The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas

@inproceedings{Salatino2018TheCS,
  title={The Computer Science Ontology: A Large-Scale Taxonomy of Research Areas},
  author={Angelo Salatino and Thiviyan Thanapalasingam and Andrea Mannocci and Francesco Osborne and Enrico Motta},
  booktitle={International Workshop on the Semantic Web},
  year={2018}
}
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the… 

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References

SHOWING 1-10 OF 29 REFERENCES

Klink-2: Integrating Multiple Web Sources to Generate Semantic Topic Networks

Klink-2 is presented, a novel approach which improves on earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web.

Ontology-Based Recommendation of Editorial Products

Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues, is created.

Automatic Classification of Springer Nature Proceedings with Smart Topic Miner

The architecture of the system is presented, the results of the evaluation showed that STM classifies publications with a high degree of accuracy, are very encouraging and as a result the required next steps to ensure large-scale deployment within the company are discussed.

A decade of Semantic Web research through the lenses of a mixed methods approach

This paper builds on a qualitative analysis of the main seminal papers, which adopt a top-down approach, and on quantitative results derived with three bottom-up data-driven approaches on a corpus of Semantic Web papers published between 2006 and 2015.

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

The Semantic Innovation Forecast (SIF) model is introduced, which predicts new concepts of an ontology at time t + 1, using only data available at time time t, which relies on lexical innovation and adoption information extracted from historical data.

A Framework for Ontology Learning and Data-driven Change Discovery

Text2Onto remains independent of a concrete target language while being able to translate the instantiated primitives into any (reasonably expressive) knowledge representation formalism, and allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.

Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles

This article describes how surveys for research fields can be represented in a semantic way, resulting in a knowledge graph that describes the individual research problems, approaches, implementations and evaluations in a structured and comparable way and demonstrates the utility of the resulting knowledge graph.

Reducing the Effort for Systematic Reviews in Software Engineering

A novel Expert-Driven Automatic Methodology, EDAM, is introduced that allows researchers to skip the tedious tasks of keywording and manually classifying primary studies, thus freeing effort for the analysis and the discussion in SRs.

Forecasting the Spreading of Technologies in Research Communities

It is hypothesised that it is possible to learn typical technology propagation patterns from historical data and to exploit this knowledge to anticipate where a technology may be adopted next and to alert relevant stakeholders about emerging and relevant technologies in other fields.

Crowdsourcing the Verification of Relationships in Biomedical Ontologies

This work uses crowdsourcing via Amazon Mechanical Turk with a Bayesian inference model to verify ontology hierarchy using microtask crowdsourcing and correctly verified 86% of the relations from the CORE subset of SNOMED CT in which Rector and colleagues previously identified errors via manual inspection.