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- item-1 hasContent rafael-s-goncalves.
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- item-2 hasContent bijan-parsia.
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- item-3 hasContent ulrike-sattler.
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- paper-07 type InProceedings.
- paper-07 label "Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools".
- paper-07 sameAs paper-07.
- paper-07 abstract "Most of the semantic content available has been generated automatically by using annotation services for existing content. Automatic annotation is not of sufficient quality to enable focused search and retrieval: either too many or too few terms are semantically annotated. User-defined semantic enrichment allows for a more targeted approach. We developed a tool for semantic annotation of digital documents and conducted an end-user study to evaluate its acceptance by and usability for non-expert users. This paper presents the results of this user study and discusses the lessons learned about both the semantic enrichment process and our methodology of exposing non-experts to semantic enrichment.".
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- paper-07 title "Semantic Enrichment by Non-Experts: Usability of Manual Annotation Tools".
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- item-1 hasContent annika-hinze.
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- item-2 hasContent ralf-heese.
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- item-3 hasContent markus-luczak-roesch.
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- item-4 hasContent adrian-paschke.
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- paper-08 type InProceedings.
- paper-08 label "Personalised Graph-based Selection of Web APIs".
- paper-08 sameAs paper-08.
- paper-08 abstract "Modelling and understanding various contexts of users is important to enable personalised selection of Web APIs in directories such as Programmable Web. Currently, relationships between users and Web APIs are not clearly understood and utilized by existing selection approaches. In this paper, we present a semantic model of a Web API directory graph that captures relationships such as Web APIs, mashups, developers, and categories. We describe a novel configurable graph-based method for selection of Web APIs with personalised and temporal aspects. The method allows users to get more control over their preferences and recommended Web APIs while they can exploit information about their social links and preferences. We evaluate the method on a real-world dataset from ProgrammableWeb.com, and show that it provides more contextualised results than currently available popularity based rankings.".
- paper-08 hasAuthorList authorList.
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- paper-08 title "Personalised Graph-based Selection of Web APIs".
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- item-1 hasContent milan-dojchinovski.
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- item-2 hasContent jaroslav-kuchar.
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- item-3 hasContent tomas-vitvar.
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- item-4 hasContent maciej-zaremba.
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- paper-09 type InProceedings.
- paper-09 label "Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy".
- paper-09 sameAs paper-09.
- paper-09 abstract "Tracking user interests over time is important for making accurate recommendations. However, the widely-used time-decay-based approach worsens the sparsity problem because it deemphasizes old item transactions. We introduce two ideas to solve the sparsity problem. First, we divide the users' transactions into epochs i.e. time periods, and identify epochs that are dominated by interests similar to the current interests of the active user. Thus, it can eliminate dissimilar transactions while making use of similar transactions that exist in prior epochs. Second, we use a taxonomy of items to model user item transactions in each epoch. This well captures the interests of users in each epoch even if there are few transactions. It suits the situations in which the items transacted by users dynamically change over time; the semantics behind classes do not change so often while individual items often appear and disappear. Fortunately, many taxonomies are now available on the web because of the spread of the Linked Open Data vision. We can now use those to understand dynamic user interests semantically. We evaluate our method using a dataset, a music listening history, extracted from users' tweets and one containing a restaurant visit history gathered from a gourmet guide site. The results show that our method predicts user interests much more accurately than the previous time-decay-based method.".
- paper-09 hasAuthorList authorList.
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- paper-09 title "Collaborative Filtering by Analyzing Dynamic User Interests Modeled by Taxonomy".
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- item-1 hasContent makoto-nakatsuji.
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- item-2 hasContent yasuhiro-fujiwara.
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- item-3 hasContent toshio-uchiyama.
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- item-4 hasContent hiroyuki-toda.
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- paper-10 type InProceedings.
- paper-10 label "Efficient Execution of Top-K SPARQL Queries".
- paper-10 sameAs paper-10.
- paper-10 abstract "Top-k queries, i.e. queries returning the top k results ordered by a user-defined scoring function, are an important category of queries. Order is an important property of data that can be exploited to speed up query processing. State-of-the-art SPARQL engines underuse order, and top-k queries are mostly managed with a materialize-then-sort processing scheme that computes all the matching solutions (e.g. thousands) even if only a limited number k (e.g. ten) are requested. The SPARQL-RANK algebra is an extended SPARQL algebra that treats order as a first class citizen, enabling efficient split-and-interleave processing schemes that can be adopted to improve the performance of top-k SPARQL queries. In this paper we propose an incremental execution model for SPARQL-RANK queries, we compare the performance of alternative physical operators, and we propose a rank-aware join algorithm optimized for native RDF stores. Experiments conducted with an open source implementation of a SPARQL-RANK query engine based on ARQ show that the evaluation of top-k queries can be sped up by orders of magnitude.".
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