Francesco Osborne, Helene de Ribaupierre, Enrico Motta

KMi, The Open University, United Kingdom

EKAW2016

TechMiner: Extracting Technologies from Academic Publications

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Osborne, F., Motta, E. and Mulholland, P.Exploring scholarly data with Rexplore.International Semantic Web Conference 2013

technologies.kmi.open.ac.uk/rexplore/

Semantic Enhanced Scholarly Data

Most scholarly datasets capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others.

We still lack comprehensive information about the content of research papers, often simply represented as a collection of keywords or categories from a taxonomy.

Hence, researchers are working for extracting other kinds of entities, including:

– Genes– Chemical components– Epistemological concepts (e.g., hypothesis, motivation, experiments)

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What about technologies?

• Technologies such as applications, systems, languages and formats are an essential part of the Computer Science ecosystem.

• Current knowledge bases cover just a little part of the set of technologies presented in the literature.

• Identifying semantic relationships between technologies and other research entities allows:– Richer semantic search;– Monitoring the emergence and impact of new technologies, both within

and across scientific fields;– Studying the scholarly dynamics associated with the emergence of new

technologies; – Supporting companies in the field of innovation brokering and initiatives

for encouraging software citations across disciplines, e.g. FORCE11 Software Citation Working Group.

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TechMiner

TechMiner (TM) is a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies applications, systems, languages and formats from research publications.

It generates an OWL ontology describing technologies and their relationships with other research entities.

We evaluated TM on a manually annotated gold standard and found that it improves significantly both precision and recall over alternative NLP approaches.

– The proposed semantic features significantly improve both recall and precision.

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Some example – Tecnologies created by E. Motta

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Some example – Popular Knowledge Bases in SW

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TechMiner - Architecture

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Evaluation – Gold Standard

We tested our approach on a gold standard (GS) of manually annotated publications in the field of the Semantic Web

We selected a number of publications tagged with keywords related to this field (e.g., ‘semantic web’, ‘linked data’, ‘RDF’) and asked a group of 8 Semantic Web experts to annotate these papers with their technologies.

The resulting GS includes 548 publications, each of them annotated by at least two experts, and 539 technologies.

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Evaluation

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Evaluation

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Future works

• Enriching the approach for identifying other categories of scientific objects, such as datasets, algorithms and so on.

• Trying the approach on other research fields.• Building a pipeline for allowing human experts to

correct and manage the information extracted by TechMiner.

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Helene de Ribaupierre Enrico MottaFrancesco Osborne

Osborne, F., Ribaupierre, H., and Motta, E. (2016) TechMiner: Extracting Technologies from Academic Publications.EKAW 2016, Bologna, Italy

Email: francesco.osborne@open.ac.ukTwitter: FraOsborneSite: people.kmi.open.ac.uk/francesco

http://oro.open.ac.uk/47332/1/EKAW2016_TM.pdf