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Large Language Models (LLM) and their abilities of text-to-SQL are today a very relevant topic, as utilizing an LLM as a database interface would facilitate easy access to the data in the database without any prior knowledge of SQL. What is being studied in this thesis, is how to best structure a prompt to increase the accuracy of an LLM on a text-to-SQL task. The methods of experimentation used in the study were experimentation with 5 different prompts, and a total of 22 questions asked about the database with the questions being of difficulties varying from easy to extra hard. The results showed that a simpler, less descriptive prompt performed better on the easy and medium questions, while a more descriptive prompt performed better on the hard and extra hard questions. The f indings did not fully align with the hypothesis that more descriptive prompts would have the most correct outputs. In conclusion, it seemed that prompts that contained less ”clutter” and were more straightforward were more effective on easy questions, while on harder questions a prompt with a better description and examples had a better impact.

Title: Semantic annotation and querying RDF data Author: Jakub Kýpeť Department: Department of Software Engineering Supervisor: Prof. RNDr. Peter Vojtáš, DrSc. Abstract: The presented thesis in detail describes a design and an implementation of self-sustained server application, that allows us to create and manage semantic annotations for various web pages. In the first part it describes the manual annotations and the human interface we have build for them. In the second part it also describes our implementation for a web crawler and an automatic annotation system utilizing this crawler. The last part of the thesis analyzes the testing of this automated system that has been performed using several e- commerce websites with different domains. Keywords: semantic annotation, querying RDF data, user interface, web crawl- ing, automatization

In this thesis, a system, called Ontology-Driven Video Annotation and Retrieval System (OntoVARS) is developed in order to provide a video management system which is used for ontology-driven semantic content annotation and querying. The proposed system is based on MPEG-7 ontology which provides interoperability and common communication platform with other MPEG-7 ontology compatible systems. The Rhizomik MPEG-7 ontology is used as the core ontology and domain specific ontologies are integrated to the core ontology in order to provide ontology-based video content annotation and querying capabilities to the user. The proposed system supports content-based annotation and spatio-temporal data modeling in video databases by using the domain ontology concepts. Moreover, the system enables ontology-driven query formulation and processing according to the domain ontology instances and concepts. In the developed system, ontology-driven concept querying, spatio-temporal querying, region-based and time-based querying capabilities are performed as simple querying types. Besides these simple query types, compound queries are also generated by combining simple queries with &quot / (&quot / , &quot / )&quot / , &quot / AND&quot / and &quot / OR&quot / operators. For all these query types, the system supports both general and video specific query processing. By this means, the user is able to pose queries on all videos in the video databases as well as the details of a specific video of interest.

Master of Science / Department of Computing and Information Sciences / Doina Caragea / In recent years, we have witnessed a significant increase in the number, size and diversity of the available data sources in many application domains. Data sources in a particular domain are autonomously created and maintained, and therefore distributed and semantically heterogeneous. In this thesis, we focused on the problem of querying such semantically heterogeneous data sources from a user's perspective. We approach this problem by using the concepts of ontologies and mappings between ontologies. A system for answering queries in a transparent way to the user has been designed and implemented. The main components of this system are an ontology mapping algorithm that maps user ontologies to data source ontologies, and a query processing engine that maps user queries to queries that can be answered by the data sources in the system. We have shown that machine learning algorithms can also be incorporated in the system, thus making it possible to learn machine learning classifiers (in particular, generative models such as Naïve Bayes) from distributed, semantically heterogeneous data sources. Because many data sources today are relational in nature, in this work we have dealt specifically with relational data sources, as opposed to flat files, XML or object oriented data sources. However, our system can be easily extended to other types of data sources.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

La problématique d'interrogation d'une base de données en présence d'une ontologie (OBQA pour "Ontology-based Query Answering") consiste à prendre en compte des connaissances générales, typiquement une ontologie de domaine, lors de l'évaluation d'une requête. Dans le cadre de cette thèse, ces connaissances sont représentées par des formules de la logique du premier ordre appelées "règles existentielles". Les règles existentielles sont aussi connues sous le nom de règles Datalog+/- ou "tuple-generating dependencies". Nous considérons une approche couramment utilisée, qui consiste à réécrire la requête en exploitant les règles de façon à se ramener à un problème classique l'interrogation d'une base de données. Nous définissons un cadre théorique d'étude des algorithmes de réécriture d'une requête conjonctive en une union de requêtes conjonctives, accompagné d'un algorithme de réécriture générique, prenant en paramètre un opérateur de réécriture. Nous proposons ensuite plusieurs opérateurs de réécriture et développons différentes optimisations, que nous évaluons sur des benchmarks du domaine. / The issue of querying a knowledge base, also called Ontology-based Query Answering (OBQA), consists of taking into account general knowledge, typically a domain ontology, when evaluating queries. In this thesis, ontological knowledge is represented by first-order logical formulas, called existential rules. Existential rules are also known as Datalog+/- and tuple-generating dependencies. We adopt a well-known approach, which consists of rewriting the query with the rules to reduce the problem to a classical database query answering problem. We define a theoretical framework to study algorithms that rewrite a conjunctive query into a union of conjunctive queries, as well as a generic rewriting algorithm that takes into account a rewriting operator. Then, we propose several rewriting operators and develop several optimisations, which we evaluate on benchmarks of the domain.

