Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction Based on Innovation-Adoption Priors

Abstract

Abstract. 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.

How are topics born? Understanding the research dynamics preceding the emergence of new areas

“…For example, the sudden appearance of a number of publications concerning a combination of previously uncorrelated topics may suggest that some pioneer researchers are investigating new possibilities and maybe shaping a new emerging area. In the same way, as pointed out in Salatino (2015), we can hypothesize a wide array of relevant dynamics that could anticipate the creation of a new research area, such as a new collaboration between two or more research communities (see for example Osborne et al (2014)), the creation of interdisciplinary workshops, a rise in the number of experts working on a certain combination of topics, a significant change in the vocabulary associated with relevant topics (Cano Basave et al 2016), and so on. This paper presents a study of some dynamics preceding the creation of novel topics which supports our hypothesis.…”

The Computer Science Ontology: A Comprehensive Automatically-Generated Taxonomy of Research Areas

“…Ontology Forecasting. The Semantic Innovation Forecast model (SIF) [42] is an approach to predict new concepts of an ontology at time t+1, using only data available at time t. Specifically, the proposed model favours the generation of innovative topics by considering distributions that enclose innovative and adopted lexicons based on word priors computed from historical data.…”

The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas

“…Ontology Forecasting. The Semantic Innovation Forecast model (SIF) [25] is an approach to predict new concepts of an ontology at time t + 1, using only data available at time t. The full version of SIF, learning from concepts in CSO, was able to significantly outperform 17 several variations of LDA [26], as reported in Table 4. Table 3.…”

“…Table 4. Mean average precision @10 for SIF [25] and other four alternative algorithms based on LDA [26]. In the following sections, we will discuss how users can explore CSO and leave feedback at different levels of granularity.…”