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

Knowledge Base Enrichment by Relation Learning from Social Tagging Data

There has been considerable interest in transforming unstructured social tagging data into structured knowledge for semantic-based retrieval and recommendation. Research in this line mostly exploits data co-occurrence and often overlooks the complex and ambiguous meanings of tags. Furthermore, there have been few comprehensive evaluation studies regarding the quality of the discovered knowledge. We propose a supervised learning method to discover subsumption relations from tags. The key to this method is quantifying the probabilistic association among tags to better characterise their relations. We further develop an algorithm to organise tags into hierarchies based on the learned relations. Experiments were conducted using a large, publicly available dataset, Bibsonomy, and three popular, human-engineered or data-driven knowledge bases: DBpedia, Microsoft Concept Graph, and ACM Computing Classification System. We performed a comprehensive evaluation using different strategies: relation-level, ontology-level, and knowledge base enrichment based evaluation. The results clearly show that the proposed method can extract knowledge of better quality than the existing methods against the gold standard knowledge bases. The proposed approach can also enrich knowledge bases with new subsumption relations, having the potential to significantly reduce time and human effort for knowledge base maintenance and ontology evolution

Conectando dados biológicos : dos fenótipos às árvores filogenéticas

Orientador: André SantanchèDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Um grande número de estudos em biologia, incluindo os que envolvem a reconstrução de árvores filogenéticas, resultam na produção de uma enorme quantidade de dados -- por exemplo, descrições fenotípicas , matrizes de dados morfológicos , árvores filogenéticas, etc. Biólogos enfrentam cada vez mais o desafio e a oportunidade de efetivamente descobrir conhecimento a partir do cruzamento e comparação de vários conjuntos de dados, nem sempre conectados e integrados. Neste trabalho, estamos interessados em um contexto específico da biologia em que biólogos aplicam ferramentas computacionais para construir e compartilhar descrições digitais dos seres vivos. Nós propomos um processo que parte de fontes de dados fragmentadas, que nós mapeamos para grafos, em direção a uma plena integração das descrições através de ontologias. Os bancos de dados de grafos intermediam o processo de evolução. Eles são menos dependentes de esquema e, uma vez que ontologias também são grafos, o processo de mapeamento do grafo inicial para uma ontologia torna-se uma sequência de transformações no grafo. Nossa motivação parte da ideia de que a conversão de descrições fenotípicas em uma rede de relações e a busca de conexões entre elementos relacionados irá aumentar a capacidade de resolver problemas mais complexos suportados por computadores. Este trabalho detalha os princípios de concepção por trás do nosso processo e duas implementações práticas como prova de conceitoAbstract: A large number of studies in biology, including those involving phylogenetic trees reconstruction, result in the production of a huge amount of data -- e.g., phenotype descriptions, morphological data matrices, phylogenetic trees, etc. Biologists increasingly face a challenge and opportunity of effectively discovering useful knowledge crossing and comparing several pieces of information, not always linked and integrated. In this work, we are interested in a specific biology context, in which biologists apply computational tools to build and share digital descriptions of living beings. We propose a process that departs from fragmentary data sources, which we map to graphs, towards a full integration of descriptions through ontologies. Graph databases mediate this evolvement process. They are less schema dependent and, since an ontology is also a graph, the mapping process from the initial graph towards an ontology becomes a sequence of graph transformations. Our motivation stems from the idea that transforming phenotypical descriptions in a network of relationships and looking for links among related elements will enhance the ability of solving more complex problems supported by machines. This work details the design principles behind our process and two practical implementations as proof of conceptMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Identificação de critérios para avaliação de ideias: um método utilizando folksonomias

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia e Gestão do Conhecimento, Florianópolis, 2016.As ferramentas de cocriação encontram uma rica fonte de conhecimento baseada nas interações sociais que ocorrem na Web. Essa interação coletiva é a principal característica dos Sistemas de apoio à inovação, em especial para os sistemas de gestão de ideias. Entretanto, para avaliar ideias, as soluções atuais limitam-se a métodos baseados em formulários com critérios pré-estabelecidos ou, então, por métricas de engajamento social. O contexto organizacional é crítico para o sucesso de uma ideia, porém, ao considerar apenas índices de popularidade, as avaliações não agregam semanticamente o conhecimento atribuído pelo usuário, bem como não determinam quais critérios foram ponderados pela comunidade. A fim de compreender este conhecimento coletivo, a presente pesquisa propõe um método de identificação e análise de critérios para a avaliação de ideias. O desenvolvimento desse artefato é baseado na metodologia da ciência do design e explora o conhecimento a partir de atribuições sociais por notas e tags, as folksonomias. Assim, no contexto do front end da Inovação, o método representa uma apropriação semântica e qualitativa dos critérios atribuídos pela comunidade. A verificação utiliza técnicas da mineração de folksonomias em uma base de dados representada por um modelo de hipergrafo. Como resultado, o método permite evidenciar um conjunto de características a serem consideradas pela organização como critérios de avaliação. Além disso, a solução constata que a popularidade não é uma medida de consenso da comunidade, portanto sub comunidades auferem medidas mais precisas em suas atribuições; e a flexibilização temporal, própria das interações sociais, colaboram na recomendação de ideias baseada em tendências e no contexto organizacional.Abstract : Co-creation tools meet a rich source of knowledge on social interactions that occurs on the Web. This collective interaction is the main characteristic of innovation support systems, especially idea management systems. However, in order to evaluate ideas, current solutions are limited to methods based on forms with pre-established criteria or metrics of social engagement. The organizational context is critical to the success of an idea. Nevertheless, when considering just popularity ratings, the evaluations do not semantically aggregate the knowledge attributed by the user. It also does not determine what criteria was weighted by the community. In order to understand this collective knowledge, the present research proposes a method for identification and analysis of criteria in idea evaluation. The development of this artefact is based on the design science research methodology, and it explores the knowledge from social attributions using grades and tags, also known as folksonomy. Therefore, within the front end of innovation, the method represents a semantic, qualitative appropriation of criteria attributed by the community. The artefact was verified using folksonomy mining techniques in a database represented by a hypergraph model. As a result, the method allows to visualize a set of characteristics to be considered as evaluation criteria by any organization. In addition, the results showed that popularity is not a community s consensus measure. Therefore, sub communities get more precise measurements in their attributes; and temporal flexibility, which is specific to social interactions, collaborate on the idea recommendation based on trends and organizational context

