value strategy, with enriched processing capability, we propose to integrate the power or value of data, information, knowledge, and wisdom resources to fit more general AI application scenarios with less cost as well as improve effectiveness and efficiency through conversions among data, information, knowledge, and wisdom. In daily reality, we might expect proper imprecision, partial correctness, acceptable uncertainty, of data, information, knowledge, and wisdom, instead of overprecision, complete correctness, and full certainty, at an unexpected cost. In model merging and transformation among DIKW elements and DIKW architecture (e.g., data graph, information graph, knowledge graph, and wisdom graph), we expect the optimization of value-driven solutions toward the integration of efficiency and effectiveness catering to cross-cutting human purposes.
To tap into the benefits and uses of DIKW, the design principles and foundations of DIKW are expected to be explored to ensure an explainable and interactive AI landscape of crossing models based on DIKW premises. Aiming at investigating experimental and theoretical results, novel designs, this Special Issue will report the latest advances and developments in theories, design mechanisms, and extensions on data, information, knowledge, and wisdom interactions in all areas and phases, with empirical or theoretical solutions. The Special Issue will cover issues such as the uncertainties of multimodal content semantic traceability, relevance, migration, interaction, and the evolution of multimodal contexts or environments. This investigation should lead to new solutions toward complex content identification, modeling, processing, and service optimization in the context of massive content interaction in multidimensional, multimodal, multiscale physical and digital space covering data collection, information analysis, knowledge reasoning, and wisdom strategies in the background of the AI trend.
Topics for discussion in this Special Issue include but are not limited to:
- Application of knowledge representation techniques to semantic modeling
- Data integration, metadata management, and interoperability
- Data, information, and knowledge transformation/conversion
- Data mining and knowledge discovery
- Data models, information semantics, and query languages
- Data provenance, cleaning, and curation
- Data visualization and interactive data exploration
- Development and management of heterogeneous knowledge bases
- Domain modeling and ontology building
- Information storage and retrieval and interface technology
- Management of data, information, and knowledge hybrid systems
- Multimedia and cross-modal “Databases”
- Optimization techniques of DIKW applications
- Theories of DIKW models and performance evaluation techniques
- Crossing model interoperability inside and between applications
- Guidelines and best practices for DIKW architecture
- Privacy, trust, and security of DIKW architecture
Dr. Yucong Duan
Dr. Ejub Kajan
Dr. Zakaria Maamar
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