Data Mining

Smart Topic Miner

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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

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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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Smart Topic Miner: Supporting Springer Nature Editors with Semantic Web Technologies

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STM interface

“Smart Topic Miner: Supporting Springer Nature Editors with Semantic Web Technologies” is poster paper presented at the Poster and Demo session [D45] on Wednesday 19th October 2016 at the 15th International Semantic Web Conference in Kobe, Japan

Authors:

Francesco Osborne, Angelo Antonio Salatino, Aliaksandr Birukou and Enrico Motta

Abstract:

Academic publishers, such as Springer Nature, annotate scholarly products with the appropriate research topics and keywords to facilitate the marketing process and to support (digital) libraries and academic search engines. This critical process is usually handled manually by experienced editors, leading to high costs and slow throughput. In this demo paper, we present Smart Topic Miner (STM), a semantic application designed to support the Springer Nature Computer Science editorial team in classifying scholarly publications.

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Automatic Classification of Springer Nature Proceedings with Smart Topic Miner

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Architecture of the system STM

 

“Automatic Classification of Springer Nature Proceedings with Smart Topic Miner” is conference paper presented on Friday 21st October 2016 at the 15th International Semantic Web Conference in Kobe, Japan

Authors:

Francesco Osborne, Angelo Antonio Salatino, Aliaksandr Birukou and Enrico Motta

Abstract:

The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. However, this process is typically carried out manually by expert editors, leading to high costs and slow throughput. In this paper we present Smart Topic Miner (STM), a novel solution which uses semantic web technologies to classify scholarly publications on the basis of 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.

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Clique Percolation Method in R: a fast implementation

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Clique Percolation Method (CPM) is an algorithm for finding overlapping communities within networks, introduced by Palla et al. (2005, see references). This implementation in R, firstly detects communities of size k, then creates a clique graph. Each community will be represented by each connected component in the clique graph.

Algorithm

The algorithm performs the following steps:

1- first find all cliques of size k in the graph
2- then create graph where nodes are cliques of size k
3- add edges if two nodes (cliques) share k-1 common nodes
4- each connected component is a community

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Advances Towards Early Detection of Research Topics

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Acknowledging new trends in the research environment is important for many stakeholders, such as researchers, institutional funding bodies, academic publishers, and companies. In particular, being able to identify them as soon as possible can bring an important strategical advantage.

A trend is usually defined as the general direction in which something is evolving. It is often used to describe the popularity of items, such as brands, words, and technologies. In order to detect trends, the relevant items should usually be already recognized and often somewhat popular. For this reason, current methods for detecting trends of research topics usually focus on identifying terms associated with a substantial number of documents, which usually took some years to be produced. Conversely, I theorise that it is possible to perform very early detection of research trends by identify embryonic topics, which have not yet been explicitly labelled or identified by a research community, and that is possible to do so by analysing the dynamics of existent topics. My work is grounded in Kuhn’s theory [1] of the scientific revolution according to which a paradigm shift, also called scientific revolution occurs when a paradigm cannot cope with anomalies, leading to a crisis that will persist until a new outcome redirects research through a new paradigm. In this abstract, I will discuss the state of the art, present an initial study which supports my hypothesis and outline the future directions of my work.

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Tech Report: Early Detection and Forecasting of Research Trends

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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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Advanced classification of Alzheimer's disease and healthy subjects based on EEG markers

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Authors:

Vitoantonio Bevilacqua, Angelo Antonio Salatino, Carlo Di Leo, Giacomo Tattoli, Domenico Buongiorno, Domenico Signorile, Claudio Babiloni, Claudio Del Percio, Antonio Ivano Triggiani, Loreto Gesualdo

Abstract:

In this study, we compared several classifiers for the supervised distinction between normal elderly and Alzheimer’s disease individuals, based on resting state electroencephalographic markers, age, gender and education.

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