Moreover, the TLLH approach may have richer vocabularies because it can combine vocabularies from the user-created tags and the textual contents. On the other hand, concept extraction from the textual contents is handled by a non-negative least squares (NNLS) algorithm which is much more efficient than the NMF algorithm. Another advantage is that the NMF algorithm executes more compact and cleaner data representations. We expect this method to be successful because the hidden document structures are discovered based on tags collectively created by users who understand the semantic content of documents. Having these relationships, the concepts are populated by terms existing in the textual contents at a higher level. At the lower level, concepts and conceptdocument relationships are discovered by non-negative matrix factorization (NMF) algorithm based on the user-created tags. the user-created tags and the textual contents. This learning method executes separately the existing textual sources, i.e. Firstly, we propose a new learning method called two-level learning hierar- chy (TLLH) to extract concepts from tagged textual contents. The application areas are concept extraction, keyword extraction and tag recom- mendation. The aim of this dissertation is to investigate the applications of machine learning methods for the alternative approaches. The alternative approaches include latent semantic indexing, keyword indexing, social indexing (web 2.0) and linked data-based indexing (semantic web). Participants’ opinions were recorded in surveys and analyzed with descriptive statistics the results indicate that the TR-Model was effective in supporting the production of friendly, understandable and relevant Transparency for data subjects, in compliance with regulations like GDPR.ĭue to some drawbacks, mainly because of semantic issues such as synonymy and polysemy, people consider some approaches to improve the performance of full-text indexing. Participants evaluated transparency considering dimensions of Human-Computer Interaction and Information Quality. The information presented was created based on the TR-Model metadata, metaevents and descriptions. The model evaluation was based on user testing in several scenarios of usage of personal data in a gym application tool. TR-Model presents a set of specification based on entities, metadata, metaevents and descriptions. TR-Model elements are focused providing Personal Data Transparency in a user-friendly and high quality format. Thus, this paper aims to present the TR-Model, a Metadata Application Profile guideline that intends to propose a standardization on information to be considered minimally necessary to Personal Data Transparency as well as a set of specifications to guide developers on how to present this data. Presentation of information about personal data usage needs improvement towards Personal Data Transparency. However, details about personal data usage are often not accessible or clear to data subject, raising concerns about privacy and security. Personal data are used by organizations in business and marketing tasks. People’s usage of social networks, mobile applications, websites, sensor networks and other computer systems leads to a massive production of personal data about their behaviors and preferences.
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