IESD’14 aims to create a forum for academic and industrial researchers and practitioners to discuss methods and techniques for intelligent exploration of semantic data from three angles:

Computational models:

methods and techniques for analysing semantic data to discover connections (e.g. relatedness, similarity, complementarity, contradictions, and causality), entity and ontology summarisation methods, graph exploration, exploratory search, visualisation and navigation, user/context modelling, adaptation and personalisation, feedback and prompts.

Human factors:

key human factors that impact the exploration of large interconnected complex data; effective interaction environments to help users in discovering connections and making sense of large volumes of structured and unstructured heterogeneous data (including large ontologies, Linked Data, semantically augmented corpus, semantic-enriched social data); comparison of different approaches to help users in a manner that does not overwhelm or confuse, intuitive ways to empower lay users of knowledge enriched technologies to explore semantic data.


Domains and applications:

Information needs which require exploration of semantic data; domains where semantic data is available and required for the practice (e.g. medical, media, social web, public administration, education, environmental/sustainability science); requirements gathering and evaluation studies; lessons learnt.


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