Based on the first RISIS project from 2014-2018, RISIS goes on given the high interest on RISIS datasets and their integration. RISIS2 has started in 2019 and provides ongoing access to the existing RISIS datasets:

RISIS2 Project Website

The SMS platform and the data store remain accessible:

SMS Platform

This project is funded by the European Union under the Seventh Framework Programme Grant Agreement n°313082
European Union

RISIS/SMS Presentations at K-CAP 2017

The Knowledge Capture (K-CAP) conference has been held since 2001 as a follow-up of the Knowledge Acquisition Workshops (KAW) that ran between 1986 and 1999. The Ninth International Conference on Knowledge Capture  (K-CAP 2017) aims at attracting researchers from diverse areas of Artificial Intelligence, including knowledge representation, knowledge acquisition, intelligent user interfaces, problem-solving and reasoning, planning, agents, text extraction, and machine learning, information enrichment and visualization, as well as researchers interested in cyber-infrastructures to foster the publication, retrieval, reuse, and integration of data.


This year,  RISIS SMS researchers will present two project-driven work in the K-CAP conference, December 4th-6th, 2017 Austin, Texas, United States. The research papers address the issue of context-sensitive linking as well as serendipitous browsing of the generated Linked Data:


Is my:sameAs the same as your:sameAs? Lenticular Lenses for Context Specific Identity“, Al Koudous Idrissou, Rinke Hoekstra, Frank Van Harmelen, Ali Khalili, Peter Van Den Besselaar

Serendipity, the art of making an unsought finding plays also an important role in the emerging field of data science, allowing the discovery of interesting and valuable facts not initially sought for. Previous research has extracted many serendipity-fostering patterns applicable to digital data-driven systems.
Linked Open Data (LOD) on the Web which is powered by the Follow-Your-Nose effect, provides already a rich source for serendipity. The serendipity most often takes place when browsing data. Therefore, flexible and intuitive browsing user interfaces which support serendipity triggers such as enigmas, anomalies and novelties, can increase the likelihood of serendipity on LOD. In this work, we propose a set of serendipity-fostering design features supported by an adaptive multigraph-based faceted browsing interface to catalyze serendipity on Semantic Web and LOD environments.


Fostering Serendipitous Knowledge Discovery using an Adaptive Multigraph-based Faceted Browser“, Ali Khalili, Pek van Andel, Peter van Den Besselaar, Klaas Andries de Graaf

Linking between entities in different datasets is a crucial element of the Semantic Web architecture, since those links allow us to integrate datasets without having to agree on a uniform vocabulary.
However, it is widely acknowledged that the owl:sameAs construct is too blunt a tool for this purpose. It entails full equality between two resources independent of context. But whether or not two resources should be considered equal depends not only on their intrinsic properties, but also on the purpose or task for which the resources are used.
We present a system for constructing context-specific equality links. In a first step, our system generates a set of probable links between two given datasets. These potential links are decorated with rich metadata describing how, why, when and by whom they were generated. In a second step, a user then selects the links which are suited for the current task and context, constructing a context-specific “Lenticular Lens”. Such lenses can be combined using operators such as union, intersection, difference and composition.
We illustrate and validate our approach with a realistic application that supports researchers in social science.


For more information, visit K-CAP 2017 website: