Företaget Widespace hanterar hundratals ärenden i veckan vilket kräver stor överblick för varje an-ställd att sätta sig in i varje enskilt ärende. På grund av denna kvantitet blir uppgiften att skapa över-blicken ett stort problem. För att lösa detta problem krävs en mer konsekvent användning av meta-data och därför har en litteraturstudie om metadata, automatiserad sammanfattning och nyckelords-generering utförts. Arbetet gick ut på att utveckla en prototyp som automatisk kan generera en sammanfattning av texten från ett ärende, samt generera en lista av nyckelord och ge en indikation om vilket språk texten är skriven i. Det ingick också i arbetet att göra en undersökning av tidigare arbeten för att se vilka system och metoder som kan användas för att lösa denna uppgift. Två egenutvecklade prototyper, MkOne och MkTwo, jämfördes med varandra och utvärderades därefter. Metoderna som använts bygger på både statistiska och lingvistiska processer. En analys av resultaten gjordes och visade att prototypen MkOne levererade bäst resultat för sammanfattningen och att nyckelordlistan tillhandahöll nyckelord av hög precision och en bred täckning. / The company Widespace handles hundreds of tasks (tickets) per week, which requires great overview by each employee. Because of this quantity, creating a clear view becomes a major problem. To solve this problem, a more consistent use of metadata is required, therefore, a study of metadata, automated summary and key words generation has been performed. The task was to develop a prototype that can automatically generate a summary, a list of keywords and give an indication of what language the text is written in. It was also included in the work to make a survey of earlier works to see which systems and methods that can be used for this task. Two prototypes were developed, compared with each other and evaluated. The methods used were based on both statistical and linguistic processes. Analysis of the results was done and showed that the prototype MkOne delivered the best results for the summary. The keyword list contained many precise keywords with high precision and a wide coverage.

There is almost a universal agreement among scholars in information retrieval (IR) research that knowledge representation needs improvement. As core component of an IR system, improvement of the knowledge representation system has so far involved manipulation of this component based on principles such as vector space, probabilistic approach, inference network, and language modeling, yet the required improvement is still far from fruition. One promising approach that is highly touted to offer a potential solution exists in the cognitive paradigm, where knowledge representation practice should involve, or start from, modeling the human conceptual system. This study based on two related cognitive theories: the theory-based approach to concept representation and the psychological theory of semantic relations, ventured to explore the connection between the human conceptual model and the knowledge representation model (represented by samples of concepts and relations from the unified medical language system, UMLS). Guided by these cognitive theories and based on related and appropriate data-analytic tools, such as nonmetric multidimensional scaling, hierarchical clustering, and content analysis, this study aimed to conduct an exploratory investigation to answer four related questions. Divided into two groups, a total of 89 research participants took part in two sets of cognitive tasks. The first group (49 participants) sorted 60 food names into categories followed by simultaneous description of the derived categories to explain the rationale for category judgment. The second group (40 participants) performed sorting 47 semantic relations (the nonhierarchical associative types) into 5 categories known a priori. Three datasets resulted as a result of the cognitive tasks: food-sorting data, relation-sorting data, and free and unstructured text of category descriptions. Using the data analytic tools mentioned, data analysis was carried out and important results and findings were obtained that offer plausible explanations to the 4 research questions. Major results include the following: (a) through discriminant analysis category members were predicted consistently in 70% of the time; (b) the categorization bases are largely simplified rules, naïve explanations, and feature-based; (c) individuals theoretical explanation remains valid and stays stable across category members; (d) the human conceptual model can be fairly reconstructed in a low-dimensional space where 93% of the variance in the dimensional space is accounted for by the subjects performance; (e) participants consistently classify 29 of the 47 semantic relations; and, (f) individuals perform better in the functional and spatial dimensions of the semantic relations classification task and perform poorly in the conceptual dimension.