Towards the integration of ontologies in the context of MDA at CIM level

In recent years, model-driven engineering has been popularized by the Model-Driven Architecture (MDA) initiative. Essentially, in MDA three types of viewpoints on models are distinguished: the Com- putation Independent Model (CIM), the Platform Independent Model (PIM) and the Platform Speci c Model (PSM). Many research works of MDA are primary focusing on the PIM and PSM, and transformations between each other. On the other hand, the Semantic Web has popular- ized another notion of model: ontologies. MDA may bene ts from on- tologies in formal model of domain semantics and automated reasoning. In this paper an approach for generating an ontology from a Language Extended Lexicon (LeL) with the aim of facilitating the tranformation between CIM and PIM is presented. In addition, a software application, called OntoLEL Tool, that implements this approach is described.Eje: Workshop Ingeniería de software (WIS)Red de Universidades con Carreras en Informática (RedUNCI

Analýza a klasifikace nabíjecích dat pro mikro sítě

This thesis aims to develop a classification model for electric vehicles (EVs) based on data from EV charging stations.The study utilizes a dataset of 6 charging sessions from EV charging station and implements two deep learning algorithm including LSTM and Auto-Encoders to classify EVs. The performance of the classification model is evaluated based on accuracy rates & precision.The study also identifies key charging characteristics that are most significant in distinguishing between different types of EVs, including charging time, energy consumption, and charging patterns.The findings of this research have significant implications for the development of EV charging infras- tructure and services. The classification model developed in this thesis can be used to optimize charging station operations, improve charging services, and develop EV adoption strategies. The study also highlights the importance of utilizing data from EV charging stations in understanding the EV market and improving the efficiency of charging infrastructure.Tato práce si klade za cíl vyvinout model klasifikace elektrických vozidel (EV) založený na datech z nabíjecích stanic pro elektromobily. Studie využívá datovou sadu 6 nabíjecích relací z nabíjecí stanice pro elektromobily a implementuje dva algoritmy hlubokého učení, včetně LSTM a Auto- Encoders pro klasifikaci EV. . Výkon klasifikačního modelu je hodnocen na základě míry přesnosti a preciznosti. Studie také identifikuje klíčové charakteristiky nabíjení, které jsou nejvýznamnější při rozlišování mezi různými typy elektromobilů, včetně doby nabíjení, spotřeby energie a vzorců nabí- jení. Zjištění tohoto výzkumu mají významné důsledky pro rozvoj infrastruktury a služeb nabíjení elektromobilů. Klasifikační model vyvinutý v této práci lze použít k optimalizaci provozu nabíjecích stanic, zlepšení nabíjecích služeb a rozvoji strategií přijetí elektromobilů. Studie také zdůrazňuje důležitost využití dat z nabíjecích stanic pro elektromobily pro pochopení trhu s elektromobily a zlepšení efektivity nabíjecí infrastruktury450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Reflections on the Goals, Concepts and Recommendationsof the IFLA Study on Functional Requirements of Bibliographic Records

The International Federation of Library Associations and Institutions (IFLA) has long promoted international bibliographic standards through its UBCIM Programme and the programs and activities of IFLA Division of Bibliographic Control and its three standing committees. IFLA\u27s many achievements over the years have resulted in several serious re-examinations of cataloguing theories and practices. The study we are discussing this morning is part of a proud tradition going back to 1961 and now leads us through the early twenty-first century. As many of you may remember, the first major IFLA initiative in international bibliographic control took place in 1961 at an international conference in Paris during which a set of cataloguing principles were approved--now known as the Paris Principles. In 1969 another important IFLA-sponsored conference was held in Copenhagen, whose purpose was to consider a resolution to establish international standards for the form and content of bibliographic descriptions. The results of this far-reaching resolution have been the International Standard Bibliographic Description for Monographic Publications, first published in 1971, and its successor standards for all formats. In 1977 the International Congress on National Bibliographies was held in Paris, which called for standards for the printed national bibliography. The congress participants also recommended that greater efforts at national international levels should be made to ensure compatibility between the bibliographic exchange formats of the library and information communities, and the establishment of ISDS centres. Another event of consequence took place in 1997 when the Standing Committee of the IFLA Section on Cataloguing approved the final report of a study on the functional requirements for bibliographic records. The report contained a series of recommendations that could have far reaching consequences for international bibliographic control standards

