Semantic Knowledge Management and Blockchain-based Privacy for Internet of Things Applications, Journal of Telecommunications and Information Technology, 2022, nr 3

Design of distributed complex systems raises several important challenges, such as: confidentiality, data authentication and integrity, semantic contextual knowledge sharing, as well as common and intelligible understanding of the environment. Among the many challenges are semantic heterogeneity that occurs during dynamic knowledge extraction and authorization decisions which need to be taken when a resource is Accessem in an open, dynamic environment. Blockchain offers the tools to protect sensitive personal data and solve reliability issues by providing a secure communication architecture. However, setting-up blockchain-based applications comes with many challenges, including processing and fusing heterogeneous information from various sources. The ontology model explored in this paper relies on a unified knowledge representation method and thus is the backbone of a distributed system aiming to tackle semantic heterogeneity and to model decentralized management of Access control authorizations.We intertwine the blockchain technology with an ontological model to enhance knowledge management processes for distributed systems. Therefore, rather than reling on the mediation of a third party, the approach enhances autonomous decision-making. The proposed approach collects data generated by sensors into higher-level abstraction using n-ary hierarchical structures to describe entities and actions. Moreover, the proposed semantic architecture relies on hyperledger fabric to ensure the checking and authentication of knowledge integrity while preserving privacy

Quantitative Characteristics of Human-Written Short Stories as a Metric for Automated Storytelling

Evaluating the extent to which computer-produced stories are structured like human-invented narratives can be an important component of the quality of a story plot. In this paper, we report on an empirical experiment in which human subjects have invented short plots in a constrained scenario. The stories were annotated according to features commonly found in existing automatic story generators. The annotation was designed to measure the proportion and relations of story components that should be used in automatic computational systems for matching human behaviour. Results suggest that there are relatively common patterns that can be used as input data for identifying similarity to human-invented stories in automatic storytelling systems. The found patterns are in line with narratological models, and the results provide numerical quantification and layout of story components. The proposed method of story analysis is tested over two additional sources, the ROCStories corpus and stories generated by automated storytellers, to illustrate the valuable insights that may be derived from them

Importance of Measuring Sentential Semantic Knowledge Base of a "Free Text" Medical Corpus

At present, the healthcare industry uses codified data mainly for billing purpose. Codified data could be used to improve patient care through decision support and analytical systems. However to reduce medical errors, these systems need access to a wide range of medical data. Unfortunately, a great deal of data is only available in a narrative or free text form, requiring natural language processing (NLP) techniques for their codification. Structuring narrative data and analyzing their underlying meaning from a medical domain requires extensive knowledge acquired through studying the domain empirically. Existing NLP system like MedLEE has a limited ability to analyze free text medical observations and codify data against Unified Medical Language System (UMLS) codes. MedLEE was successful in extracting meaning from relatively simple sentences from radiological reports, but could not analyze more complicated sentences which appear frequently in medical reports. An important problem in medical NLP is, understanding how many codes or symbols are necessary to codify a medical domain completely. Another problem is determining whether existing medical lexicons like SNOMED-CT and ICD-9, etc. are suitable for representing the knowledge in medical reports unambiguously. This thesis investigates the problems behind current NLP systems and lexicons, and attempts to estimate the number of required symbols or codes to represent a large corpus of radiology reports. The knowledge will provide a greater understanding of how many symbols may be needed for the complete representation of concepts in other medical domains