The overall context of this thesis research is to explore natural language as a medium to interact with computer software in the graphics domain, e.g. programs like MS Paint or OpenGL. A core element of most natural language understanding systems is an ontology, which represents concepts and items of the underlying domain of discourse. This thesis presents an ontology for the graphics domain based on several resources, including documentation and textbooks on graphics systems, existing ontologies, and - most importantly - a collection of natural language instructions to create and modify graphic images. The ontology was developed in several phases, and finally tested as part of a complex natural language interface. This natural language interface accepts verbal instructions in the graphics domain as input and creates matching graphic images as output. The results of our tests indicate an accuracy of the system in the area of 80%.

The overall context of this thesis research is to explore natural language as a medium to interact with computer software in the graphics domain, e.g. programs like MS Paint or OpenGL. A core element of most natural language understanding systems is an ontology, which represents concepts and items of the underlying domain of discourse. This thesis presents an ontology for the graphics domain based on several resources, including documentation and textbooks on graphics systems, existing ontologies, and - most importantly - a collection of natural language instructions to create and modify graphic images. The ontology was developed in several phases, and finally tested as part of a complex natural language interface. This natural language interface accepts verbal instructions in the graphics domain as input and creates matching graphic images as output. The results of our tests indicate an accuracy of the system in the area of 80%.

Today an increasing amount of systems make use of Natural Language Interfaces (NLIs), which make them easy and efficient to use. The purpose of this research was to gain an increased understanding of the usability of different input methods for NLIs. This was done by implementing two versions of a text-based game with an NLI, where one version used speech as input method and the other used text. Tests were then performed with users that all played both versions of the game and then evaluated them individually using the System Usability Scale. It was found that text was better as input method in all aspects. However, speech scored high when the users felt confident in their English proficiency, acknowledging the possibility of using speech as input method for NLIs. / Idag använder en ökande mängd system naturliga språkgränssnitt, vilket gör dem enkla och effektiva att använda. Syftet med denna forskning var att få en ökad förståelse för användbarheten av olika inmatningsmetoder för naturliga språkgränssnitt. Detta gjordes genom att skapa två versioner av ett text-baserat spel med ett naturligt språkgränssnitt, där en version använde tal som inmatningsmetod och andra använde text. Tester utfördes sedan med användare som alla spelade igenom båda versionerna av spelet och sedan utvärderade dem individuellt med hjälp av System Usability Scale, ett system för att mäta graden av användbarhet. Det konstaterades att text fungerade bättre som inmatningsmetod ur alla aspekter. Tal fick dock en hög poäng när användarna kände sig säkra på sin engelska kunnighet, vilket talar för möjligheten att använda tal som en inmatningsmetod för naturliga gränssnitt.

[pt] Os sistemas de busca textual fornecem aos usuários uma alternativa amigável para acessar datasets RDF (Resource Description Framework). A avaliação de desempenho de tais sistemas requer benchmarks adequados, consistindo de datasets RDF, consultas e respectivas respostas esperadas. No entanto, os benchmarks disponíveis geralmente possuem poucas consultas e respostas incompletas, principalmente porque são construídos manualmente com a ajuda de especialistas. A contribuição central desta tese é um método para construir benchmarks automaticamente, com um maior número de consultas e com respostas mais completas. O método proposto aplica-se tanto a consultas baseadas em palavras-chave quanto em linguagem natural e possui duas partes: geração de consultas e geração de respostas. A geração de consultas seleciona um conjunto de entidades relevantes, chamadas de indutores, e, para cada uma, heurísticas orientam o processo de extração de consultas relacionadas. A geração de respostas recebe as consultas produzidas no passo anterior e computa geradores de solução (SG), subgrafos do dataset original contendo diferentes respostas às consultas. Heurísticas também orientam a construção dos SGs evitando o desperdiço de recursos computacionais na geração de respostas irrelevantes. / [en] Text search systems provide users with a friendly alternative to access Resource Description Framework (RDF) datasets. The performance evaluation of such systems requires adequate benchmarks, consisting of RDF datasets, text queries, and respective expected answers. However, available benchmarks often have small sets of queries and incomplete sets of answers, mainly because they are manually constructed with the help of experts. The central contribution of this thesis is a method for building benchmarks automatically, with larger sets of queries and more complete answers. The proposed method works for both keyword and natural language queries and has two steps: query generation and answer generation. The query generation step selects a set of relevant entities, called inducers, and, for each one, heuristics guide the process of extracting related queries. The answer generation step takes the queries and computes solution generators (SG), subgraphs of the original dataset containing different answers to the queries. Heuristics also guide the construction of SGs, avoiding the waste of computational resources in generating irrelevant answers.

