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- 168 creator christian-beecks.
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- 168 creator tom-de-nies.
- 168 creator wesley-de-neve.
- 168 type InProceedings.
- 168 label "Normalized Semantic Web Distance".
- 168 sameAs 168.
- 168 abstract "In this paper, we investigate the Normalized Semantic Web Distance (NSWD), a semantics-aware distance measure between two concepts in a knowledge graph. Our measure advances the Normalized Web Distance, a recently established distance between two textual terms, to be more semantically aware. In addition to the theoretic fundamentals of the NSWD, we investigate its properties and qualities with respect to computation and implementation. We investigate three variants of the NSWD that make use of all semantic properties of nodes in a knowledge graph. Our performance evaluation based on the Miller-Charles benchmark shows that the NSWD is able to correlate with human similarity assessments on both Freebase and DBpedia knowledge graphs with values up to 0.69. Moreover, we verified the semantic awareness of the NSWD on a set of 20 unambiguous concept-pairs. We conclude that the NSWD is a promising measure with (1) a reusable implementation across knowledge graphs, (2) sufficient correlation with human assessments, and (3) awareness of semantic differences between ambiguous concepts.".
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- 17 creator andreas-both.
- 17 creator axel-cyrille-ngonga-ngomo.
- 17 creator ivan-ermilov.
- 17 creator michael-roder.
- 17 type InProceedings.
- 17 label "Detecting Similar Linked Datasets Using Topic Modelling".
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- 17 abstract "The Web of data is growing continuously with respect to both the size and number of the datasets published. Porting a dataset to five-star Linked Data however requires the publisher of this dataset to link it with the already available linked datasets. Given the size and growth of the Linked Data Cloud, the current mostly manual approach used for detecting relevant datasets for linking is obsolete. We study the use of topic modelling for dataset search experimentally and present TAPIOCA, a linked dataset search engine that provides data publishers with similar existing datasets automatically. Our search engine uses a novel approach for determining the topical similarity of datasets. This approach relies on probabilistic topic modelling to determine related datasets by relying solely on the metadata of datasets. We evaluate our approach on a manually created gold standard and with a user study. Our evaluation shows that our algorithm outperforms a set of comparable baseline algorithms including standard search engines significantly by 6% F1-score. Moreover, we show that it can be used on a large real world dataset with a comparable performance.".
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