Scaffolding reflective inquiry - enabling why-questioning while e-learning

This paper presents some theoretical and interdisciplinary perspectives that might inform the design and development of information and communications technology (ICT) tools to support reflective inquiry during e-learning. The role of why-questioning provides the focus of discussion and is guided by literature that spans critical thinking, inquiry-based and problem-based learning, storytelling, sense-making, and reflective practice, as well as knowledge management, information science, computational linguistics and automated question generation. It is argued that there exists broad scope for the development of ICT scaffolding targeted at supporting reflective inquiry duringe-learning. Evidence suggests that wiki-based learning tasks, digital storytelling, and e-portfolio tools demonstrate the value of accommodating reflective practice and explanatory content in supporting learning; however, it is also argued that the scope for ICT tools that directly support why-questioning as a key aspect of reflective inquiry is a frontier ready for development

Implementation of an information retrieval system within a central knowledge management system

Páginas numeradas: I-XIII, 14-126Estágio realizado na Wipro Portugal SA e orientado pelo Eng.º Hugo NetoTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

AMBIENTE INTELIGENTE E COLABORATIVO PARA APOIO À PRODUÇÃO ACADÊMICA ESCLARECIMENTO DE DÚVIDAS

A elaboração de trabalhos acadêmicos, como sabemos, exige grande esforço dos autores, em grande parte de cunho operacional, ocupando horas de trabalho que poderiam ser dedicadas às atividades de análise e criação. Entre tais atividades podemos citar o levantamento de informações sobre o tema a ser pesquisado, o gerenciamento de artigos lidos ou a serem lidos, ou a busca por publicações relacionadas ao tema. Sabemos ainda que muitos destes esforços podem ser atenuados através de iniciativas de cooperação e uso de ferramentas computacionais. Existem ferramentas conceituadas que oferecem suporte para algumas dessas etapas, no entanto o acadêmico precisa combinar um ou mais dessas ferramentas ao mesmo tempo. Procurando contribuir para atenuar esse problema concebemos no LIED um Ambiente Inteligente e Colaborativo, que combina técnicas de Inteligência Artificial no intuito de proporcionar um ambiente colaborativo capaz de apoiar computacionalmente, de forma integrada, algumas das etapas essenciais da produção acadêmica. Este trabalho apresenta um subsistema deste ambiente, a Recuperação de Documentos, que apresenta informações de forma direta e automática a partir de perguntas em linguagem natural, considerando o contexto de um projeto e a base de documentos indicada pelo usuário durante as suas interações

Rapport : a fact-based question answering system for portuguese

Question answering is one of the longest-standing problems in natural language processing. Although natural language interfaces for computer systems can be considered more common these days, the same still does not happen regarding access to specific textual information. Any full text search engine can easily retrieve documents containing user specified or closely related terms, however it is typically unable to answer user questions with small passages or short answers. The problem with question answering is that text is hard to process, due to its syntactic structure and, to a higher degree, to its semantic contents. At the sentence level, although the syntactic aspects of natural language have well known rules, the size and complexity of a sentence may make it difficult to analyze its structure. Furthermore, semantic aspects are still arduous to address, with text ambiguity being one of the hardest tasks to handle. There is also the need to correctly process the question in order to define its target, and then select and process the answers found in a text. Additionally, the selected text that may yield the answer to a given question must be further processed in order to present just a passage instead of the full text. These issues take also longer to address in languages other than English, as is the case of Portuguese, that have a lot less people working on them. This work focuses on question answering for Portuguese. In other words, our field of interest is in the presentation of short answers, passages, and possibly full sentences, but not whole documents, to questions formulated using natural language. For that purpose, we have developed a system, RAPPORT, built upon the use of open information extraction techniques for extracting triples, so called facts, characterizing information on text files, and then storing and using them for answering user queries done in natural language. These facts, in the form of subject, predicate and object, alongside other metadata, constitute the basis of the answers presented by the system. Facts work both by storing short and direct information found in a text, typically entity related information, and by containing in themselves the answers to the questions already in the form of small passages. As for the results, although there is margin for improvement, they are a tangible proof of the adequacy of our approach and its different modules for storing information and retrieving answers in question answering systems. In the process, in addition to contributing with a new approach to question answering for Portuguese, and validating the application of open information extraction to question answering, we have developed a set of tools that has been used in other natural language processing related works, such as is the case of a lemmatizer, LEMPORT, which was built from scratch, and has a high accuracy. Many of these tools result from the improvement of those found in the Apache OpenNLP toolkit, by pre-processing their input, post-processing their output, or both, and by training models for use in those tools or other, such as MaltParser. Other tools include the creation of interfaces for other resources containing, for example, synonyms, hypernyms, hyponyms, or the creation of lists of, for instance, relations between verbs and agents, using rules

Capturing negation in question answering systems

This masters thesis will present my research on the world of Negation, as well as my attempt to apply the Negation Algorithm on a new Question Answering system which is able to accept negative input in natural language. The aim of this project is to focus on the uses of negation in natural language, and on the importance of including negative constructions in Information Retrieval processes, which for the moment treat negation as a nonexisting phenomenon in natural language. The new restricted-domain question answering system is called NotFilms, and accepts subject and object questions regarding Movies of 2005. NotFilms reads the input in natural language, produces its semantic representation, applies the IR algorithm on the semantic reading, and provides the user with the exact answer. It allows the existence of the negative particle ’not’ in the input, and as long as the input can be semantically represented by the linguistic processes of the system, it answers both affirmative and negative questions with the same efficiency. Results have shown that the linguistic and IR processes of the system can give relevant answers for 75% of the users’ questions

Hybrid geo-spatial query processing on the semantic web

SemanticWeb data sources such as DBpedia are a rich resource of structured representations of knowledge about geographical features and provide potential data for computing the results of Question Answering System queries that require geo-spatial computations. Retrieval from these resources of all content that is relevant to a particular spatial query of, for example, containment, proximity or crossing is not always straightforward as the geometry is usually confined to point representations and there is considerable inconsistency in the way in which geographical features are referenced to locations. In DBpedia, some geographical feature instances have point coordinates, others have qualitative properties that provide explicit or implicit spatial relationships between named places, and some have neither of these. This thesis demonstrates that structured geo-spatial query, a form of question answering, on DBpedia can be performed with a hybrid query method that exploits quantitative and qualitative spatial properties in combination with a high quality reference geo-dataset that can help to support a full range of geo-spatial query operators such as proximity, containment and crossing as well as vague directional queries such as Find airports north of London?. A quantitative model based on the spatial directional relations in DBpedia has been used to assist in query processing. Evaluation experiments confirm the benefits of combining qualitative and quantitative methods for containment queries and of employing high-quality spatial data, as opposed to DBpedia points, as reference objects for proximity queries, particularly for linear features. The high quality geo-data also enabled answering questions impossible to answer with SemanticWeb resources alone, such as finding geographic features within some distance from a region boundary. The contributions were validated by a prototype geo-spatial query system that combined qualitative and quantitative processing and included ranking answers for directional queries based on models derived from DBpedia contributed data

Representation and Inference for Open-Domain Question Answering: Strength and Limits of two Italian Semantic Lexicons

La ricerca descritta nella tesi è stata dedicata alla costruzione di un prototipo di sistema di Question Answering per la lingua italiana. Il prototipo è stato utilizzato come ambiente di valutazione dell’utilità dell’informazione codificata in due lessici semantici computazionali, ItalWordNet e SIMPLE-CLIPS. Il fine è quello di metter in evidenza ipunti di forza e ilimiti della rappresentazione dell’informazione proposta dai due lessici