ENHANCING IMAGE FINDABILITY THROUGH A DUAL-PERSPECTIVE NAVIGATION FRAMEWORK

This dissertation focuses on investigating whether users will locate desired images more efficiently and effectively when they are provided with information descriptors from both experts and the general public. This study develops a way to support image finding through a human-computer interface by providing subject headings and social tags about the image collection and preserving the information scent (Pirolli, 2007) during the image search experience. In order to improve search performance most proposed solutions integrating experts’ annotations and social tags focus on how to utilize controlled vocabularies to structure folksonomies which are taxonomies created by multiple users (Peters, 2009). However, these solutions merely map terms from one domain into the other without considering the inherent differences between the two. In addition, many websites reflect the benefits of using both descriptors by applying a multiple interface approach (McGrenere, Baecker, & Booth, 2002), but this type of navigational support only allows users to access one information source at a time. By contrast, this study is to develop an approach to integrate these two features to facilitate finding resources without changing their nature or forcing users to choose one means or the other. Driven by the concept of information scent, the main contribution of this dissertation is to conduct an experiment to explore whether the images can be found more efficiently and effectively when multiple access routes with two information descriptors are provided to users in the dual-perspective navigation framework. This framework has proven to be more effective and efficient than the subject heading-only and tag-only interfaces for exploratory tasks in this study. This finding can assist interface designers who struggle with determining what information is best to help users and facilitate the searching tasks. Although this study explicitly focuses on image search, the result may be applicable to wide variety of other domains. The lack of textual content in image systems makes them particularly hard to locate using traditional search methods. While the role of professionals in describing items in a collection of images, the role of the crowd in assigning social tags augments this professional effort in a cost effective manner

Extending Faceted Search to the Open-Domain Web

Faceted search enables users to navigate a multi-dimensional information space by combining keyword search with drill-down options in each facets. For example, when searching “computer monitor”\u27 in an e-commerce site, users can select brands and monitor types from the the provided facets {“Samsung”, “Dell”, “Acer”, ...} and {“LET-Lit”, “LCD”, “OLED”, ...}. It has been used successfully for many vertical applications, including e-commerce and digital libraries. However, this idea is not well explored for general web search in an open-domain setting, even though it holds great potential for assisting multi-faceted queries and exploratory search. The goal of this work is to explore this potential by extending faceted search into the open-domain web setting, which we call Faceted Web Search. We address three fundamental issues in Faceted Web Search, namely: how to automatically generate facets (facet generation); how to re-organize search results with users\u27 selections on facets (facet feedback); and how to evaluate generated facets and entire Faceted Web Search systems. In conventional faceted search, facets are generated in advance for an entire corpus either manually or semi-automatically, and then recommended for particular queries in most of the previous work. However, this approach is difficult to extend to the entire web due to the web\u27s large and heterogeneous nature. We instead propose a query-dependent approach, which extracts facets for queries from their web search results. We further improve our facet generation model under a more practical scenario, where users care more about precision of presented facets than recall. The dominant facet feedback method in conventional faceted search is Boolean filtering, which filters search results by users\u27 selections on facets. However, our investigation shows Boolean filtering is too strict when extended to the open-domain setting. Thus, we propose soft ranking models for Faceted Web Search, which expand original queries with users\u27 selections on facets to re-rank search results. Our experiments show that the soft ranking models are more effective than Boolean filtering models for Faceted Web Search. To evaluate Faceted Web Search, we propose both intrinsic evaluation, which evaluates facet generation on its own, and extrinsic evaluation, which evaluates an entire Faceted Web Search system by its utility in assisting search clarification. We also design a method for building reusable test collections for such evaluations. Our experiments show that using the Faceted Web Search interface can significantly improve the original ranking if allowed sufficient time for user feedback on facets

