Intelligent semantic systems

Developing intelligent solutions requires a comprehensive understanding and management of the data. Intelligent semantic systems provide the smart technologies to harvest large amounts of data and insight in order to find solutions to the problems in various application areas. Mainly, intelligent semantic systems helps organizations to:

Research challenges

Benefit

Semantic data management

Semantic technologies eliminate the need for tight integration imposed by relational databases. Therefore they have been proposed to enrich the unstructured or semi-structured information space with ontologically annotated data, as such improving interoperability and discoverability of datasets by reusing standard vocabularies, linking to external sources and enabling rich queries that span over datasets. The advantages are that they can lower the entry cost by importing datasets as they are and processing city data incrementally (cataloguing, entity extraction, annotation, linking, integration and reconciliation), without the need for global integration, pre-defined schemas, or even linking the entire input, while fully exploiting the power of semantic technologies and the use of a Web-wide wealth of resources, rich in meaning and structure.

Semantic reasoning

Semantic reasoning enables the automatic generation of consistent knowledge for decision making with better accuracy than state-of-the-art knowledge discovery systems. Our scalable reasoning architecture enables the identification of relevant data and information, as well as the capturing and representation of time-evolving knowledge from heterogeneous data sources. It also explains the logical connection of knowledge across space and time. This enables decision makers to understand (explanations, diagnoses) for events and anomalies in real-time by applying machine reasoning techniques and also to proceed from providing explanations to formulating recommendations and predictions.

Semantic search

The goal of semantic search research is to enable next-generation search systems to handle the discovery of information over Big Data. It poses specific challenges across a spectrum of topics; from information retrieval, to knowledge representation, cloud computing and user experience in order to find answers to complex user queries out of massive amounts of data.

Our approach is to develop a holistic information discovery framework which involves novel distributed indexing and federated query processing techniques and intelligently understands the underlying heterogeneous data and the specific information needs of the users. Drawing on our research into real-world use cases such as Smarter Cities or Integrated Care, we aim to extend the capabilities of current search systems by using semantic technologies.