Natural language interfaces to databases (NLIDB) are systems that aim to bridge the gap between the languages used by humans and computers, and automatically translate natural language sentences to database queries. This thesis proposes a novel approach to NLIDB, using graph-based models. The system starts by collecting as much information as possible from existing databases and sentences, and transforms this information into a knowledge base for the system. Given a new question, the system will use this knowledge to analyze and translate the sentence into its corresponding database query statement. The graph-based NLIDB system uses English as the natural language, a relational database model, and SQL as the formal query language. In experiments performed with natural language questions ran against a large database containing information about U.S. geography, the system showed good performance compared to the state-of-the-art in the field.

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In this thesis I address the topic of a non-visual approach for interaction on mobile,as an alternative to their existing visual displays in situations where hands free usageof the device is preferred. The current technology will be examined through existingwork with special attention to its limitations, which user groups are currently using anysort of speech recognition or voice command functions and look at in which scenariosare these the most used and most desired. Then I will examine through interviews whypeople trust or distrust voice interactions and how they feel about the possibilities andlimitations of the technology at hand, how individual users use this currently and wheredo they see the technology in the future. After this I will develop an alternative voiceinteraction concept, and validate it through a set of workshops.

[pt] Perguntas e Respostas (Question Answering - QA) é um campo de estudo dedicado à construção de sistemas que respondem automaticamente a perguntas feitas em linguagem natural. A tradução de uma pergunta feita em linguagem natural em uma consulta estruturada (SQL ou SPARQL) em um banco de dados também é conhecida como Interface de Linguagem Natural para Bancos de Dados (Natural Language Interface to Database - NLIDB). Os sistemas NLIDB geralmente não lidam com agregações, que podem ter os seguintes elementos: funções de agregação (como contagem, soma, média, mínimo e máximo), uma cláusula de agrupamento (GROUP BY) e uma cláusula HAVING. No entanto, eles fornecem bons resultados para consultas normais. Esta dissertação aborda a criação de um módulo genérico, para ser utilizado em sistemas NLIDB, que permite a tais sistemas realizar consultas com agregações, desde que os resultados da consulta que o NLIDB retorna sejam, ou possam ser transformados, em um resultado no formato tabular. O trabalho cobre agregações com especificidades como ambiguidades, diferenças de escala de tempo, agregações em atributos múltiplos, o uso de adjetivos superlativos, reconhecimento básico de unidade de medida, agregações em atributos com nomes compostos e subconsultas com funções de agregação aninhadas em até dois níveis. / [en] Question Answering (QA) is a field of study dedicated to building systems that automatically answer questions asked in natural language. The translation of a question asked in natural language into a structured query (SQL or SPARQL) in a database is also known as Natural Language Interface to Database (NLIDB). NLIDB systems usually do not deal with aggregations, which can have the following elements: aggregation functions (as count, sum, average, minimum and maximum), a grouping clause (GROUP BY) and a having clause (HAVING). However, they deliver good results for normal queries. This dissertation addresses the creation of a generic module, to be used in NLIDB systems, that allows such systems to perform queries with aggregations, on the condition that the query results the NLIDB return are, or can be transformed into, a result set in the form of a table. The work covers aggregations with specificities such as ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic unit measure recognition, aggregations in attributes with compound names and subqueries with aggregation functions nested up to two levels.