Ontology forecasting in scientific literature:semantic concepts prediction based on innovation-adoption priors

The ontology engineering research community has focused for many years on supporting the creation, development and evolution of ontologies. Ontology forecasting, which aims at predicting semantic changes in an ontology, represents instead a new challenge. In this paper, we want to give a contribution to this novel endeavour by focusing on the task of forecasting semantic concepts in the research domain. Indeed, ontologies representing scientific disciplines contain only 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. We address this issue by introducing the Semantic Innovation Forecast (SIF) model, which predicts new concepts of an ontology at time t + 1, using only data available at time t. Our approach relies on lexical innovation and adoption information extracted from historical data. We evaluated the SIF model on a very large dataset consisting of over one million scientific papers belonging to the Computer Science domain: the outcomes show that the proposed approach offers a competitive boost in mean average precision-at-ten compared to the baselines when forecasting over 5 years.

The value of innovation:the interaction of competition, R&D and IP

This paper analyses market valuations of UK companies using a new data set of their R&D and IP activities (1989–2002). In contrast to previous studies, the analysis is conducted at the sectoral-level, where the sectors are based on the technological classification originating from Pavitt [Pavitt, K., 1984. Sectoral patterns of technical change. Research Policy 13, 343–373]. The first main result is that the valuation of R&D varies substantially across these sectors. Another important result is that, on average, firms that receive only UK patents tend to have no significant market premium. In direct contrast, patenting through the European Patent Office does raise market value, as does the registration of trade marks in the UK for most sectors. To explore these variations the paper links competitive conditions with the market valuation of innovation. Using profit persistence as a measure of competitive pressure, we find that the sectors that are the most competitive have the lowest market valuation of R&D. Furthermore, within the most competitive sector (‘science based’ manufacturing), firms with larger market shares (an inverse indicator of competitive pressure) also have higher R&D valuations, as well as some positive return to UK patents. We conclude that this evidence supports Schumpeter by finding higher returns to innovation in less than fully competitive markets and contradicts Arrow [Arrow, K., 1962. Economic welfare and the allocation of resources for invention. In: Nelson, R. (Ed.), The Rate and Direction of Inventive Activity. Princeton University Press, Princeton], who argued that, with the existence of IP rights, competitive market structure provides higher incentives to innovate.