Context. Information overload is an increasingly important problem of our age where the amount of data we have is expanding drastically with the use of digital communication. Information retrieval models are developed to help overcoming this problem with computerized tools. Semantic information retrieval, which means retrieving information based on the interpretations of meanings of the words, is one of these models and started to be used commonly to handle large amount of data in the Internet and in enterprises to overcome information overload problems. Objectives. In this study we investigate different information retrieval models for using with knowledge management systems in large-scale organizations from the perspective of software engineers. To this end, we aim at identifying existing issues and needs about information overload and then assessing different solutions against these needs. Afterwards, we analyze the chosen solution, which is semantic search, and define and carry out an implementation process to reflect on it. Finally, the usefulness and feasibility of this type of solutions to overcome the specified information overload problems in software engineering is studied and discussed. Methods. We performed a literature review to extract the existing knowledge, technology, and the problems and solutions in the defined context. Then a case study was conducted at a development site of Ericsson AB in Sweden. Case study involved unstructured and semi-structured interviews for data collection, and an implementation attempt for a simple semantic knowledge management system. Thematic Coding Analysis method is used for qualitative data analysis. Results. We identified 23 codes that are categorized under 8 themes from the opinions of company practitioners about semantic knowledge management systems. They are mainly about the existing problems, arguments for using semantic system for solving them, and suggestions and challenges. Conclusions. We conclude that semantic knowledge management systems have a very high potential to solve information overload problems in software engineering if the necessary measures are taken. We found that the problems are related to search engine and the document structure of the tools; usefulness of semantic system is the capability of ontology based retrieval to filter out irrelevant documents and extract hidden data and people’s skills and interests; and finally the challenge is the necessary endeavor to elicit and satisfy all the needs.

Com o intuito de auxiliar usuários na procura por produtos relevantes, sistemas Web integraram módulos de recomendação de itens, que selecionam automaticamente conteúdo de acordo com os interesses de cada indivíduo. Apesar de existirem diversas abordagens para calcular recomendações de acordo com interações disponíveis no sistema, a maioria delas sofre com a carência de informações utilizadas para caracterizar as preferências dos usuários e as descrições dos itens. Trabalhos recentes sobre sistemas de recomendação têm estudado a possibilidade de utilizar revisões de usuários como fonte de metadados, já que são criadas colaborativamente pelos indivíduos. Entretanto, ainda carecem de estudos sobre como organizar e estruturar os dados de maneira semântica. Desta maneira, este trabalho tem como objetivo desenvolver técnicas de construção de representação de itens baseadas em descrições colaborativas para um sistema de recomendação. Objetiva-se analisar o impacto que métodos distintos de desambiguação lexical de sentido causam na precisão da recomendação, sendo avaliada no cenário de predição de notas. A partir dessa estruturação, é possível caracterizar os itens e usuários de maneira mais eficiente, favorecendo o cálculo da recomendação de acordo com as preferências do indivíduo. / Web systems integrate recommending modules for items, which automatically select content according to the interest of each individual in order to help users in the search for relevant products. Although there are diverse recommending approaches to calculate recommendations according to users preferences, most of them lack information to characterize users preferences and item descriptions. Recent researches on recommender systems have studied the possibility of using users reviews as source of metadata, because users create them collaboratively. However, the literature still lacks studies about how to organize and structure data in a semantic manner. Therefore, this study aims to develop techniques for constructing the representation of items based on collaborative descriptions for recommender systems. For this reason, it is also aimed to analyze the impact caused by distinct methods of word sense disambiguation on the precision of recommendations, which we analyzed in the scenario of ratings predictions. Our results showed that we can characterize users and items in a more efficient way, favoring the calculation of recommendations according to users preferences.

