Search

Contract creation and management is an essential part of the working process for entities (i.e. legal persons, businesses, and institutions) that consume these contracts. Paper-based methods that are used to create and manage contracts have proven to be time-consuming and prone to various errors. To move from these outdated methods, Contract Management Tools (CMTs) offer (semi)-automated approaches to contract creation and management. The Automatic Contracting Tool (ACT) is a Semantic Web-based CMT that enables the creation and management of contracts centered around the data-sharing domain. Data-sharing and processing is a complex domain. Often, data comes from different systems that use different formats and standards. Therefore, there is a need to ensure that distinct data sources and representations can work together, enabling collaboration across platforms. Semantic Web-based technologies (i.e., knowledge graphs and ontologies) allow for interoperability, thus providing a foundation for expressing and representing knowledge in a standardized way. In this thesis, I present a running example of the smashHit ontology and contract data from the knowledge graph that is used to represent the concepts and relationships defined in thisontology. Contract Compliance Verification (CCV) offers a method to ensure whether a contract adheres to GDPR and consequently reports instances where breaches have been detected. CCV is traditionally practiced using the Python coding language in an advanced way to perform compliance checks, such as in the case of Tauqeer et al. . In this thesis, I present a new approach to CCV, which leverages Shapes Constraint Language (SHACL), a validation standard developed by W3C. In order to ensure that contracts are GDPR-compliant and report whether contracts have been breached or violated, a SHACL-based CCV process has been introduced. Moreover, I try to give an answer to the question of whether a repair strategy can be leveraged for ACT in order, Masterarbeit Universität Innsbruck 2024

The General Data Protection Regulation (GDPR) has imposed strict requirements for data sharing, one of which is informed consent. A common way to request consent online is via cookies. However, commonly, users accept online cookies being unaware of the meaning of the given consent and the following implications. Once consent is given, the cookie “disappears”, and one forgets that consent was given in the first place. Retrieving cookies and consent logs becomes challenging, as most information is stored in the specific Internet browser’s logs. To make users aware of the data sharing implied by cookie consent and to support transparency and traceability within systems, we present a knowledge graph (KG) based tool for personalised cookie consent information visualisation. The KG is based on the OntoCookie ontology, which models cookies in a machine-readable format and supports data interpretability across domains. Evaluation results confirm that the users’ comprehension of the data shared through cookies is vague and insufficient. Furthermore, our work has resulted in an increase of 47.5% in the users’ willingness to be cautious when viewing cookie banners before giving consent. These and other evaluation results confirm that our cookie data visualisation approach and tool help to increase users’ awareness of cookies and data sharing.

Search on the web, specifically fetching of the relevant content, has been paid attention to since the advent of the web and particularly in recent years due to the tremendous growth in the volume of data and web pages. This paper categorizes the search services from the early days of the web to the present into keyword search engines, semantic search engines, question answering systems, dialogue systems and chatbots. As the first generation of search engines, keyword search engines have adopted keyword-based techniques to find the web pages containing the query keywords and ranking search results. In contrast, semantic search engines try to find meaningful and accurate results on the meaning and relations of things. Question-answering systems aim to find precise answers to natural language questions rather than returning a ranked list of relevant sources. As a subset of question answering systems, dialogue systems target to interact with human users through a dialog expressed in natural language. As a subset of dialogue systems, chatbots try to simulate human-like conversations. The paper provides an overview of the typical aspects of the studied search services, including process models, data preparation and presentation, common methodologies and categories.