Abstract
Most knowledge sources on the Data Web were extracted from structured or semistructured data sources. Thus, they encompass solely a small fraction of the information available on the document-oriented Web. In this chapter, we present Bootstrapping Linked Data (BOA), a framework that aims to facilitate the extraction of Resource Description Framework (RDF) from text. The idea behind BOA is to extract natural language patterns that represent predicates found on the Data Web from unstructured data by using background knowledge from the Data Web. These patterns are then used to extract instance knowledge from unstructured data sources. This knowledge can finally be fed back into the Data Web. The approach followed by BOA is quasi-independent of the language in which the corpus is written. We demonstrate our approach by applying it to four different corpora and two different languages. We evaluate BOA on these data sets using DBpedia as background knowledge. Our results show that we can extract several thousand new facts in one iteration with high accuracy. Moreover, we provide the first multilingual repository of natural language representations (NLR) of predicates found on the Data Web. Finally, we present two applications of the natural language patterns generated by BOA, i.e., the fact validation framework DeFacto and the question answering engine Template - based SPARQL Learner (TBSL).
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A demo of the framework can be found at http://boa.aksw.org. The code of the project is at http://boa.googlecode.com.
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Gerber, D., Ngomo, AC.N. (2014). From RDF to Natural Language and Back. In: Buitelaar, P., Cimiano, P. (eds) Towards the Multilingual Semantic Web. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43585-4_12
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DOI: https://doi.org/10.1007/978-3-662-43585-4_12
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