Com o intuito de auxiliar usuários na procura por produtos relevantes, sistemas Web integraram módulos de recomendação de itens, que selecionam automaticamente conteúdo de acordo com os interesses de cada indivíduo. Apesar de existirem diversas abordagens para calcular recomendações de acordo com interações disponíveis no sistema, a maioria delas sofre com a carência de informações utilizadas para caracterizar as preferências dos usuários e as descrições dos itens. Trabalhos recentes sobre sistemas de recomendação têm estudado a possibilidade de utilizar revisões de usuários como fonte de metadados, já que são criadas colaborativamente pelos indivíduos. Entretanto, ainda carecem de estudos sobre como organizar e estruturar os dados de maneira semântica. Desta maneira, este trabalho tem como objetivo desenvolver técnicas de construção de representação de itens baseadas em descrições colaborativas para um sistema de recomendação. Objetiva-se analisar o impacto que métodos distintos de desambiguação lexical de sentido causam na precisão da recomendação, sendo avaliada no cenário de predição de notas. A partir dessa estruturação, é possível caracterizar os itens e usuários de maneira mais eficiente, favorecendo o cálculo da recomendação de acordo com as preferências do indivíduo. / Web systems integrate recommending modules for items, which automatically select content according to the interest of each individual in order to help users in the search for relevant products. Although there are diverse recommending approaches to calculate recommendations according to users preferences, most of them lack information to characterize users preferences and item descriptions. Recent researches on recommender systems have studied the possibility of using users reviews as source of metadata, because users create them collaboratively. However, the literature still lacks studies about how to organize and structure data in a semantic manner. Therefore, this study aims to develop techniques for constructing the representation of items based on collaborative descriptions for recommender systems. For this reason, it is also aimed to analyze the impact caused by distinct methods of word sense disambiguation on the precision of recommendations, which we analyzed in the scenario of ratings predictions. Our results showed that we can characterize users and items in a more efficient way, favoring the calculation of recommendations according to users preferences.

Thesis (PhD (Industrial Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The crucial role of innovation in creating sustainable competitive advantage is widely recognised in industry today. Likewise, the importance of having the required information accessible to the right employees at the right time is well-appreciated. More specifically, the dependency of effective, efficient innovation processes on the availability of information has been pointed out in literature. A great challenge is countering the effects of the information overload phenomenon in organisations in order for employees to find the information appropriate to their needs without having to wade through excessively large quantities of information to do so. The initial stages of the innovation process, which are characterised by free association, semi-formal activities, conceptualisation, and experimentation, have already been identified as a key focus area for improving the effectiveness of the entire innovation process. The dependency on information during these early stages of the innovation process is especially high. Any organisation requires a strategy for innovation, a number of well-defined, implemented processes and measures to be able to innovate in an effective and efficient manner and to drive its innovation endeavours. In addition, the organisation requires certain enablers to support its innovation efforts which include certain core competencies, technologies and knowledge. Most importantly for this research, enablers are required to more effectively manage and utilise innovation-related information. Information residing inside and outside the boundaries of the organisation is required to feed the innovation process. The specific sources of such information are numerous. Such information may further be structured or unstructured in nature. However, an