The Knowledge Media Institute is currently offering two fully-funded studentships commencing October 2017. Applications are invited from UK, EU and international students for full-time, 3-year study on the following PhD topics.
In particular, our research group is looking for PhD students to work on Big Data Analytics, under the supervision of Professor Enrico Motta.
We offer the following PhD topics.
Understanding and forecasting the spreading of research concepts.
The research will focus on the development of new algorithms which can automatically identify research concepts (e.g., technologies, approaches, theories, methods, models) in the literature and analyse how these concepts, which emerge in a particular research community (e.g., machine learning), are adopted by other communities (e.g., social science). The aim here is to be able to learn patterns of ‘research concept migration’, both to improve our understanding of the transmission of scientific ideas and also to enable the implementation of new systems able to alert researchers to potential interesting developments in other areas.
See the Rexplore project for our relevant previous work.
Exploratory search in large heterogenous data hubs.
Exploratory search solutions have so far primarily focused on supporting users in locating and making sense of information in large homogeneous repositories. With the emergence of large scale data portals, such as the MK Data Hub, the need has arisen for novel solutions effectively supporting users in exploring large heterogenous repositories, comprising thousands of different (but potentially related) data sets. This research will require the design and development of novel exploratory solutions, which will comprise not only new user interface paradigms but also novel intelligent data aggregation and abstraction techniques to facilitate retrieval and sensemaking.
See the MK:Smart project for our relevant previous work.
The deadline for applications is 10 April 2017 – see http://kmi.open.ac.uk/studentships/vacancies/ftphd/#sthash.hK3lMMEq.dpuf for more details on this and other opportunities.
Our research group at KMi, The Open University, is searching for a research assistant to work on the analysis of Big Scholarly Data in the context of Rexplore, a system which provides an innovative environment for exploring and making sense of scholarly data.
We are offering a paid internship for an initial period of 6 months, with the possibility for renewal. We believe it is a good opportunity for an undergrad or a master student to be part of a high-profile research team, under the supervision of Enrico Motta, Professor of Knowledge Technologies at the Open University. They would also have the opportunity of collaborate with major international publishers such as Elsevier and Springer Nature.
Specific tasks of the role are as follows:
– Integration and management of Big Data in the academic domain;
– Developing and testing the Rexplore technology as a service to be used by publishers and universities worldwide;
– Contributing to the creation of innovation algorithms to extract information from research data and to forecast the flow of knowledge in the research domain.
I would be grateful if you could flag this position to friend of yours who may be interested in this opportunity.
For further information please write to firstname.lastname@example.org.
I am organising with Alejandra Gonzalez-Beltran, Silvio Peroni and Sahar Vahdati the third edition of the Semantics, Analytics, Visualisation: Enhancing Scholarly Data workshop (SAVE-SD 2017). The SAVE-SD workshop aims to bring together publishers, companies and researchers from different fields to bridge the gap between the theoretical and practical aspects in regards to scholarly data. It was the first workshops to experiment with RASH, the novel HTML-based format that permits to embed semantic annotations within a paper and is now accepted by the main Semantic Web conferences.
SAVE-SD 17 is co-located with WWW 2017 and will take place April 3 2016 in Perth, Australia. The submission deadline is January 31, 2016.
More information on SAVE-SD 2017 are available at http://cs.unibo.it/save-sd/2017/index.html.
Last week I presented two research papers at the 20th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2016) in Bologna, Italy.
The first one introduces TechMiner, a novel tool which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, richer expert search, monitoring the emergence and impact of new technologies, studying the scholarly dynamics associated with the emergence of new technologies, and others.
The second paper deal with the novel task of ontology forecasting and introduces the Semantic Innovation Forecast (SIF) model, which predicts the concepts that will enrich an ontology in the future. Indeed, ontologies representing scientific disciplines contain only the 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. SIF instead allows to forecast future ontologies by analysing lexical innovation and adoption information extracted from historical data.
The papers presented at EKAW 2016 are the following:
- Osborne, F., Ribaupierre, H., and Motta, E. (2016) TechMiner: Extracting Technologies from Academic Publications. EKAW 2016, Bologna, Italy
- Cano-Basave, A. E., Osborne, F., Salatino, A.A. (2016) Ontology Forecasting in Scientific Literature: Semantic Concepts Prediction based on Innovation-Adoption Priors. EKAW 2016, Bologna, Italy
Last week I attended the 15th edition of the International Semantic Web Conference (ISWC 2016) where I presented our work on Smart Topic Miner (STM), the innovative application developed in collaboration with Springer Nature for automatically classifying research publications. STM was designed to classify proceedings and more in general any collection of articles by tagging them with relevant research areas and SN classification labels. It can be used for supporting editors in classifying new books and for quickly annotating several proceedings, thus creating a comprehensive knowledge base to assist the analysis of venues, journals and topic trends. Differently from other applications which characterize a text with topics, STM produce a full taxonomy of the relevant research areas rather than a flat list of keywords or categories. This helps editors and users to understand the context of each topic and its relationships with other research areas.
The demo of the system (available here http://rexplore.kmi.open.ac.uk/STM_demo/) was widely appreciated by the community and shortlisted for the best demo.
The papers presented at ISWC 2016 are the following:
- Osborne, F., Salatino, A., Birukou, A. and Motta, E. (2016) Automatic Classification of Springer Nature Proceedings with Smart Topic Miner. International Semantic Web Conference 2016, Kobe, Japan. – slides
- Osborne, F., Salatino, A., Birukou, A. and Motta, E. (2016) Smart Topic Miner: Supporting Springer Nature Editors with Semantic Web Technologies. Demo at International Semantic Web Conference 2016, Kobe, Japan.
The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. This process is typically carried out manually by expert editors, leading to high costs and slow throughput. For these reasons, the Rexplore team, in collaboration with Springer Nature, created 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, consisting in about 800 proceedings books each year. It analyses in real time a set of publications provided by an editor and produces a structured set of topics and a number of Springer Nature Classification tags, which best characterise the proceedings book. Differently from other applications which characterize a text with topics, STM produces a full taxonomy of the relevant research areas rather than a flat list of keywords or categories. This helps editors and users to understand the context of each topic and its relationships with other research areas.
You can try a public demo of STM at http://rexplore.kmi.open.ac.uk/STM_demo/
- Osborne, F., Salatino, A., Birukou, A. and Motta, E. (2016)Automatic Classification of Springer Nature Proceedings with Smart Topic Miner. International Semantic Web Conference 2016, Kobe, Japan. – slides