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BACKGROUND: Aging is a process that does not refer only to the accumulation of damage in a human being over time but rather to individual changes determined by genetic, lifestyle, social and environmental factors. Aging is one of the greatest known risk factors for most human diseases. An older person in good health has a good level of independence, weighs less on the national health system and plays a productive and active role in his/her community; thus, the concept of "healthy aging", reflecting older adult-environment fit should be promoted. The interactions between lifestyle, including nutrition, and health play a fundamental role in the aging process; eating habits and eating behaviours are recognized as important modifiable factors potentially leading to a healthy "phenotype". OBJECTIVE AND METHODS: A multidisciplinary consortium with three Italian universities (the University of Milano-Bicocca, University of Pavia and University of Calabria) and Italian Small-Medium Enterprises proposed the Food Social Network (Food NET) project. Food NET overall outcome is to achieve target-specific guidelines and exact technologies for accessible functional foods, aimed at improving the quality of life and nutritional status of citizens (aged>65) of the Metropolitan City of Milan (Italy). CONCLUSIONS: This project is part of the "Smart Living" and food-related strategies aimed at responding to the needs of this target population, developing new food products, appropriate to meet the specific requirements and ensuring and promoting sustainable diets for healthy aging by effective food policy approaches. [ABSTRACT FROM AUTHOR]

Topical profiling of the datasets contained in the Linking Open Data (LOD) cloud has been of interest since such kind of data became available within the Web. Different automatic classification approaches have been proposed in the past, in order to overcome the manual task of assigning topics for each and every individual (new) dataset. Although the quality of those automated approaches is comparably sufficient, it has been shown, that in most cases a single topical label per dataset does not capture the topics described by the content of the dataset. Therefore, within the following study, we introduce a machine-learning based approach in order to assign a single topic, as well as multiple topics for one LOD dataset and evaluate the results. As part of this work, we present the first multi-topic classification benchmark for LOD cloud datasets, which is freely accessible. In addition, the article discusses the challenges and obstacles, which need to be addressed when building such a benchmark. [ABSTRACT FROM AUTHOR]

The development and standardization of Semantic Web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD.We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD. [ABSTRACT FROM AUTHOR]