Nevertheless, there still has a long way to go todeal with cumbersome deep architectures and the increasinglygrowing knowledge graphs.

Modern approaches to metadata, ontologies and semantics have revolutionized the design and implementation of Digital Libraries, data repositories and Information Retrieval Systems, while they have made possible the management, analysis and querying of big volumes of heterogeneous data, both structured (linked data) and unstructured (e.g. social data, open data, or social networking interactions). Despite the benefits of these technologies for end users, like improved interaction with information, or better search, interpretation and sharing of data; yet there are many metadata and semantic challenges that need to be overcome in order to improve the user experience. These challenges are multi-dimensional in nature and could span from system architecture and data modeling to user interface design and evaluation, including: the implementation of linked data models in Digital Libraries, the design and evaluation of information retrieval algorithms across heterogeneous collections of structured and unstructured data; the integration, mapping and interoperability of heterogeneous linked data models and ontologies; analysis of social data for the discovery of trends and future predictions; or social network analysis.

Despite a long traditionof efforts addressing the data integration challenge compris-ing enterprise architecture, master data management, datawarehouses, mediators and service oriented architectures, theintegration of such enterprise information and datasources isstill a major challenge [4].