Ontologies for context-aware applications

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Implementing OBDA for an end-user query answering service on an educational ontology

In the age where productivity of society is no longer defined by the amount of information generated, but from the quality and assertiveness that a set of data may potentially hold, the right questions to do depends on the semantic awareness capability that an information system could evolve into. To address this challenge, in the last decade, exhaustive research has been done in the Ontology Based Data Access (OBDA) paradigm. A conspectus of the most promising technologies with data integration capabilities and the foundations where they rely are documented in this memory as a point of reference for choosing tools that supports the incorporation of a conceptual model under a OBDA method. The present study provides a practical approach for implementing an ontology based data access service, to educational context users of a Learning Analytics initiative, by means of allowing them to formulate intuitive enquiries with a familiar domain terminology on top of a Learning Management System. The ontology used was completely transformed to semantic linked data standards and some data mappings for testing were included. Semantic Linked Data technologies exposed in this document may exert modernization to environments in which object oriented and relational paradigms may propagate heterogeneous and contradictory requirements. Finally, to validate the implementation, a set of queries were constructed emulating the most relevant dynamics of the model regarding the dataset nature

Demonstration of Semantic Web-based Medical Ontologies and Clinical Decision Support Systems

Master's thesis in Information- and communication technology IKT590 - University of Agder 2016Konfidensiell til / confidential until 01.01.202

A software reference architecture for journalistic knowledge platforms

Newsrooms and journalists today rely on many different artificial-intelligence, big-data and knowledge-based systems to support efficient and high-quality journalism. However, making the different systems work together remains a challenge, calling for new unified journalistic knowledge platforms. A software reference architecture for journalistic knowledge platforms could help news organisations by capturing tried-and-tested best practices and providing a generic blueprint for how their IT infrastructure should evolve. To the best of our knowledge, no suitable architecture has been proposed in the literature. Therefore, this article proposes a software reference architecture for integrating artificial intelligence and knowledge bases to support journalists and newsrooms. The design of the proposed architecture is grounded on the research literature and on our experiences with developing a series of prototypes in collaboration with industry. Our aim is to make it easier for news organisations to evolve their existing independent systems for news production towards integrated knowledge platforms and to direct further research. Because journalists and newsrooms are early adopters of integrated knowledge platforms, our proposal can hopefully also inform architectures in other domains with similar needs.publishedVersio

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Comparison of Concept Learning Algorithms With Emphasis on Ontology Engineering for the Semantic Web

In the context of the Semantic Web, ontologies based on Description Logics are gaining more and more importance for knowledge representation on a large scale. While the need arises for high quality ontologies with large background knowledge to enable powerful machine reasoning, the acquisition of such knowledge is only advancing slowly, because of the lack of appropriate tools. Concept learning algorithms have made a great leap forward and can help to speed up knowledge acquisition in the form of induced concept descriptions. This work investigated whether concept learning algorithms have reached a level on which they can produce results that can be used in an ontology engineering process. Two learning algorithms (YinYang and DL-Learner) are investigated in detail and tested with benchmarks. A method that enables concept learning on large knowledge bases on a SPARQL endpoint is presented and the quality of learned concepts is evaluated in a real use case. A proposal is made to increase the complexity of learned concept descriptions by circumventing the Open World Assumption of Description Logics

Technologies to enhance self-directed learning from hypertext

With the growing popularity of the World Wide Web, materials presented to learners in the form of hypertext have become a major instructional resource. Despite the potential of hypertext to facilitate access to learning materials, self-directed learning from hypertext is often associated with many concerns. Self-directed learners, due to their different viewpoints, may follow different navigation paths, and thus they will have different interactions with knowledge. Therefore, learners can end up being disoriented or cognitively-overloaded due to the potential gap between what they need and what actually exists on the Web. In addition, while a lot of research has gone into supporting the task of finding web resources, less attention has been paid to the task of supporting the interpretation of Web pages. The inability to interpret the content of pages leads learners to interrupt their current browsing activities to seek help from other human resources or explanatory learning materials. Such activity can weaken learner engagement and lower their motivation to learn. This thesis aims to promote self-directed learning from hypertext resources by proposing solutions to the above problems. It first presents Knowledge Puzzle, a tool that proposes a constructivist approach to learn from the Web. Its main contribution to Web-based learning is that self-directed learners will be able to adapt the path of instruction and the structure of hypertext to their way of thinking, regardless of how the Web content is delivered. This can effectively reduce the gap between what they need and what exists on the Web. SWLinker is another system proposed in this thesis with the aim of supporting the interpretation of Web pages using ontology based semantic annotation. It is an extension to the Internet Explorer Web browser that automatically creates a semantic layer of explanatory information and instructional guidance over Web pages. It also aims to break the conventional view of Web browsing as an individual activity by leveraging the notion of ontology-based collaborative browsing. Both of the tools presented in this thesis were evaluated by students within the context of particular learning tasks. The results show that they effectively fulfilled the intended goals by facilitating learning from hypertext without introducing high overheads in terms of usability or browsing efforts