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.

Discursive approaches to language policy

This book brings together the fields of language policy and discourse studies from a multidisciplinary theoretical, methodological and empirical perspective. The chapters in this volume are written by international scholars active in the field of language policy and planning and discourse studies. The diverse research contexts range from education in Paraguay and Luxembourg via businesses in Wales to regional English language policies in Tajikistan. Readers are thereby invited to think critically about the mutual relationship between language policy and discourse in a range of social, political, economic and cultural spheres. Using approaches that draw on discourse-analytic, anthropological, ethnographic and critical sociolinguistic frameworks, the contributors in this collection explore and refine the ‘discursive’ and the ‘critical’ aspects of language policy as a multilayered, fluid, ideological, discursive and social process that can operate as a tool of social change as well as reinforcing established power structures and inequalities.

Introduction : why are discursive approaches to language policy necessary?

This volume is a series of explorations of language policy from a discursive perspective. Its chief aim is to systematically explore the interconnectedness of language policy and discourse through what we are terming ‘discursive approaches to language policy’ (DALP). We show that language policy is a multilayered phenomenon that is constituted and enacted in and through discourse (which is defined more closely in Sect. 1.2). Language policy is a fast-growing, vibrant, and interdisciplinary field of inquiry that offers a variety of theoretical frameworks, methodologies, analytic approaches, and empirical findings: the framing sections at the beginning of each part of this volume and the commentary at the end frame the discussion of developments in language policy and especially the role of DALP therein.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.