ever-increasing ratio of available innovation-related information is of the unstructured type. Examples include the textual content of reports, books, e-mail messages and web pages. This research explores the innovation landscape and typical sources of innovation-related information. In addition, it explores the landscape of text analytical approaches and techniques in search of ways to more effectively and efficiently deal with unstructured, textual information. A framework that can be used to provide a unified, dynamic view of an organisation‟s innovation-related information, both structured and unstructured, is presented. Once implemented, this framework will constitute an innovation-focused knowledge base that will organise and make accessible such innovation-related information to the stakeholders of the innovation process. Two novel, complementary text analytical techniques, Latent Dirichlet Allocation and the Concept-Topic Model, were identified for application with the framework. The potential value of these techniques as part of the information systems that would embody the framework is illustrated. The resulting knowledge base would cause a quantum leap in the accessibility of information and may significantly improve the way innovation is done and managed in the target organisation. / AFRIKAANSE OPSOMMING: Die belangrikheid van innovasie vir die daarstel van „n volhoubare mededingende voordeel word tans wyd erken in baie sektore van die bedryf. Ook die belangrikheid van die toeganklikmaking van relevante inligting aan werknemers op die geskikte tyd, word vandag terdeë besef. Die afhanklikheid van effektiewe, doeltreffende innovasieprosesse op die beskikbaarheid van inligting word deurlopend beklemtoon in die navorsingsliteratuur. „n Groot uitdaging tans is om die oorsake en impak van die inligtingsoorvloedverskynsel in ondernemings te bestry ten einde werknemers in staat te stel om inligting te vind wat voldoen aan hul behoeftes sonder om in die proses deur oormatige groot hoeveelhede inligting te sif. Die aanvanklike stappe van die innovasieproses, gekenmerk deur vrye assosiasie, semi-formele aktiwiteite, konseptualisering en eksperimentasie, is reeds geïdentifiseer as sleutelareas vir die verbetering van die effektiwiteit van die innovasieproses in sy geheel. Die afhanklikheid van hierdie deel van die innovasieproses op inligting is besonder hoog. Om op „n doeltreffende en optimale wyse te innoveer, benodig elke onderneming „n strategie vir innovasie sowel as „n aantal goed gedefinieerde, ontplooide prosesse en metingskriteria om die innovasieaktiwiteite van die onderneming te dryf. Bykomend benodig ondernemings sekere innovasie-ondersteuningsmeganismes wat bepaalde sleutelaanlegde, -tegnologiëe en kennis insluit. Kern tot hierdie navorsing, benodig organisasies ook ondersteuningsmeganismes om hul in staat te stel om meer doeltreffend innovasie-verwante inligting te bestuur en te gebruik. Inligting, gehuisves beide binne en buite die grense van die onderneming, word benodig om die innovasieproses te voer. Die bronne van sulke inligting is veeltallig en hierdie inligting mag gestruktureerd of ongestruktureerd van aard wees. „n Toenemende persentasie van innovasieverwante inligting is egter van die ongestruktureerde tipe, byvoorbeeld die inligting vervat in die tekstuele inhoud van verslae, boeke, e-posboodskappe en webbladsye. In hierdie navorsing word die innovasielandskap asook tipiese bronne van innovasie-verwante inligting verken. Verder word die landskap van teksanalitiese benaderings en -tegnieke ondersoek ten einde maniere te vind om meer doeltreffend en optimaal met ongestruktureerde, tekstuele inligting om te gaan. „n Raamwerk wat aangewend kan word om „n verenigde, dinamiese voorstelling van „n onderneming se innovasieverwante inligting, beide gestruktureerd en ongestruktureerd, te skep word voorgestel. Na afloop van implementasie sal hierdie raamwerk die innovasieverwante inligting van die onderneming organiseer en meer toeganklik maak vir die deelnemers van die innovasieproses. Daar word verslag gelewer oor die aanwending van twee nuwerwetse, komplementêre teksanalitiese tegnieke tot aanvulling van die raamwerk. Voorts word die potensiele waarde van hierdie tegnieke as deel van die inligtingstelsels wat die raamwerk realiseer, verder uitgewys en geillustreer.

