EUROLAN, which has been held biennially since 1993, is one of the most significant European summer schools in the area of natural language processing. Each of the EUROLAN sessions has focused on an area of timely interest to researchers in the field; this year's EU-ROLAN involved students in tutorials and hands-on sessions concerned with semantic web technologies as applied to language processing, ontology creation and use, and consideration of the semantic web's potential and limitations. This year's school was organized by the Faculty of Computer Science at the A. I. Cuza University of Iasi, the Research Institute for Artificial Intelligence at the Romanian Academy in Bucharest, and the Department of Computer Science at Vassar College. It was the most successful in its 10-year history, with 119 registered participants from 23 countries. Hosted by the Romanian Academy, the most prestigious cultural and scientific institution in the country, the event was given significant attention in the media.

In the past, many knowledge representation systems failed because they were too monolithic and didn't scale well, whereas other systems failed to have an impact because they were small and isolated. Along with this tradeoff in size, there is also a constant tension between the cost involved in building a larger community that can interoperate through common terms and the cost of the lack of interoperability. The semantic web offers a good compromise between these approaches as it achieves wide-scale communication and interoperability using finite effort and cost. The semantic web is a set of standards for knowledge representation and exchange that is aimed at providing interoperability across applications and organizations. We believe that the gathering success of this technology is not derived from the particular choice of syntax or of logic.

Semantic web technologies (Hitzler, Krötzsch, and Rudolph 2010) are meant to deal with these issues, and indeed since the advent of linked data (Bizer, Heath, and Berners-Lee 2009) a few years ago, they have become central to mainstream semantic web research and development. We can easily understand linked data as being a part of the greater big data landscape, as many of the challenges are the same (Hitzler and Janowicz 2013). The linking component of linked data, however, puts an additional focus on the integration and conflation of data across multiple sources. This issue of AI Magazine is a followup from that meeting and contains significantly extended, enhanced, and updated contributions. We summarize the articles in the following paragraphs.

The titles of the five symposia were Discovery Informatics: AI Takes a Science-Centered View on Big Data (FS-13-01); How Should Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or --? (FS-13-02); Integrated Cognition (FS-13-03); Semantics for Big Data (FS-13-04); and Social Networks and Social Contagion: Web Analytics and Computational Social Science (FS-13-05). The highlights of each symposium are presented in this report. The symposia included Discovery Informatics: AI Takes a Science-Centered View on Big Data; How Should Intelligence Be Abstracted in AI Research: MDPs, Symbolic Representations, Artificial Neural Networks, or --? Integrated Cognition; Semantics for Big Data; and Social Networks and Social Contagion: Web Analytics and Computational Social Science. The Discovery Informatics symposium provided a forum for understanding the role of AI techniques in improving or innovating scientific processes. Researchers at the How Should Intelligence be Abstracted in AI Research symposium discussed and compared different abstractions of both intelligence and processes that might create it.