The goal with this project was to convert open structured data, e.g. csv-files, to Linked Open Data and to demonstrate possible uses of such data. The project was also supposed to inspire and lay a foundation for future work in the area. The conversion was done using the D2RQ platform, and the chosen dataset contains air quality measurements from Umeå Municipality. The resulting data was then published on the internet with D2R server. For demonstration purposes two tasks were done. A visualization of the converted data was published on the web, together with traffic and weather data. Secondly a physical city model was built of carton with eight photodiodes, a Raspberry Pi and visualized with a local webserver. The goals for the project were fulfilled and it has also inspired Knowit to continue with commercial projects in the area of Linked Open Data. Future effort should be put in converting more data to Linked Open Data and to create full scale sensor networks in a city.

Texts and images provide alternative, yet orthogonal views of the same underlying cognitive concept. By uncovering synergistic, semantic relationships that exist between words and images, I am working to develop novel techniques that can help improve tasks in natural language processing, as well as effective models for text-to-image synthesis, image retrieval, and automatic image annotation. Specifically, in my dissertation, I will explore the interoperability of features between language and vision tasks. In the first part, I will show how it is possible to apply features generated using evidence gathered from text corpora to solve the image annotation problem in computer vision, without the use of any visual information. In the second part, I will address research in the reverse direction, and show how visual cues can be used to improve tasks in natural language processing. Importantly, I propose a novel metric to estimate the similarity of words by comparing the visual similarity of concepts invoked by these words, and show that it can be used further to advance the state-of-the-art methods that employ corpus-based and knowledge-based semantic similarity measures. Finally, I attempt to construct a joint semantic space connecting words with images, and synthesize an evaluation framework to quantify cross-modal semantic relationships that exist between arbitrary pairs of words and images. I study the effectiveness of unsupervised, corpus-based approaches to automatically derive the semantic relatedness between words and images, and perform empirical evaluations by measuring its correlation with human annotators.

<p>The general objective of the thesis was to examine aspects of declarative memory functioning across the adult life span. The four papers were based on data collected as part of the Betula Prospective Cohort Study (Nilsson et al., 1997) and included largescale population-based samples of participants in the age range 35 to 90. In study I and study II the possibility that age differences in episodic memory may be compensated for by provision of encoding support in the form of enactment was investigated, using free and cued recall and recognition portioned into components of recollective experience as the dependent measures. In Study III, unitary, two-, and multi-factorial models of declarative memory were compared and age-invariance was tested for. In Study IV cross-sectional age differences were contrasted with five-year longitudinal changes on aggregate measures of episodic and semantic memory within age groups ranging from 35 to 85 years. The results of Study I and Study II demonstrated that enactment constitutes an effective form of encoding support, but that the age differences generalize across this form of encoding support. Study II indicated that most of the age-related variance in recognition and levels of recollective experience following enacted and non-enacted encoding was shared by a measure of processing speed. Study III confirmed that a two-factor model of declarative memory (episodic and semantic memory) yields superior fit as compared with a unitary model of declarative memory. However, the best fitting model was a six-factor model with recall and recognition (episodic memory) and knowledge and fluency (semantic associated with different patterns of age-related differences, with some indications that the first-order factors show differential age-related patterns, indicative of variability that cross-sectional data may give a false impression of decline for adults in the age range 35-60 years for episodic memory. There was no evidence of time-related decline within these age groups, even though practice effects were taken into account. However, past this age, substantial time-related decline was observed for the older adults, in line with cross-sectional data. Semantic memory performance tended to improve across time for the younger groups, but decline in old age, although the magnitude of this decline was less pronounced than for episodic memory. Cohort differences in education may be one important factor underlying the discrepancy between the cross-sectional and longitudinal aging patterns, both in the case of episodic and semantic memory. In conclusion, the result of the present studies show that age-related functional losses occur in forms of declarative memory, especially memory) as first-order factors. Episodic and semantic memory were found to be within the episodic and semantic memory domains. The results of Study IV showed episodic memory, but that the onset of decline does not begin until old age. </p>

The general objective of the thesis was to examine aspects of declarative memory functioning across the adult life span. The four papers were based on data collected as part of the Betula Prospective Cohort Study (Nilsson et al., 1997) and included largescale population-based samples of participants in the age range 35 to 90. In study I and study II the possibility that age differences in episodic memory may be compensated for by provision of encoding support in the form of enactment was investigated, using free and cued recall and recognition portioned into components of recollective experience as the dependent measures. In Study III, unitary, two-, and multi-factorial models of declarative memory were compared and age-invariance was tested for. In Study IV cross-sectional age differences were contrasted with five-year longitudinal changes on aggregate measures of episodic and semantic memory within age groups ranging from 35 to 85 years. The results of Study I and Study II demonstrated that enactment constitutes an effective form of encoding support, but that the age differences generalize across this form of encoding support. Study II indicated that most of the age-related variance in recognition and levels of recollective experience following enacted and non-enacted encoding was shared by a measure of processing speed. Study III confirmed that a two-factor model of declarative memory (episodic and semantic memory) yields superior fit as compared with a unitary model of declarative memory. However, the best fitting model was a six-factor model with recall and recognition (episodic memory) and knowledge and fluency (semantic associated with different patterns of age-related differences, with some indications that the first-order factors show differential age-related patterns, indicative of variability that cross-sectional data may give a false impression of decline for adults in the age range 35-60 years for episodic memory. There was no evidence of time-related decline within these age groups, even though practice effects were taken into account. However, past this age, substantial time-related decline was observed for the older adults, in line with cross-sectional data. Semantic memory performance tended to improve across time for the younger groups, but decline in old age, although the magnitude of this decline was less pronounced than for episodic memory. Cohort differences in education may be one important factor underlying the discrepancy between the cross-sectional and longitudinal aging patterns, both in the case of episodic and semantic memory. In conclusion, the result of the present studies show that age-related functional losses occur in forms of declarative memory, especially memory) as first-order factors. Episodic and semantic memory were found to be within the episodic and semantic memory domains. The results of Study IV showed episodic memory, but that the onset of decline does not begin until old age.

While pragmatics, through its integration of situational awareness and real world relevant knowledge, offers a high level of analysis that is suitable for real interpretation of natural dialogue, semantics, on the other end, represents a lower yet more tractable and affordable linguistic level of analysis using current technologies. Generally, the understanding of semantic meaning in literature has revolved around the famous quote ``You shall know a word by the company it keeps''. In this thesis we investigate the role of context constituents in decoding the semantic meaning of the engulfing context; specifically we probe the role of salient concepts, defined as content-bearing expressions which afford encyclopedic definitions, as a suitable source of semantic clues to an unambiguous interpretation of context. Furthermore, we integrate this world knowledge in building a new and robust unsupervised semantic model and apply it to entail semantic relatedness between textual pairs, whether they are words, sentences or paragraphs. Moreover, we explore the abstraction of semantics across languages and utilize our findings into building a novel multi-lingual semantic relatedness model exploiting information acquired from various languages. We demonstrate the effectiveness and the superiority of our mono-lingual and multi-lingual models through a comprehensive set of evaluations on specialized synthetic datasets for semantic relatedness as well as real world applications such as paraphrase detection and short answer grading. Our work represents a novel approach to integrate world-knowledge into current semantic models and a means to cross the language boundary for a better and more robust semantic relatedness representation, thus opening the door for an improved abstraction of meaning that carries the potential of ultimately imparting understanding of natural language to machines.

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