FROM USER-GENERATED-CONTENT TO STRUCTURED KNOWLEDGE EXPLORING MULTI-ASPECT SENTENCE REPRESENTATION AND PROTOTYPE HIERARCHY BASED CATEGORIZATION FOR ORGANIZATION OF TEXT COLLECTIONS

Ph.DDOCTOR OF PHILOSOPH

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Hierarchical categorisation of tags for delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerning the unavailability of data to reliably, accurately measure interests across such fine-grained categorisation, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at Delicious #http://delicious.com# are classified into 200 subcategories of interest.Preprin

Hierarchical categorisation of web tags for Delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. The benefits of social tagging are clear – tags enhance Web content browsing and search. However, since these tags may be publicly available to any Internet user, a privacy attacker may collect this information and extract an accurate snapshot of users’ interests or user profiles, containing sensitive information, such as health-related information, political preferences, salary or religion. In order to hinder attackers in their efforts to profile users, this report focuses on the practical aspects of capturing user interests from their tagging activity. More accurately, we study how to categorise a collection of tags posted by users in one of the most popular bookmarking services, Delicious (http://delicious.com).Preprin

Formal concept matching and reinforcement learning in adaptive information retrieval

The superiority of the human brain in information retrieval (IR) tasks seems to come firstly from its ability to read and understand the concepts, ideas or meanings central to documents, in order to reason out the usefulness of documents to information needs, and secondly from its ability to learn from experience and be adaptive to the environment. In this work we attempt to incorporate these properties into the development of an IR model to improve document retrieval. We investigate the applicability of concept lattices, which are based on the theory of Formal Concept Analysis (FCA), to the representation of documents. This allows the use of more elegant representation units, as opposed to keywords, in order to better capture concepts/ideas expressed in natural language text. We also investigate the use of a reinforcement leaming strategy to learn and improve document representations, based on the information present in query statements and user relevance feedback. Features or concepts of each document/query, formulated using FCA, are weighted separately with respect to the documents they are in, and organised into separate concept lattices according to a subsumption relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the concepts in the lattice representation. This avoids implementation drawbacks faced by other FCA-based approaches. Retrieval of a document for an information need is based on concept matching between concept lattice representations of a document and a query. The learning strategy works by making the similarity of relevant documents stronger and non-relevant documents weaker for each query, depending on the relevance judgements of the users on retrieved documents. Our approach is radically different to existing FCA-based approaches in the following respects: concept formulation; weight assignment to object-attribute pairs; the representation of each document in a separate concept lattice; and encoding concept lattices in BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our learning strategy makes use of relevance feedback information to enhance document representations, thus making the document representations dynamic and adaptive to the user interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are presented and compared with published results. In particular, the performance of the system is shown to improve significantly as the system learns from experience.The School of Computing, University of Plymouth, UK

Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns. The Development and Evaluation of New Web Mining Methods that enhance Information Retrieval and improve the Understanding of User¿s Web Behavior in Websites and Social Blogs.

The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a raw environment for information consumption to a dynamic and rich platform for information production and sharing worldwide. However, this growth and transformation of the Web has resulted in an uncontrollable explosion and abundance of the textual contents, creating a serious challenge for any user to find and retrieve the relevant information that he truly seeks to find on the Web. The process of finding a relevant Web page in a website easily and efficiently has become very difficult to achieve. This has created many challenges for researchers to develop new mining techniques in order to improve the user experience on the Web, as well as for organizations to understand the true informational interests and needs of their customers in order to improve their targeted services accordingly by providing the products, services and information that truly match the requirements of every online customer. With these challenges in mind, Web mining aims to extract hidden patterns and discover useful knowledge from Web page contents, Web hyperlinks, and Web usage logs. Based on the primary kinds of Web data used in the mining process, Web mining tasks can be categorized into three main types: Web content mining, which extracts knowledge from Web page contents using text mining techniques, Web structure mining, which extracts patterns from the hyperlinks that represent the structure of the website, and Web usage mining, which mines user's Web navigational patterns from Web server logs that record the Web page access made by every user, representing the interactional activities between the users and the Web pages in a website. The main goal of this thesis is to contribute toward addressing the challenges that have been resulted from the information explosion and overload on the Web, by proposing and developing novel Web mining-based approaches. Toward achieving this goal, the thesis presents, analyzes, and evaluates three major contributions. First, the development of an integrated Web structure and usage mining approach that recommends a collection of hyperlinks for the surfers of a website to be placed at the homepage of that website. Second, the development of an integrated Web content and usage mining approach to improve the understanding of the user's Web behavior and discover the user group interests in a website. Third, the development of a supervised classification model based on recent Social Web concepts, such as Tag Clouds, in order to improve the retrieval of relevant articles and posts from Web social blogs

