5,541 research outputs found

    Ontology-Based Recommendation of Editorial Products

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    Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution

    Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

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    Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

    SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships

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    In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it combines four important factors in scientific publications: content, publicity, impact and randomness. Particularly, a recommendation system (called SciRecSys) has been presented to support users to efficiently find out relevant articles

    Theory and Applications for Advanced Text Mining

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    Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

    Semantic Relevance Analysis of Subject-Predicate-Object (SPO) Triples

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    The goal of this thesis is to explore and integrate several existing measurements for ranking the relevance of a set of subject-predicate-object (SPO) triples to a given concept. As we are inundated with information from multiple sources on the World-Wide-Web, SPO similarity measures play a progressively important role in information extraction, information retrieval, document clustering and ontology learning. This thesis is applied in the Cyber Security Domain for identifying and understanding the factors and elements of sociopolitical events relevant to cyberattacks. Our efforts are towards developing an algorithm that begins with an analysis of news articles by taking into account the semantic information and word order information in the SPOs extracted from the articles. The semantic cohesiveness of a user provided concept and the extracted SPOs will then be calculated using semantic similarity measures derived from 1) structured lexical databases; and 2) our own corpus statistics. The use of a lexical database will enable our method to model human common sense knowledge, while the incorporation of our own corpus statistics allows our method to be adaptable to the Cyber Security domain. The model can be extended to other domains by simply changing the local corpus. The integration of different measures will help us triangulate the ranking of SPOs from multiple dimensions of semantic cohesiveness. Our results are compared to rankings gathered from surveys of human users, where each respondent ranks a list of SPO based on their common knowledge and understanding of the relevance evaluations to a given concept. The comparison demonstrates that our integrated SPO similarity ranking scheme closely reflects the human common sense knowledge in a specific domain it addresses
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