Towards Efficient Novel Materials Discovery

Die Entdeckung von neuen Materialien mit speziellen funktionalen Eigenschaften ist eins der wichtigsten Ziele in den Materialwissenschaften. Das Screening des strukturellen und chemischen Phasenraums nach potentiellen neuen Materialkandidaten wird häufig durch den Einsatz von Hochdurchsatzmethoden erleichtert. Schnelle und genaue Berechnungen sind eins der Hauptwerkzeuge solcher Screenings, deren erster Schritt oft Geometrierelaxationen sind. In Teil I dieser Arbeit wird eine neue Methode der eingeschränkten Geometrierelaxation vorgestellt, welche die perfekte Symmetrie des Kristalls erhält, Resourcen spart sowie Relaxationen von metastabilen Phasen und Systemen mit lokalen Symmetrien und Verzerrungen erlaubt. Neben der Verbesserung solcher Berechnungen um den Materialraum schneller zu durchleuchten ist auch eine bessere Nutzung vorhandener Daten ein wichtiger Pfeiler zur Beschleunigung der Entdeckung neuer Materialien. Obwohl schon viele verschiedene Datenbanken für computerbasierte Materialdaten existieren ist die Nutzbarkeit abhängig von der Darstellung dieser Daten. Hier untersuchen wir inwiefern semantische Technologien und Graphdarstellungen die Annotation von Daten verbessern können. Verschiedene Ontologien und Wissensgraphen werden entwickelt anhand derer die semantische Darstellung von Kristallstrukturen, Materialeigenschaften sowie experimentellen Ergebenissen im Gebiet der heterogenen Katalyse ermöglicht werden. Wir diskutieren, wie der Ansatz Ontologien und Wissensgraphen zu separieren, zusammenbricht wenn neues Wissen mit künstlicher Intelligenz involviert ist. Eine Zwischenebene wird als Lösung vorgeschlagen. Die Ontologien bilden das Hintergrundwissen, welches als Grundlage von zukünftigen autonomen Agenten verwendet werden kann. Zusammenfassend ist es noch ein langer Weg bis Materialdaten für Maschinen verständlich gemacht werden können, so das der direkte Nutzen semantischer Technologien nach aktuellem Stand in den Materialwissenschaften sehr limitiert ist.The discovery of novel materials with specific functional properties is one of the highest goals in materials science. Screening the structural and chemical space for potential new material candidates is often facilitated by high-throughput methods. Fast and still precise computations are a main tool for such screenings and often start with a geometry relaxation to find the nearest low-energy configuration relative to the input structure. In part I of this work, a new constrained geometry relaxation is presented which maintains the perfect symmetry of a crystal, saves time and resources as well as enables relaxations of meta-stable phases and systems with local symmetries or distortions. Apart from improving such computations for a quicker screening of the materials space, better usage of existing data is another pillar that can accelerate novel materials discovery. While many different databases exists that make computational results accessible, their usability depends largely on how the data is presented. We here investigate how semantic technologies and graph representations can improve data annotation. A number of different ontologies and knowledge graphs are developed enabling the semantic representation of crystal structures, materials properties as well experimental results in the field of heterogeneous catalysis. We discuss the breakdown of the knowledge-graph approach when knowledge is created using artificial intelligence and propose an intermediate information layer. The underlying ontologies can provide background knowledge for possible autonomous intelligent agents in the future. We conclude that making materials science data understandable to machines is still a long way to go and the usefulness of semantic technologies in the domain of materials science is at the moment very limited

