Towards a Knowledge Graph Representing Research Findings by Semantifying Survey Articles
Despite significant advances in technology, the way how research is done and especially communicated has not changed much. We have the vision that ultimately researchers will work on a common knowledge base comprising comprehensive descriptions of their research, thus making research contributions transparent and comparable. The current approach for structuring, systematizing and comparing research results is via survey or review articles. In this article, we describe 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. We present a comprehensive ontology for capturing the content of survey articles. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantification of a survey. We demonstrate the utility of the resulting knowledge graph by using it to answer queries about the different research contributions covered by the survey and evaluate how well the query answers serve readers’ information needs, in comparison to having them extract the same information from reading a survey paper.
KeywordsSemantic metadata enrichment Quality assessment Recommendation services Scholarly communication Semantic publishing
This work has been supported by the H2020 project no. 645833 (OpenBudgets.eu). The authors would like to thank Prof. Maria-Esther Vidal and Afshin Sadeghi for their support. We also appreciate the help of all participants of the evaluation. This work was conducted using the Protégé resource, which is supported by grant GM10331601 from the National Institute of General Medical Sciences of the United States National Institutes of Health.
- 1.Antoniou, G., Van Harmelen, F.: A Semantic Web Primer. MIT Press, Cambridge (2004)Google Scholar
- 2.Bryl, V., et al.: What’s in the proceedings? combining publisher’s and researcher’s perspectives. In: Proceedings of the 4th Workshop on Semantic Publishing (SePublica) (2014)Google Scholar
- 3.Capadisli, S., Riedl, R., Auer, S.: Enabling accessible knowledge. In: Conference for E-Democracy and Open Governement, p. 257 (2015)Google Scholar
- 5.Musen, M.A.: The Protég’e project: a look back and a look forward. In: AI Matters. Association of Computing Machinery Specific Interest Group in Artificial Intelligence, vol. 1(4) (2015)Google Scholar
- 7.Noy, N.F., McGuinness, D.L., et al.: Ontology development 101: A guide to creating your first ontology (2001)Google Scholar
- 8.Peroni, S., et al.: Research articles in simplified HTML: a web-first format for HTML-based scholarly articles. Technical report, PeerJ Preprints (2016)Google Scholar
- 9.Rakhmawati, N.A., et al.: Querying over federated SPARQL endpoints - a state of the art survey. In: CoRR abs/1306.1723 (2013)Google Scholar
- 12.Spanos, D.-E., Stavrou, P., Mitrou, N.: Bringing relational databases into the semantic web: a survey. Semant. Web 3(2), 169–209 (2012)Google Scholar
- 13.Vahdati, S., Arndt, N., Auer, S., Lange, C.: OpenResearch: collaborative management of scholarly communication metadata. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 778–793. Springer, Cham (2016). doi: 10.1007/978-3-319-49004-5_50 CrossRefGoogle Scholar