Benefits of the application of web-mining methods and techniques for the field of analytical customer relationship management of the marketing function in a knowledge management perspective

Le Web Mining (WM) reste une technologie relativement méconnue. Toutefois, si elle est utilisée adéquatement, elle s'avère être d'une grande utilité pour l'identification des profils et des comportements des clients prospects et existants, dans un contexte internet. Les avancées techniques du WM améliorent grandement le volet analytique de la Gestion de la Relation Client (GRC). Cette étude suit une approche exploratoire afin de déterminer si le WM atteint, à lui seul, tous les objectifs fondamentaux de la GRC, ou le cas échéant, devrait être utilisé de manière conjointe avec la recherche marketing traditionnelle et les méthodes classiques de la GRC analytique (GRCa) pour optimiser la GRC, et de fait le marketing, dans un contexte internet. La connaissance obtenue par le WM peut ensuite être administrée au sein de l'organisation dans un cadre de Gestion de la Connaissance (GC), afin d'optimiser les relations avec les clients nouveaux et/ou existants, améliorer leur expérience client et ultimement, leur fournir de la meilleure valeur. Dans un cadre de recherche exploratoire, des entrevues semi-structurés et en profondeur furent menées afin d'obtenir le point de vue de plusieurs experts en (web) data rnining. L'étude révéla que le WM est bien approprié pour segmenter les clients prospects et existants, pour comprendre les comportements transactionnels en ligne des clients existants et prospects, ainsi que pour déterminer le statut de loyauté (ou de défection) des clients existants. Il constitue, à ce titre, un outil d'une redoutable efficacité prédictive par le biais de la classification et de l'estimation, mais aussi descriptive par le biais de la segmentation et de l'association. En revanche, le WM est moins performant dans la compréhension des dimensions sous-jacentes, moins évidentes du comportement client. L'utilisation du WM est moins appropriée pour remplir des objectifs liés à la description de la manière dont les clients existants ou prospects développent loyauté, satisfaction, défection ou attachement envers une enseigne sur internet. Cet exercice est d'autant plus difficile que la communication multicanale dans laquelle évoluent les consommateurs a une forte influence sur les relations qu'ils développent avec une marque. Ainsi le comportement en ligne ne serait qu'une transposition ou tout du moins une extension du comportement du consommateur lorsqu'il n'est pas en ligne. Le WM est également un outil relativement incomplet pour identifier le développement de la défection vers et depuis les concurrents ainsi que le développement de la loyauté envers ces derniers. Le WM nécessite toujours d'être complété par la recherche marketing traditionnelle afin d'atteindre ces objectives plus difficiles mais essentiels de la GRCa. Finalement, les conclusions de cette recherche sont principalement dirigées à l'encontre des firmes et des gestionnaires plus que du côté des clients-internautes, car ces premiers plus que ces derniers possèdent les ressources et les processus pour mettre en œuvre les projets de recherche en WM décrits.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Web mining, Gestion de la connaissance, Gestion de la relation client, Données internet, Comportement du consommateur, Forage de données, Connaissance du consommateu