Sistemas de recomendação surgiram da necessidade de selecionar e apresentar conteúdo relevante a usuários de acordo com suas preferências. Dentre os diversos métodos existentes, aqueles baseados em conteúdo faz em uso exclusivo da informação inerente aos itens. Estas informações podem ser criadas a partir de técnicas de indexação automática e manual. Enquanto que as abordagens automáticas necessitam de maiores recursos computacionais e são limitadas á tarefa específica que desempenham, os métodos manuais são caros e propensos a erros. Por outro lado, com a expansão da Web e a possibilidade de usuários comuns criarem novos conteúdos e anotações sobre diferentes itens e produtos, uma alternativa é obter esses metadados criados colaborativamente pelos próprios usuários. Entretanto, essas informações, em especial revisões e comentários, podem conter ruídos, além de estarem em uma forma desestruturada. Deste modo, este trabalho1 tem como objetivo desenvolver métodos de construção de representações de itens baseados em descrições colaborativas para um sistema de recomendação. Objetiva-se analisar o impacto que diferentes técnicas de extração de características, aliadas à análise de sentimento, causam na precisão da geração de sugestões, avaliando-se os resultados em dois cenários de recomendação: predição de notas e geração de ranques. Dentre as técnicas analisadas, observa-se que a melhor apresenta um ganho no poder descritivo dos itens, ocasionando uma melhora no sistema de recomendação. / Recommender systems arose from the need to select and present relevant content to users according to their preferences. Among several existent methods, those based on content make exclusive use of information inherent to the items. This information can be created through automatic and manual indexing techniques. While automa-tic approaches require greater computing resources and are limited to the specific task they perform, manual methods are expensive and prone to errors. On the other hand, with the expansion of theWeb and the possibility of common users to create new content and descriptions about different items and products, an alternative is to get these metadata created collaboratively by the users. However, this information, especially reviews and comments, may contain noise, be- sides being in a unstructured fashion. Thus, this study aims to develop methods for the construction of items representations based on collaborative descriptions for a recommender system. This study aims to analyze the impact that different feature extraction techniques, combined with sentiment analysis, caused in the accuracy of the generated suggestions, evaluating the results in both recommendations cenarios: rating prediction and ranking generation. Among the analyzed techniques, it is observed that the best is able to describe items in a more effcient manner, resulting in an improvement in the recommendation system.

Sistemas de recomendação surgiram da necessidade de selecionar e apresentar conteúdo relevante a usuários de acordo com suas preferências. Dentre os diversos métodos existentes, aqueles baseados em conteúdo faz em uso exclusivo da informação inerente aos itens. Estas informações podem ser criadas a partir de técnicas de indexação automática e manual. Enquanto que as abordagens automáticas necessitam de maiores recursos computacionais e são limitadas á tarefa específica que desempenham, os métodos manuais são caros e propensos a erros. Por outro lado, com a expansão da Web e a possibilidade de usuários comuns criarem novos conteúdos e anotações sobre diferentes itens e produtos, uma alternativa é obter esses metadados criados colaborativamente pelos próprios usuários. Entretanto, essas informações, em especial revisões e comentários, podem conter ruídos, além de estarem em uma forma desestruturada. Deste modo, este trabalho1 tem como objetivo desenvolver métodos de construção de representações de itens baseados em descrições colaborativas para um sistema de recomendação. Objetiva-se analisar o impacto que diferentes técnicas de extração de características, aliadas à análise de sentimento, causam na precisão da geração de sugestões, avaliando-se os resultados em dois cenários de recomendação: predição de notas e geração de ranques. Dentre as técnicas analisadas, observa-se que a melhor apresenta um ganho no poder descritivo dos itens, ocasionando uma melhora no sistema de recomendação. / Recommender systems arose from the need to select and present relevant content to users according to their preferences. Among several existent methods, those based on content make exclusive use of information inherent to the items. This information can be created through automatic and manual indexing techniques. While automa-tic approaches require greater computing resources and are limited to the specific task they perform, manual methods are expensive and prone to errors. On the other hand, with the expansion of theWeb and the possibility of common users to create new content and descriptions about different items and products, an alternative is to get these metadata created collaboratively by the users. However, this information, especially reviews and comments, may contain noise, be- sides being in a unstructured fashion. Thus, this study aims to develop methods for the construction of items representations based on collaborative descriptions for a recommender system. This study aims to analyze the impact that different feature extraction techniques, combined with sentiment analysis, caused in the accuracy of the generated suggestions, evaluating the results in both recommendations cenarios: rating prediction and ranking generation. Among the analyzed techniques, it is observed that the best is able to describe items in a more effcient manner, resulting in an improvement in the recommendation system.

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