Last week I presented two research papers at the 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2016) in Bologna, Italy.
The first one introduces TechMiner, a novel tool which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, richer expert search, monitoring the emergence and impact of new technologies, studying the scholarly dynamics associated with the emergence of new technologies, and others.
The second paper deal with the novel task of ontology forecasting and introduces the Semantic Innovation Forecast (SIF) model, which predicts the concepts that will enrich an ontology in the future. Indeed, ontologies representing scientific disciplines contain only the research topics that are already popular enough to be selected by human experts or automatic algorithms. They are thus unfit to support tasks which require the ability of describing and exploring the forefront of research, such as trend detection and horizon scanning. SIF instead allows to forecast future ontologies by analysing lexical innovation and adoption information extracted from historical data.
The papers presented at EKAW 2016 are the following:
- Osborne, F., Ribaupierre, H., and Motta, E. (2016) TechMiner: Extracting Technologies from Academic Publications. EKAW 2016, Bologna, Italy
- Cano-Basave, A. E., Osborne, F., Salatino, A.A. (2016) Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption Priors. EKAW 2016, Bologna, Italy