Collaborative Knowledge Visualisation for Cross-Community Knowledge Exchange

The notion of communities as informal social networks based on shared interests or common practices has been increasingly used as an important unit of analysis when considering the processes of cooperative creation and sharing of knowledge. While knowledge exchange within communities has been extensively researched, different studies observed the importance of cross-community knowledge exchange for the creation of new knowledge and innovation in knowledge-intensive organizations. Especially in knowledge management a critical problem has become the need to support the cooperation and exchange of knowledge between different communities with highly specialized expertise and activities. Though several studies discuss the importance and difficulties of knowledge sharing across community boundaries, the development of technological support incorporating these findings has been little addressed. This work presents an approach to supporting cross-community knowledge exchange based on using knowledge visualisation for facilitating information access in unfamiliar community domains. The theoretical grounding and practical relevance of the proposed approach are ensured by defining a requirements model that integrates theoretical frameworks for cross-community knowledge exchange with practical needs of typical knowledge management processes and sensemaking tasks in information access in unfamiliar domains. This synthesis suggests that visualising knowledge structures of communities and supporting the discovery of relationships between them during access to community spaces, could provide valuable support for cross-community discovery and sharing of knowledge. This is the main hypothesis investigated in this thesis. Accordingly, a novel method is developed for eliciting and visualising implicit knowledge structures of individuals and communities in form of dynamic knowledge maps that make the elicited knowledge usable for semantic exploration and navigation of community spaces. The method allows unobtrusive construction of personal and community knowledge maps based on user interaction with information and their use for dynamic classification of information from a specific point of view. The visualisation model combines Document Maps presenting main topics, document clusters and relationships between knowledge reflected in community spaces with Concept Maps visualising personal and shared conceptual structures of community members. The technical realization integrates Kohonen’s self-organizing maps with extraction of word categories from texts, collaborative indexing and personalised classification based on user-induced templates. This is accompanied by intuitive visualisation and interaction with complex information spaces based on multi-view navigation of document landscapes and concept networks. The developed method is prototypically implemented in form of an application framework, a concrete system and a visual information interface for multi-perspective access to community information spaces, the Knowledge Explorer. The application framework implements services for generating and using personal and community knowledge maps to support explicit and implicit knowledge exchange between members of different communities. The Knowledge Explorer allows simultaneous visualisation of different personal and community knowledge structures and enables their use for structuring, exploring and navigating community information spaces from different points of view. The empirical evaluation in a comparative laboratory study confirms the adequacy of the developed solutions with respect to specific requirements of the cross-community problem and demonstrates much better quality of knowledge access compared to a standard information seeking reference system. The developed evaluation framework and operative measures for quality of knowledge access in cross-community contexts also provide a theoretically grounded and practically feasible method for further developing and evaluating new solutions addressing this important but little investigated problem

Open Source Workflow Engine for Cheminformatics: From Data Curation to Data Analysis

The recent release of large open access chemistry databases into the public domain generates a demand for flexible tools to process them so as to discover new knowledge. To support Open Drug Discovery and Open Notebook Science on top of these data resources, is it desirable for the processing tools to be Open Source and available to everyone. The aim of this project was the development of an Open Source workflow engine to solve crucial cheminformatics problems. As a consequence, the CDK-Taverna project developed in the course of this thesis builds a cheminformatics workflow solution through the combination of different Open Source projects such as Taverna (workflow engine), the Chemistry Development Kit (CDK, cheminformatics library) and Pgchem::Tigress (chemistry database cartridge). The work on this project includes the implementation of over 160 different workers, which focus on cheminformatics tasks. The application of the developed methods to real world problems was the final objective of the project. The validation of Open Source software libraries and of chemical data derived from different databases is mandatory to all cheminformatics workflows. Methods to detect the atom types of chemical structures were used to validate the atom typing of the Chemistry Development Kit and to identify curation problems while processing different public databases, including the EBI drug databases ChEBI and ChEMBL as well as the natural products Chapman & Hall Chemical Database. The CDK atom typing shows a lack on atom types of heavier atoms but fits the need of databases containing organic substances including natural products. To support combinatorial chemistry an implementation of a reaction enumeration workflow was realized. It is based on generic reactions with lists of reactants and allows the generation of chemical libraries up to O(1000) molecules. Supervised machine learning techniques (perceptron-type artificial neural networks and support vector machines) were used as a proof of concept for quantitative modelling of adhesive polymer kinetics with the Mathematica GNWI.CIP package. This opens the perspective of an integration of high-level "experimental mathematics" into the CDK-Taverna based scientific pipelining. A chemical diversity analysis based on two different public and one proprietary databases including over 200,000 molecules was a large-scale application of the methods developed. For the chemical diversity analysis different molecular properties are calculated using the Chemistry Development Kit. The analysis of these properties was performed with Adaptive-Resonance-Theory (ART 2-A algorithm) for an automatic unsupervised classification of open categorical problems. The result shows a similar coverage of the chemical space of the two databases containing natural products (one public, one proprietary) whereas the ChEBI database covers a distinctly different chemical space. As a consequence these comparisons reveal interesting white-spots in the proprietary database. The combination of these results with pharmacological annotations of the molecules leads to further research and modelling activities