Smart Topic Miner

Smart Topic Miner (STM) is a web application which uses Semantic Web technologies to classify scholarly publications on the basis of Computer Science Ontology (CSO), a very large automatically generated ontology of research areas.

 

STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family. It analyses in real time a set of publications provided by an editor and produces a structured set of research topics and a number of Springer Nature Classification tags, which best characterise the proceedings book. Indeed, if you regularly publish in the main Computer Science conferences, your work was probably already classified and indexed by using STM. **

During the classification of proceedings, editors are involved in different tasks and one of them is determining the list of related terms and categories. This is accomplished according to their own experience, like exploring titles and abstracts visually. However, this appears to be time-consuming as well as complex to perform. In addition, new emerging topics may not find their space while some other current topics could be considered still popular.

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

Semantic Innovation Forecasting Model
Semantic Innovation Forecasting Model

Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption Priors” is a peer-reviewed paper presented on Tuesday 22nd November 2016 at the “Entity detection, matching and evolution” session at the 20th International Conference on Knowledge Engineering and Knowledge Management, Bologna, Italy

Authors:

Amparo Elizabeth Cano-Basave, Francesco Osborne and Angelo Antonio Salatino

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.

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Detection of Embryonic Research Topics by Analysing Semantic Topic Networks

“Detection of Embryonic Research Topics by Analysing Semantic Topic Networks” is a workshop paper I presented at the SAVESD workshop held in conjunction with the World Wide Web Conference in 2016 in Montreal (CA). Angelo Antonio Salatino and Enrico Motta Abstract Being aware of new research topics is an important asset for anybody involved in … Read more

Tech Report: Early Detection and Forecasting of Research Trends

This is the First Year Probation Report submitted for the registration to the degree of Doctor of Philosophy at the Open University.

Abstract

Identifying and forecasting research trends is of critical importance for a variety of stakeholders, including researchers, academic publishers, institutional funding bodies, companies operating in the innovation space and others.

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Early Detection and Forecasting of Research Trends

“Early Detection and Forecasting of Research Trends” is a paper presented at the Doctoral Consortium of the 14th International Semantic Web Conference (ISWC2015) in Bethlehem (PA, USA). Abstract Identifying and forecasting research trends is of critical importance for a variety of stakeholders, including researchers, academic publishers, institutional funding bodies, companies operating in the innovation space and others.Currently, … Read more

SSSW 2015 – 11th Summer School on Ontology Engineering and the Semantic Web

From the 5th to 11th of July the 11th Summer School on Ontology Engineering and the Semantic Web took place. Unlike other years, it was held in Bertinoro (IT) instead of Cercedilla (Spain). About the Summer School [from the sssw.org]: The Semantic Web Summer School, SSSW, was founded in 2003 by Enrico Motta and Asun Gomez-Perez as … Read more

Rexplore: Exploring Research Data

Rexplore leverages novel solutions in large-scale data mining, semantic technologies and visual analytics, to provide an innovative environment for exploring and making sense of scholarly data. In particular, Rexplore allows users:

  1. To detect and make sense of important trends in research, such as, significant migrations of researchers from one area to another, the emergence of new topics, the evolution of communities within a particular area, and several others.
  2. To identify a variety of interesting relations between researchers, e.g., recognizing authors who share similar research trajectories. These relations go well beyond the standard co-authorship links or relationships informed by social networks, which are commonly found in other systems.
  3. To perform fine-grained expert search with respect to detailed multi-dimensional parameters.
  4. To analyse research performance at different levels of abstraction, including individual researchers, organizations, countries, and research communities identified on the basis of dynamic criteria.

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