Management of data quality when integrating data with known provenance

Abstract unavailable please refer to PD

Big Data Mining to Construct Truck Tours

Cross-Border shipping of goods among different distributors is an essential part of transportation across Canada and U.S. These two countries are heavily dependent on border crossing locations to facilitate international trade between each other. This research considers the identification of the international tours accomplishing the shipping of goods. A truck tour is a round trip where a truck starts its journey from its firm or an industry, performing stops for different purposes that include taking a rest, fuel refilling, and transferring goods to multiple locations, and returns back to its initial firm location. In this thesis, we present a three step method on mining GPS truck data to identify all possible truck tours belonging to different carriers. In the first step, a clustering technique is applied on the stop locations to discover the firm for each carrier. A modified DBSCAN algorithm is proposed to achieve this task by automatically determining the two input parameters based on the data points provided. Various statistical measures like count of unique trucks and count of truck visits are applied on the resulting clusters to identify the firms of the respective carriers. In the second step, we tackle the problem of classifying the stop locations into two types: primary stops, where goods are transferred, and secondary stops like rest stations, where vehicle and driver needs are met. This problem is solved using one of the trade indicator called Specialization Index. Moreover, several set of features are explored to build the classification model to classify the type of stop locations. In the third step, having identified the firm, primary and secondary locations, an automated path finder is developed to identify the truck tours starting from each firm. The results of the specialization index and the feature-based classification in identifying stop events are compared with the entropy index from previous work. Experimental results show that the proposed set of cluster features significantly add classification power to our model giving 98.79% accuracy which in turn helps in discovering accurate tours

Embedding Based Link Prediction for Knowledge Graph Completion

Knowledge Graphs (KGs) are the most widely used representation of structured information about a particular domain consisting of billions of facts in the form of entities (nodes) and relations (edges) between them. Besides, the KGs also encapsulate the semantic type information of the entities. The last two decades have witnessed a constant growth of KGs in various domains such as government, scholarly data, biomedical domains, etc. KGs have been used in Machine Learning based applications such as entity linking, question answering, recommender systems, etc. Open KGs are mostly heuristically created, automatically generated from heterogeneous resources such as text, images, etc., or are human-curated. However, these KGs are often incomplete, i.e., there are missing links between the entities and missing links between the entities and their corresponding entity types. This thesis focuses on addressing these two challenges of link prediction for Knowledge Graph Completion (KGC): \textbf{(i)} General Link Prediction in KGs that include head and tail prediction, triple classification, and \textbf{(ii)} Entity Type Prediction. Most of the graph mining algorithms are proven to be of high complexity, deterring their usage in KG-based applications. In recent years, KG embeddings have been trained to represent the entities and relations in the KG in a low-dimensional vector space preserving the graph structure. In most published works such as the translational models, convolutional models, semantic matching, etc., the triple information is used to generate the latent representation of the entities and relations. In this dissertation, it is argued that contextual information about the entities obtained from the random walks, and textual entity descriptions, are the keys to improving the latent representation of the entities for KGC. The experimental results show that the knowledge obtained from the context of the entities supports the hypothesis. Several methods have been proposed for KGC and their effectiveness is shown empirically in this thesis. Firstly, a novel multi-hop attentive KG embedding model MADLINK is proposed for Link Prediction. It considers the contextual information of the entities by using random walks as well as textual entity descriptions of the entities. Secondly, a novel architecture exploiting the information contained in a pre-trained contextual Neural Language Model (NLM) is proposed for Triple Classification. Thirdly, the limitations of the current state-of-the-art (SoTA) entity type prediction models have been analysed and a novel entity typing model CAT2Type is proposed that exploits the Wikipedia Categories which is one of the most under-treated features of the KGs. This model can also be used to predict missing types of unseen entities i.e., the newly added entities in the KG. Finally, another novel architecture GRAND is proposed to predict the missing entity types in KGs using multi-label, multi-class, and hierarchical classification by leveraging different strategic graph walks in the KGs. The extensive experiments and ablation studies show that all the proposed models outperform the current SoTA models and set new baselines for KGC. The proposed models establish that the NLMs and the contextual information of the entities in the KGs together with the different neural network architectures benefit KGC. The promising results and observations open up interesting scopes for future research involving exploiting the proposed models in domain-specific KGs such as scholarly data, biomedical data, etc. Furthermore, the link prediction model can be exploited as a base model for the entity alignment task as it considers the neighbourhood information of the entities