Motivated by the global unrest related to the COVID-19 pandemic, this demo paper presents a system for acquisition of COVID-related data from different, public sources, and interlinking under a common semantic data model at a fine level of granularity. The integrated data set contains data from several European countries, which come in different schemata, formats, granularity, and data integration acts as a facilitator towards querying data from different sources, joint data analysis, and identifying correlations at varying geographical level. Moreover, our work shows how such an integrated data set can be exploited to answer complex questions for the pandemic, also in combination with other data sets via federated queries. © 2021 Copyright held by the owner/author(s).

We present a big data framework for the prediction of streaming trajectory data, enriched from other data sources and exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. To meet this goal, we follow a multi-step methodology. First, we efficiently compress surveillance data in an online fashion, by constructing trajectory synopses that are spatio-temporally linked with streaming and archival data from a variety of diverse and heterogeneous data sources. The enriched stream of trajectory synopses is stored in a distributed RDF store, supporting data exploration via SPARQL queries. The enriched stream of synopses along with the raw data is consumed by trajectory prediction algorithms that exploit mined patterns from the RDF store, namely medoids of (sub-) trajectory clusters, which prolong the horizon of useful predictions. The framework is extended with offline and online interactive visual analytics tool to facilitate real world analysis in the maritime and the aviation domains.

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This paper addresses the problem of synthesizing ontology alignment methods by maximizing the social welfare within a group of interacting agents: Specifically, each agent is responsible for computing mappings concerning a specific ontology element, using a specific alignment method. Each agent interacts with other agents with whom it shares constraints concerning the validity of the mappings it computes. Interacting agents form a bipartite factor graph, composed of variable and function nodes, representing alignment decisions and utilities, respectively. Agents need to reach an agreement to the mapping of the ontology elements consistently to the semantics of specifications with respect to their mapping preferences. Addressing the synthesis problem in such a way allows us to use an extension of the max-sum algorithm to generate near-to-optimal solutions to the alignment of ontologies through local decentralized message passing. We show the potential of such an approach by synthesizing a number of alignment methods, studying their performance in the OAEI benchmark series.

This paper presents a method along with a set of measures for evaluating learned ontologies against gold ontologies. The proposed method transforms the ontology concepts and their properties into a vector space representation to avoid the common string matching of concepts and properties at the lexical layer. The proposed evaluation measures exploit the vector space representation and calculate the similarity of the two ontologies (learned and gold) at the lexical and relational levels. Extensive evaluation experiments are provided, which show that these measures capture accurately the deviations from the gold ontology. The proposed method is tested using the Genia and the Lonely Planet gold ontologies, as well as the ontologies in the benchmark series of the Ontology Alignment Evaluation Initiative.

This paper proposes a method for the mapping of ontologies that, in a greater extent than other approaches, discovers and exploits sets of latent features for approximating the intended meaning of ontology elements. This is done by applying the reverse generative process of the Latent Dirichlet Allocation model. Similarity between element pairs is computed by means of the Kullback-Leibler divergence measure. Experimental results show the potential of the method.

This paper proposes a method for learning ontologies given a corpus of text documents. The method identifies concepts in documents and organizes them into a subsumption hierarchy, without presupposing the existence of a seed ontology. The method uncovers latent topics in terms of which document text is being generated. These topics form the concepts of the new ontology. This is done in a language neutral way, using probabilistic space reduction techniques over the original term space of the corpus. Given multiple sets of concepts (latent topics) being discovered, the proposed method constructs a subsumption hierarchy by performing conditional independence tests among pairs of latent topics, given a third one. The paper provides experimental results over the GENIA corpus from the domain of biomedicine.

For groups of agents to act collaboratively, they need to recognize the need for collaboration, decide on the method to be followed for achieving goal states, assign responsibilities to subgroups and individuals, and so on, until responsibilities that can be fulfilled by individuals are reached. Aiming to support collaborative activity of humans within organized settings, this paper introduces a set of constructs for specifying organizational structures and introduces an explicit representation of individual and collaborative responsibilities within organizations. We conjecture that group members create common awareness towards recognizing the need for collaboration by forming group acceptances. Acceptances are formed by means of shared practices and beliefs of individual agents. The paper introduces state recognition recipes that drive group members within organizations to form acceptances, and thoroughly explains the exploitation of these recipes in conjunction to state achievement recipes for achieving goal states and fulfilling responsibilities collaboratively.