An Empirical Investigation of Collaborative Web Search Tool on Novice\u27s Query Behavior

In the past decade, research efforts dedicated to studying the process of collaborative web search have been on the rise. Yet, a limited number of studies have examined the impact of collaborative information search processes on novices’ query behaviors. Studying and analyzing factors that influence web search behaviors, specifically users’ patterns of queries when using collaborative search systems can help with making query suggestions for group users. Improvements in user query behaviors and system query suggestions help in reducing search time and increasing query success rates for novices. This thesis investigates the influence of collaboration between experts and novices as well as the use of a collaborative web search tool on novices’ query behavior. We used SearchTeam as our collaborative search tool. This empirical study involves four collaborative team conditions: SearchTeam and expert-novice team, SearchTeam and novice-novice team, traditional and expert-novice team, and traditional and novice-novice team. We analyzed participants’ query behavior in two dimensions: quantitatively (e.g. the query success rate), and qualitatively (e.g. the query reformulation patterns). The findings of this study reveal that the successful query rate is higher in expert-novice collaborative teams, who used the collaborative search tools. Participants in expert-novice collaborative teams who used the collaborative search tools, required less time to finalize all tasks compared to expert-novice collaborative teams, who used the traditional search tools. Self-issued queries and chat logs were major sources of terms that novice participants in expert-novice collaborative teams who used the collaborative search tools used. Novices as part of expert-novice pairs who used the collaborative search tools, employed New and Specialization more often as query reformulation patterns. The results of this study contribute to the literature by providing detailed investigation regarding the influence of utilizing collaborative search tool (SearchTeam) in the context of software troubleshooting and development. This study highlights the possible collaborative information seeking (CIS) activities that may occur among software developers’ interns and their mentors. Furthermore, our study reveals that there are specific features, such as awareness and built-in instant messaging (IM), offered by SearchTeam that can promote the CIS activities among participants and help increase novices’ query success rates. Finally, we believe the use of CIS tools, designed to support collaborative search actions in big software development companies, has the potential to improve the overall novices’ query behavior and search strategies

Graph-based reasoning in collaborative knowledge management for industrial maintenance

Capitalization and sharing of lessons learned play an essential role in managing the activities of industrial systems. This is particularly the case for the maintenance management, especially for distributed systems often associated with collaborative decision-making systems. Our contribution focuses on the formalization of the expert knowledge required for maintenance actors that will easily engage support tools to accomplish their missions in collaborative frameworks. To do this, we use the conceptual graphs formalism with their reasoning operations for the comparison and integration of several conceptual graph rules corresponding to different viewpoint of experts. The proposed approach is applied to a case study focusing on the maintenance management of a rotary machinery system

