| Autor: |
Hitzler, Pascal, Janowicz, Krzysztof, Alam, Mehwish, Buscaldi, Davide, Cochez, Michael, Osborne, Francesco, Reforgiato Recupero, Diego, Sack, Harald, Refogiato Recupero, Diego |
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| Zdroj: |
Semantic Web (1570-0844); 2022, Vol. 13 Issue 3, p293-297, 5p |
| Abstrakt: |
The fourth paper, "Answer Selection in Community Question Answering Exploiting Knowledge Graph and Context Information" [[2]] by Golshan Afzali Boroujenia, Heshaam Failia, and Yadollah Yaghoobzadeha present a novel answer selection method that takes advantage of the knowledge embedded in KGs. Over the past years, there has been a rapid growth in the use and the importance of Knowledge Graphs (KGs) along with their application to many important tasks such as entity linking, recommender systems [[8]], etc. Link prediction within a KG is the task to find a set of links between entities (nodes) of the KG which might provide more knowledge and fill potential gaps of information. This special issue aims to reinforce the relationships between these communities and foster interdisciplinary research in the areas of KG, Deep Learning, and Natural Language Processing. [Extracted from the article] |
| Databáze: |
Complementary Index |
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