Abstract
The vision of the Semantic Web is to get information with a defined meaning in a way that computers and people can work collaboratively. In this sense, the RDF model provides such a definition by linking and representing resources and descriptions through defined schemes and vocabularies. However, much of the information able to be represented is contained within plain text, which results in an unfeasible task by humans to annotate large scale data sources such as the Web. Therefore, this paper presents a strategy for the extraction and representation of RDF statements from text. The idea is to provide an architecture that receives sentences and returns triples with elements linked to resources and vocabularies of the Semantic Web. The results demonstrate the feasibility of representing RDF statements from text through an implementation following the proposed strategy.
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Notes
- 1.
An ontology defines the concepts, terms, classes, taxonomies, and rules of a domain [11].
- 2.
In this context, knowledge elements refer to Conceptual Knowledge [22] in terms of things or concepts and the way they are related to each other with the support of an ontology.
- 3.
https://www.w3.org/TR/sparql11-overview/. All URLs in this paper were last accessed on 2019/04/15.
- 4.
Different to formatted text, plain text does not contain any style information or graphical objects and refers to only readable characters.
- 5.
Semantic roles identify the participants in an event guided by a verb and its underlying relationship [13].
- 6.
FOX framework. http://aksw.org/Projects/FOX.html.
- 7.
- 8.
- 9.
WordNet is a lexical database for English http://wordnet.princeton.edu.
- 10.
From a First Order Logic perspective, the predicate of a sentence corresponds to the main verb and any auxiliaries surrounding it.
- 11.
Although the architecture only admits plain text as input data, there are several types of data that could be considered such as structured data (e.g., databases, tables), images, or raw data (e.g., data from sensors).
- 12.
In this work, we indistinctly refer to named entities as only entities.
- 13.
- 14.
This process is often supported by the Semantic Role Labeling task, which helps to determine the role or action performed by an entity within a statement.
- 15.
Stanford CoreNLP models https://stanfordnlp.github.io/CoreNLP/.
- 16.
MatePlus https://github.com/microth/mateplus.
- 17.
Data models downloaded from https://code.google.com/archive/p/mate-tools/downloads.
- 18.
- 19.
Jena https://jena.apache.org.
- 20.
- 21.
- 22.
- 23.
The LonelyPlanet dataset was originally downloaded by Martin Kavalec from the site http://www.lonelyplanet.com/destinations.
- 24.
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Acknowledgments
This work was funded in part by the Fondo SEP-Cinvestav, Project No. 229. We would like to thank the reviewers for their comments on this paper.
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Martinez-Rodriguez, J.L., Lopez-Arevalo, I., Rios-Alvarado, A.B., Hernandez, J., Aldana-Bobadilla, E. (2019). Extraction of RDF Statements from Text. In: Villazón-Terrazas, B., Hidalgo-Delgado, Y. (eds) Knowledge Graphs and Semantic Web. KGSWC 2019. Communications in Computer and Information Science, vol 1029. Springer, Cham. https://doi.org/10.1007/978-3-030-21395-4_7
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