Computational fact checking from knowledge networks

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation

Semantic contextualisation of social tag-based profiles and item recommendations

Proceedigns of 12th International Conference, EC-Web 2011, Toulouse, France, August 30 - September 1, 2011.The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-23014-1_9We present an approach that efficiently identifies the semantic meanings and contexts of social tags within a particular folksonomy, and exploits them to build contextualised tag-based user and item profiles. We apply our approach to a dataset obtained from Delicious social bookmarking system, and evaluate it through two experiments: a user study consisting of manual judgements of tag disambiguation and contextualisation cases, and an offline study measuring the performance of several tag-powered item recommendation algorithms by using contextualised profiles. The results obtained show that our approach is able to accurately determine the actual semantic meanings and contexts of tag annotations, and allow item recommenders to achieve better precision and recall on their predictions.This work was supported by the Spanish Ministry of Science and Innovation (TIN2008-06566-C04-02), and the Community of Madrid (CCG10- UAM/TIC-5877

Boncolás nyomai egy XVIII. századi gyermek mumifikálódott testén = Signs of Autopsy on the Body of a 10-Year-Old Girl Lived in the 18th Century

In 1994–1995, in the Church of the Whites, the corpses of 265 individuals dressed in funeral clothes, mummified to varying degrees, were unearthed from coffins excavated by the ethnographic museologists of the Ignác Tragor Museum, Vác (Hungary). The individuals were preserved by spontaneous mummification due to the crypt’s unique microclimate and burial pattern. Signs of an autopsy were found on the body of a 10-year old girl, Maria Theresa of Swartz, who died on the 26th January 1784. Two incisions were seen on her body: the longitudinal incision extends from the manubrium sterni to the symphysis, the second one is perpendicular to it, and connects the two hip paddles. The wound edges were later sutured, and the suture was partially retained. Since there was no sign of any other opening in the body, the autopsy was apparently aimed at finding out the cause of death and was limited to the area of the suspected disease. Rapid miliary tuberculosis and extrapulmonary bone tuberculosis must have contributed to the child's death; this was demonstrated by radiological examinations and confirmed by paleomicrobiological examination (residues of Mycobacterium tuberculosis detected in pulmonary and extrapulmonary samples as well). Another hypothesis is that appendicitis might have caused the death of a child with advanced tuberculosis. In the case of appendicitis, the intestinal function may stop. Suspected abdominal complaints may also have been caused by extrapulmonary gastrointestinal tuberculosis

Location-Adapted Music Recommendation Using Tags

Abstract. Context-aware music recommender systems are capable to suggest music items taking into consideration contextual conditions, such as the user mood or location, that may influence the user preferences at a particular moment. In this paper we consider a particular kind of context aware recommendation task — selecting music content that fits a place of interest (POI). To address this problem we have used emotional tags attached by a users ’ population to both music and POIs. Moreover, we have considered a set of similarity metrics for tagged resources to establish a match between music tracks and POIs. In order to test our hypothesis, i.e., that the users will reckon that a music track suits a POI when this track is selected by our approach, we have designed a live user experiment where subjects are repeatedly presented with POIs and a selection of music tracks, some of them matching the presented POI and some not. The results of the experiment show that there is a strong overlap between the users ’ selections and the best matching music that is recommended by the system for a POI

Social Bookmarking or Tagging

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