Dem@Care Context Descriptor Pattern

The Dem@Care Context Descriptor ontology has been integrated in the Linked Open Vocabularies (LOV) dataset, enabling its sharing and reuse by other datasets in the Linked Data Cloud (a human-readable description of the vocabulary is available here).

ESSIR 2015 crowd

MKLab successfully organized the 10th European Summer School on Information Retrieval

The European Summer School in Information Retrieval (ESSIR) is a scientific event founded in 1990, which has given rise to a series of Summer Schools held on a regular basis to provide high quality teaching of Information Retrieval (IR) and advanced IR topics to an audience of researchers and research students. ESSIR is typically a week-long event consisting of guest lectures and seminars from invited lecturers who are recognized experts in the field.

The 10th European Summer School in Information Retrieval (ESSIR 2015) was held in Thessaloniki, Greece between August 31 and September 4, 2015 and was hosted by the Multimedia Knowledge and Social Media Analytics Laboratory (MKLab) of the Information Technologies Institute (ITI) at the Centre for Research and Technology Hellas (CERTH).

Successful participation of CERTH-ITI in the interactive Surveillance Event Detection task of TRECVID 2015

CERTH-ITI participated to the interactive Surveillance Event Detection (SED) task of TRECVID 2015 with the video retrieval engine VERGE. VERGE results achieved the second place, among the submissions made by all institutions participating to TRECVID 2015 SED task (runs from 4 different institutions searching for 7 events: cell to ear, embrace, object put, people meet, people split up, poining) with interactive systems.

MKLab successfully participates in MediaEval 2015

MKLab had a very successful participation in the 2015 edition of the MediaEval benchmarking activity that took place in Wurzen, Germany on 14-15 September.

MKLab participates in the Hardware Grant Program of NVIDIA

MKLab is happy to announce that we received support from the Hardware Grant Program of the NVIDIA Corporation to continue our research (Zampoglou et al., 2015) on the field of multimedia forensics, which is currently carried out in the context of the REVEAL EC co-funded project. In particular, we are going to explore the potential of employing Deep Learning approaches on the problem of image splicing detection. 

H2020 - MAMEM's website is launched

The H2020 project MAMEM, started in May 1st, 2015, aims at integrating people with disabilities back into society by endowing them with the critical skill of managing and authoring multimedia content using novel and more natural interface channels. These channels will be controlled by eye-movements and mental commands, significantly increasing the potential for communication and exchange in leisure and non-leisure context.

Corporate Social Responsibility Metrics Retrieval tool

We are making available a new demo for the extraction of Corporate Responsibility Metrics in companies. The tool, which has been developed in the context of the Wikirate project, exploits information extraction techniques to fetch articles and metrics from relevant websites concerning the corporate sustainability practices of thousands of companies.

Workshop on Semantic Web Technologies for Video and Image Analytics, in conjunction with ISWC 2015

1st Workshop on Semantic Web Technologies for Video and Image Analytics, in conjunction with ISWC 2015, Bethlehem, Pennsylvania, October 11-15, 2015 (


- Submission deadline: July 1, 2015

- Notifications: July 31, 2015

- Camera ready version: August 21, 2015

- Workshop: October 11-12, 2015

Dem@Care at ESWC2015 EU Project Networking Session

Dem@Care has been accepted at the EU Project Networking Session at ESWC 2015 that will take place on June 3, 2015 in Portoroz, Slovenia. 

MKLab collaborates in project to predict UK general election result

Researchers from Multimedia Knowledge and Social Media Analytics Laboratory collaborate with the University of Warwick and City University of London on a project where Twitter signals are leveraged to predict the outcome of the UK general election. They believe that their forecasts could be more accurate than traditional opinion polls.

Together, the team is using a framework that harvests political tweets, extracts various features about every party (e.g. volume of discussions) and then injects this information into public polling reports, producing a daily prediction of voting share.

With the outcome of the general election in terms of seats more uncertain than ever, this approach will provide crucial insights into how public opinion is developing and what factors might be influencing any changes in support. Early results of the system have already tracked the surge in support for the SNP and the fluctuating fortunes of UKIP.