In this thesis, we explore the issues of uncertain data management in several different aspects. First, we propose a novel linear time algorithm to compute the positional probability, the computation of which is a primitive operator for most of the ranking definitions. Our algorithm is based on the conditional probability formulation of positional probability and the system of linear equations. Based on the formulation of conditional probability, we also prove a tight upper bound of the top-k probability of tuples, which is then used to stop the top-k computation earlier. Second, we study top-k probabilistic ranking queries with joins when scores and probabilities are stored in different relations. We focus on reducing the join cost in probabilistic top-k ranking. We investigate two probabilistic score functions, namely, expected rank value and probability of highest ranking. We give upper/lower bounds of such probabilistic score functions in random access and sequential access, and propose new I/O efficient algorithms to find top-k objects. Third, we extend the possible worlds semantics to probabilistic XML ranking query, which is to rank top-k probabilities of the answers of a twig query in probabilistic XML data. The new challenge is how to compute top-k probabilities of answers of a twig query in probabilistic XML in the presence of containment (ancestor/descendant) relationships. We focus on node queries first, and propose a new dynamic programming algorithm which can compute top-k probabilities for the answers of node queries based on the previously computed results in probabilistic XML data. We further propose optimization techniques to share the computational cost. We also show techniques to support path queries and tree queries. Fourth, we study how to rank documents using a set of keywords, given a context that is associated with the documents. We model the problem using a graph with two different kinds of nodes (document nodes and multi-attribute nodes), where the edges between document nodes and multi-attribute nodes exist with some probability. We discuss its score function, cost function, and ranking with uncertainty. We also propose new algorithms to rank documents that are most related to the user-given keywords by integrating the context information. / Uncertain data management has received a lot of attentions recently due to the fact that data obtained can be incomplete or uncertain in many real applications. Ranking of uncertain data becomes an important research issue, the possible worlds semantics-based ranking makes it different from the ranking of deterministic data. In the traditional deterministic data, we can compute a score for each object, and then the objects are ranked based on the computed scores. However, in the scenario of uncertain data, each object has a probability to be the true answer (or the existence probability), besides the computed score. A probabilistic top-k ranking query ranks objects by the interplay of score and probability based on the possible worlds semantics. Many definitions have been proposed in the literature based on the possible worlds semantics. / Chang, Lijun. / Advisers: Hong Cheng; Jeffrey Xu Yu. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 131-139). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

"Keeping views fresh by maintaining the consistency between materialized views and their base data in the presence of base updates is a critical problem for many applications, including data warehousing and data integration. While heavily studied for traditional databases, the maintenance of XML views remains largely unexplored. Maintaining XML views is complex due to the richness of the XML data model and the powerful capabilities of XML query languages, such as XQuery. This dissertation proposes a comprehensive solution for the general problem of maintaining materialized XQuery views. Our solution is the first to enable the maintenance of a large class of XQuery views including XPath expressions, FLWOR expressions, and Element Constructors. These views may contain arbitrary result construction and arbitrary grouping and join operations. Our solution also supports the unique order requirements of XQuery including source document order and query order. The contributions of this dissertation include: (i) an efficient solution for supporting order in XML query processing and view maintenance, (ii) an identifier-based technique for enabling incremental construction of XML views, (iii) a mechanism for modeling and validating source XML updates, (iv) a counting algorithm for supporting view maintenance on delete and modify updates, (v) an algebraic solution for propagating bulk XML updates, and (vi) an efficient mechanism for refreshing materialized XML views on propagated updates. We provide proofs of correctness of our proposed techniques for materialized XQuery maintenance. We have implemented a prototype of our view maintenance solution on top of the Rainbow XML query engine, developed at WPI. Our experiments confirm that our solution provides a practical and efficient solution for maintaining materialized XQuery views even when handling heterogeneous batches of possibly large source updates. Our solution follows the widely adopted propagate-apply framework for view maintenance common to all mainstream query engines. That is, our solution produces incremental maintenance plans in the same algebraic language used to define the views. These plans can thus be optimized and executed by standard query processing techniques. Being compatible with standard frameworks paves the way for our XML view maintenance solution to be easily adopted by existing database engines."

"Update operations over XML views are essential for applications using XML views. In this dissertation work, we provide scalable solutions to support updating through XML views defined over relational databases. Especially we focus on the update-public semantic, where updates are always public (made to the public database), and the update-local semantic, where update effects are first kept local and then made public as and when required. Towards this, we propose the clean extended-source theory for determining whether a correct view update translation exists, which then serves as a theoretical foundation for us to design practical XML view updating algorithms. Under update-public semantic, state-of-the-art view updating work focus on identifying the correct update translation purely on the data. We instead take a schema-centric solution, which utilizes the schema of the underlying source to effectively prune updates that are guaranteed to be not translatable and pass updates that are guaranteed to be translatable directly to the SQL engine. Only those updates that could not be classified using schema knowledge are finally analyzed by examining the data. This required data-level check is further optimized under schema guidance to prune the search space for finding a correct translation. As the first work addressing the update-local semantic, we propose a practical framework, called LoGo. LoGo Localizes the view update translation, while preserves the properties of views being side-effect free and updates being always updatable. LoGo also supports on-demand merging of the local database of the subject viewinto the public database (also called global database), while still guaranteeing the subject view being free of side effects. A flexible synchronization service is provided in LoGo that enables all other views defined over the same public database to be refreshed, i.e., synchronized with the publically committed changes, if so desired. Further, given that XMLis an ordered datamodel,we propose an ordersensitive solution named O-HUX to support XML view updating with order. We have implemented the algorithms, along with respective optimization techniques. Experimental results confirm the effectiveness of the proposed services, and highlight its performance characteristics."