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Summarization: Real world social networks are highly dynamic environments consisting of numerous users and communities, rendering the tracking of their evolution a challenging problem. In this work, we propose a parallel algorithm for tracking dynamic communities between consecutive timeframes of the social network, where communities are represented as undirected graphs. Our method compares the communities based on the widely adopted Jaccard similarity measure and is implemented on top of Apache Flink, a novel framework for parallel and distributed data processing. We evaluate the benefits, in terms of execution time, that parallel processing brings to community tracking on datasets carrying different quantitative characteristics, derived from two popular social media platforms; Twitter and Mathematics Stack Exchange Q&A. Experiments show that our parallel method has the ability to calculate the similarity of communities within seconds, even for large social networks, consisting of more than 600 communities per timeframe. Παρουσιάστηκε στο: 10th Hellenic Conference on Artificial Intelligence

In this paper, we reflect on ways to improve the quality of bio-medical information retrieval by drawing implicit negative feedback from negated information in noisy natural language search queries. We begin by studying the extent to which negations occur in clinical texts and quantify their detrimental effect on retrieval performance. Subsequently, we present a number of query reformulation and ranking approaches that remedy these shortcomings by resolving natural language negations. Our experimental results are based on data collected in the course of the TREC Clinical Decision Support Track and show consistent improvements compared to state-of-the-art methods. Using our novel algorithms, we are able to reduce the negative impact of negations on early precision by up to 65%.

We studied the predictability of community evolution in on-line social networks as a supervised learning task with sequential and non-sequential classifiers. Communities that are formed in on-line social networks as a result of user interaction evolve over time. Structural, content and contextual features as well as the previous states of a community are considered as the features that are involved in the task of community evolution. The evolution phenomena we try to predict are the continuation, shrinking, growth and dissolution. The evolution labels stem from a community tracker that provided the background truth. We have obtained interesting results on a set from Twitter.

This paper introduces a method that deals with unwanted mail messages by combining active learning with incremental clustering. The proposed approach is motivated by the fact that the user cannot provide the correct category for all received messages. The email messages are divided into chronological batches (e.g. one per day). The user is asked to give the correct categories (labels) for the messages of the first batch and from then on the proposed algorithm decides when to ask for a new label, based on a clustering of the messages that is incrementally updated. We test different variants of the algorithm on a number of different datasets and show that it achieves very good results with only 2% of all email messages labelled by the user.

We present the application of a recently proposed probabilistic logical formalism, on the task of sensor data fusion in the USEFIL project. USEFIL seeks to extract valuable knowledge concerning the well-being of elderly people by combining information coming from low-cost, unobtrusive monitoring devices. The approach we adopt to device its data fusion component is based on the Event Calculus and the stochastic logic programming language ProbLog and aims towards constructing a semantic representation of the received data, usable by a Decision Support System that will assist elderly people in their every day activities and will provide to doctors, relatives and carers insights on the user's behaviour and health.