532 research outputs found
Applying Science Models for Search
The paper proposes three different kinds of science models as value-added
services that are integrated in the retrieval process to enhance retrieval
quality. The paper discusses the approaches Search Term Recommendation,
Bradfordizing and Author Centrality on a general level and addresses
implementation issues of the models within a real-life retrieval environment.Comment: 14 pages, 3 figures, ISI 201
Finding Your Literature Match -- A Recommender System
The universe of potentially interesting, searchable literature is expanding
continuously. Besides the normal expansion, there is an additional influx of
literature because of interdisciplinary boundaries becoming more and more
diffuse. Hence, the need for accurate, efficient and intelligent search tools
is bigger than ever. Even with a sophisticated search engine, looking for
information can still result in overwhelming results. An overload of
information has the intrinsic danger of scaring visitors away, and any
organization, for-profit or not-for-profit, in the business of providing
scholarly information wants to capture and keep the attention of its target
audience. Publishers and search engine engineers alike will benefit from a
service that is able to provide visitors with recommendations that closely meet
their interests. Providing visitors with special deals, new options and
highlights may be interesting to a certain degree, but what makes more sense
(especially from a commercial point of view) than to let visitors do most of
the work by the mere action of making choices? Hiring psychics is not an
option, so a technological solution is needed to recommend items that a visitor
is likely to be looking for. In this presentation we will introduce such a
solution and argue that it is practically feasible to incorporate this approach
into a useful addition to any information retrieval system with enough usage.Comment: Contribution to the proceedings of the colloquium Future Professional
Communication in Astronomy II, 13-14 April 2010, Cambridge, Massachusetts. 11
pages, 4 figures
Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance
Increasingly, organizations are adopting ontologies to describe their large catalogues of items. These ontologies need to evolve regularly in response to changes in the domain and the emergence of new requirements. An important step of this process is the selection of candidate concepts to include in the new version of the ontology. This operation needs to take into account a variety of factors and in particular reconcile user requirements and application performance. Current ontology evolution methods focus either on ranking concepts according to their relevance or on preserving compatibility with existing applications. However, they do not take in consideration the impact of the ontology evolution process on the performance of computational tasks – e.g., in this work we focus on instance tagging, similarity computation, generation of recommendations, and data clustering. In this paper, we propose the Pragmatic Ontology Evolution (POE) framework, a novel approach for selecting from a group of candidates a set of concepts able to produce a new version of a given ontology that i) is consistent with the a set of user requirements (e.g., max number of concepts in the ontology), ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. Our approach also supports users in navigating the space of possible solutions by showing how certain choices, such as limiting the number of concepts or privileging trendy concepts rather than historical ones, would reflect on the application performance. An evaluation of POE on the real-world scenario of the evolving Springer Nature taxonomy for editorial classification yielded excellent results, demonstrating a significant improvement over alternative approaches
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