Many ranking methods have been proposed for RDF data. These methods often use the structure behind the data to measure its importance. Recently, some of these methods have started to explore information from other sources such as the Wikipedia page graph for better ranking RDF data. In this work, we propose DBtrends, a ranking function based on query logs. We extensively evaluate the application of di fferent ranking functions for entities, classes, and properties across two di fferent countries as well as their combination. Thereafter, we propose MIXED-RANK, a ranking function that combines DBtrends with the best-evaluated entity ranking function. We show that: (i) MIXED-RANK outperforms state-of-the-art entity ranking functions, and; (ii) query logs can be used to improve RDF ranking functions.
Edgard Marx is a Ph.D. candidate at the University of Leipzig. He worked for six years in one of the largest South America research laboratories, Tecgraf, focusing mostly on designing and developing Geographic Information Systems and Urban mass transit monitoring systems. Since 2013 Edgard is been working on Natural Language Processing (NLP) with focusing on Question Answering and Semantic Search systems. He is active in the NLP academic community participating as a reviewer in conferences such as QALD (Open Challenge on Question Answering over Linked Data), and publishing several peer-reviewed publications in the area.