Folksonomized ontologies : an approach to fuse ontologies and folksonomies

Orientador: André SantanchèDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Um número crescente de repositórios na web se baseia em metadados na forma de rótulos (tags) para organizar e classificar o seu conteúdo. Os usuários destes sistemas associam livremente tags a recursos do sistema - e.g., URLs, imagens, marcadores. O termo folksonomia se refere a esta classificação coletiva, que emerge do processo de rotulação (tagging) realizado por usuários interagindo em ambientes sociais na web. Uma das maiores qualidades das folksonomias é a sua simplicidade de uso pela ausência de um vocabulário controlado. Folksonomias crescem de forma orgânica, refletindo o conhecimento da comunidade de usuários. Por outro lado, esta falta de estrutura leva a dificuldades em operações de organização e descoberta de conteúdo. Melhores resultados podem ser obtidos se forem consideradas as relações semânticas entre os rótulos. Por esta razão, vários trabalhos foram propostos com o objetivo de relacionar ontologias e folksonomias, combinando a estrutura sistematizada das ontologias à semântica latente das folksonomias. Enquanto em uma direção algumas abordagens criam "ontologias sociais" a partir dos dados das folksonomias, em outra direção algumas abordagens conectam rótulos a ontologias preexistentes. Em ambos os casos nota-se uma unidirecionalidade, ou seja, um modelo apenas dá suporte ao enriquecimento do outro. Nossa proposta, por outro lado, é bidirecional. Ontologias e folksonomias são fundidas em uma nova entidade, que chamamos de "ontologia folksonomizada", combinando aspectos complementares de ambas. O conhecimento formal e projetado das ontologias é fundido com a semântica latente dos dados sociais. Nesta dissertação apresentamos nossa ontologia folksonomizada e seus desdobramentos. Nós introduzimos um framework formal para a análise de trabalhos relacionados, a fim de confrontá-los com a nossa abordagem. Além das melhorias nas operações de indexação e descoberta, que foram validadas em experimentos práticos, nós propomos uma técnica chamada 3E Steps para dar suporte à evolução de ontologias usando dados de folksonomias. Nós também implementamos o protótipo de uma ferramenta para a construção de ontologias folksonomizadas e para dar suporte à revisão de ontologiasAbstract: An increasing number of web repositories relies on tag-based metadata to organize and classify their content. The users of these systems freely associate tags with resources of the system - e.g., URLs, images, and bookmarks. The term folksonomy refers to this collective classification, which emerges from tagging carried by users interacting in web social environments. One of the major strengths of folksonomies is their simplicity due to the absence of a controlled vocabulary. Folksonomies grow organically, reflecting the knowledge of a community of users. On the other hand, this lack of structure leads to difficulties in operations of content organization and discovery. Better results can be obtained if we take into account the semantic relations among tags. For this reason, many proposals were developed aiming to relate ontologies and folksonomies, combining the systematized structure of ontologies to the latent semantics of folksonomies. While in one direction some approaches build "social ontologies" from folksonomic data, in the other direction some approaches connect tags to existing ontologies. In both cases they are unidirectional approaches, i.e., one model is used only to support the enrichment of the other. Our proposal, on the other hand, is bidirectional. Ontologies and folksonomies are fused in a new entity, we call "folksonomized ontology", which combines complementary aspects of both. The formal and engineered knowledge of ontologies is fused with the latent semantics of social data. In this dissertation we present our folksonomized ontology and its outcomes. We introduce here a formal framework to analyze the related work, confronting it with our approach. Besides the improvements in indexing and discovery operations, which are validated by practical experiments, we propose a 3E Steps technique to support ontology evolvement by using folksonomic data. We also have implemented a tool prototype to build folksonomized ontologies and to support ontology reviewMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Semantic Interaction in Web-based Retrieval Systems : Adopting Semantic Web Technologies and Social Networking Paradigms for Interacting with Semi-structured Web Data

Existing web retrieval models for exploration and interaction with web data do not take into account semantic information, nor do they allow for new forms of interaction by employing meaningful interaction and navigation metaphors in 2D/3D. This thesis researches means for introducing a semantic dimension into the search and exploration process of web content to enable a significantly positive user experience. Therefore, an inherently dynamic view beyond single concepts and models from semantic information processing, information extraction and human-machine interaction is adopted. Essential tasks for semantic interaction such as semantic annotation, semantic mediation and semantic human-computer interaction were identified and elaborated for two general application scenarios in web retrieval: Web-based Question Answering in a knowledge-based dialogue system and semantic exploration of information spaces in 2D/3D

Associative search through formal concept analysis in criminal intelligence analysis

Criminal Intelligence Analysis often requires a search different from the semantic and keyword based searching to reveal the associations among semantically and operationally connected objects within a crime knowledge base. In this paper we introduce associative search as a search along the networks of association between objects like people, places, other organizations, products, events, services, and so on. We also propose an associative search model based on the 5WH associated concepts of a crime, i.e. WHAT (what has happened), WHO (who was involved in the crime), WHEN (the temporal information of the crime), WHERE (the geo-spatial information of the crime) HOW (the modus-operandi used in committing a crime). We have employed Formal Concept Analysis theory to reveal the associations, highlighting Hot Spots, offender‘s profile and its associated offenders in a criminal activit

Knowledge Discovery and Management within Service Centers

These days, most enterprise service centers deploy Knowledge Discovery and Management (KDM) systems to address the challenge of timely delivery of a resourceful service request resolution while efficiently utilizing the huge amount of data. These KDM systems facilitate prompt response to the critical service requests and if possible then try to prevent the service requests getting triggered in the first place. Nevertheless, in most cases, information required for a request resolution is dispersed and suppressed under the mountain of irrelevant information over the Internet in unstructured and heterogeneous formats. These heterogeneous data sources and formats complicate the access to reusable knowledge and increase the response time required to reach a resolution. Moreover, the state-of-the art methods neither support effective integration of domain knowledge with the KDM systems nor promote the assimilation of reusable knowledge or Intellectual Capital (IC). With the goal of providing an improved service request resolution within the shortest possible time, this research proposes an IC Management System. The proposed tool efficiently utilizes domain knowledge in the form of semantic web technology to extract the most valuable information from those raw unstructured data and uses that knowledge to formulate service resolution model as a combination of efficient data search, classification, clustering, and recommendation methods. Our proposed solution also handles the technology categorization of a service request which is very crucial in the request resolution process. The system has been extensively evaluated with several experiments and has been used in a real enterprise customer service center