Rich and diverse information sources of static or stream structured data do exist. However these are heterogeneous in various aspects: On the ways information is formed, on the ways information is described and shaped, on the ways different aspects of (abstract or concrete) entities are conceptualized and represented.
Computing accurate semantic descriptions of heterogeneous and disparate data sources, and managing their heterogeneity via scalable semantic alignment solutions is challenge. We need algorithms that – among others- are able to cope with big data sources, take advantage of background knowledge and consult humans to compute semantic agreements.
In addition to that, considering the existence of many diverse information sources in an open environment; each source having its own way of structuring, describing data and representing the world, we can easily see the necessity of distributed algorithms for these sources to reach semantic agreements towards shaping information jointly.
Generating linked RDF data from heterogeneous streaming and archival data sources: Populating the datAcron ontology Conference
Semantics 2017, Amstrerdam, Holland, 2017.
Open Proceedings , 2017.
Accessing and Reasoning with Data from Disparate Data Sources Using Modular Ontologies and OBDA Conference
SEMANTiCS 2015, Vienna, 2015.
Ontology-based Data Sourcestextquoteleft Integration for Maritime Event Recognition Conference
Workshop on Modeling, Computing and Data Handling for Marine Transportation (MCDMT 2015) @ IISA 2015, IEEE IEEE, Corfu – Greece, 2015.
AI Communications, On Line , 2014, ISSN: 1875-8452.