Folksonomized ontology and the 3E steps technique to support ontology evolvement

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Folksonomies are increasingly adopted in web systems. These "social taxonomies", which emerge from collaborative tagging, contrast with the formalism and the systematic creation process applied to ontologies. However, they can play complementary roles, as the knowledge systematically formalized in ontologies by a restricted group can be enriched by the implicit knowledge collaboratively produced by a much wider group. Existing initiatives that involve folksonomies and ontologies are often unidirectional, i.e., ontologies improve tag operations or tags are used to automatically create ontologies. We propose a new fusion approach in which the semantics travels in both directions - from folksonomies to ontologies and vice versa. The result of this fusion is our Folksonomized Ontology (FO). In this paper, we present our 3E steps technique - Extraction, Enrichment, and Evolution - which explores the latent semantics of a given folksonomy - expressed in a FO - to support ontology review and enhancement. It was implemented and tested in a visual review/enhancement tool. (c) 2012 Elsevier B.V. All rights reserved.1811930Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Unicamp/PAPDICConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)CNPq [CNPq 557.128/2009-9

Learning and Leveraging Structured Knowledge from User-Generated Social Media Data

Knowledge has long been a crucial element in Artificial Intelligence (AI), which can be traced back to knowledge-based systems, or expert systems, in the 1960s. Knowledge provides contexts to facilitate machine understanding and improves the explainability and performance of many semantic-based applications. The acquisition of knowledge is, however, a complex step, normally requiring much effort and time from domain experts. In machine learning as one key domain of AI, the learning and leveraging of structured knowledge, such as ontologies and knowledge graphs, have become popular in recent years with the advent of massive user-generated social media data. The main hypothesis in this thesis is therefore that a substantial amount of useful knowledge can be derived from user-generated social media data. A popular, common type of social media data is social tagging data, accumulated from users' tagging in social media platforms. Social tagging data exhibit unstructured characteristics, including noisiness, flatness, sparsity, incompleteness, which prevent their efficient knowledge discovery and usage. The aim of this thesis is thus to learn useful structured knowledge from social media data regarding these unstructured characteristics. Several research questions have then been formulated related to the hypothesis and the research challenges. A knowledge-centred view has been considered throughout this thesis: knowledge bridges the gap between massive user-generated data to semantic-based applications. The study first reviews concepts related to structured knowledge, then focuses on two main parts, learning structured knowledge and leveraging structured knowledge from social tagging data. To learn structured knowledge, a machine learning system is proposed to predict subsumption relations from social tags. The main idea is to learn to predict accurate relations with features, generated with probabilistic topic modelling and founded on a formal set of assumptions on deriving subsumption relations. Tag concept hierarchies can then be organised to enrich existing Knowledge Bases (KBs), such as DBpedia and ACM Computing Classification Systems. The study presents relation-level evaluation, ontology-level evaluation, and the novel, Knowledge Base Enrichment based evaluation, and shows that the proposed approach can generate high quality and meaningful hierarchies to enrich existing KBs. To leverage structured knowledge of tags, the research focuses on the task of automated social annotation and propose a knowledge-enhanced deep learning model. Semantic-based loss regularisation has been proposed to enhance the deep learning model with the similarity and subsumption relations between tags. Besides, a novel, guided attention mechanism, has been proposed to mimic the users' behaviour of reading the title before digesting the content for annotation. The integrated model, Joint Multi-label Attention Network (JMAN), significantly outperformed the state-of-the-art, popular baseline methods, with consistent performance gain of the semantic-based loss regularisers on several deep learning models, on four real-world datasets. With the careful treatment of the unstructured characteristics and with the novel probabilistic and neural network based approaches, useful knowledge can be learned from user-generated social media data and leveraged to support semantic-based applications. This validates the hypothesis of the research and addresses the research questions. Future studies are considered to explore methods to efficiently learn and leverage other various types of structured knowledge and to extend current approaches to other user-generated data