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- 202 creator dimitris-kontokostas.
- 202 creator jens-lehmann.
- 202 creator lazaros-ioannidis.
- 202 creator martin-bruemmer.
- 202 creator sebastian-hellmann.
- 202 type InProceedings.
- 202 label "NLP data cleansing based on Linguistic Ontology constraints".
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- 202 abstract "Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is -- compared to other domains, such as biology -- a late Linked Data adopter. However, it has seen a steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the Lemon and NIF vocabularies in 277 test cases and point out common quality issues.".
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- 202 keyword "Linked Data".
- 202 keyword "NLP".
- 202 keyword "data quality".
- 202 title "NLP data cleansing based on Linguistic Ontology constraints".
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- 210 creator anisa-rula.
- 210 creator axel-cyrille-ngonga-ngomo.
- 210 creator daniel-gerber.
- 210 creator jens-lehmann.
- 210 creator lorenz-buhmann.
- 210 creator matteo-palmonari.
- 210 type InProceedings.
- 210 label "Hybrid Acquisition of Temporal Scopes for RDF Data".
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- 210 abstract "Information on the temporal interval of validity for facts described by RDF triples plays an important role in a large number of applications. Yet, most of the knowledge bases available on the Web of Data do not provide such information in an explicit manner. In this paper, we present a generic approach which addresses this drawback by inserting temporal information into knowledge bases. Our approach combines two types of information to associate RDF triples with time intervals. First, it relies on temporal information gathered from the document Web by an extension of the fact validation framework DeFacto. Second, it harnesses the time information contained in knowledge bases. This knowledge is combined within a three-step approach which comprises the steps matching, selection and merging. We evaluate our approach against a corpus of facts gathered from Yago2 by using DBpedia and Freebase as input and different parameter settings for the underlying algorithms. Our results suggest that we can detect temporal information for facts from DBpedia with an F-measure of up to 70%.".
- 210 hasAuthorList authorList.
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- 210 keyword "Fact Checking".
- 210 keyword "Temporal Information Extraction".
- 210 keyword "Temporal Scoping".
- 210 keyword "Temporal Semantic Web".
- 210 title "Hybrid Acquisition of Temporal Scopes for RDF Data".
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- 211 creator fabio-ciravegna.
- 211 creator khadija-elbedweihy.
- 211 creator stuart-wrigley.
- 211 creator suvodeep-mazumdar.