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    A hybrid method for rating prediction using Linked Data features and text reviews
    (CEUR-WS, 2016) Yumusak S.; Muñoz E.; Minervini P.; Dogdu E.; Kodaz H.
    This paper describes our entry for the Linked Data Mining Challenge 2016, which poses the problem of classifying music albums as 'good' or 'bad' by mining Linked Data. The original labels are assigned according to aggregated critic scores published by the Metacritic website. To this end, the challenge provides datasets that contain the DBpedia reference for music albums. Our approach benefits from Linked Data (LD) and free text to extract meaningful features that help distinguishing between these two classes of music albums. Thus, our features can be summarized as follows: (1) direct object LD features, (2) aggregated count LD features, and (3) textual review features. To build unbiased models, we filtered out those properties somehow related with scores and Metacritic. By using these sets of features, we trained seven models using 10-fold cross-validation to estimate accuracy. We reached the best average accuracy of 87.81% in the training data using a Linear SVM model and all our features, while we reached 90% in the testing data.
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    A short survey of linked data ranking
    (Association for Computing Machinery, Inc, 2014) Yumusak S.; Dogdu E.; Kodaz H.
    Linked data systems are still far from maturity. Hence, the basic principles are still open for discussion. In our study on building a novel linked data search engine, we have surveyed fundamental methods of internet search technologies in the context of linked data crawling, indexing, ranking, and monitoring. The scope of this ranking survey covers linked data related statistical ranking, database ranking, document level ranking, and Web ranking techniques. In order to classify the linked data ranking methods, we identified a number of categories. These categories are ontology ranking, RDF ranking, graph ranking, entity ranking, document/domain ranking. At the end of the survey, we have listed the ranking techniques based on the well-known PageRank algorithm. Copyright 2014 ACM.