Special issue on question answering for Linked Data

Semantic Web ◽  
2017 ◽  
Vol 8 (4) ◽  
pp. 509-510 ◽  
Author(s):  
Axel-Cyrille Ngonga Ngomo ◽  
Christina Unger
Keyword(s):  
Linked Data ◽  
Question Answering ◽  
Special Issue

scholarly journals Special issue on Linked Data for Health Care and the Life Sciences

Semantic Web ◽  
2014 ◽  
Vol 5 (2) ◽  
pp. 99-100 ◽  
Author(s):  
Mikel Egaña Aranguren ◽  
Jesualdo Tomás Fernández-Breis ◽  
Michel Dumontier
Keyword(s):  
Health Care ◽  
Linked Data ◽  
Life Sciences ◽  
Special Issue

scholarly journals A survey on knowledge graph embeddings with literals: Which model links better literal-ly?

Semantic Web ◽  
2020 ◽  
pp. 1-31
Author(s):  
Genet Asefa Gesese ◽  
Russa Biswas ◽  
Mehwish Alam ◽  
Harald Sack
Keyword(s):  
Link Prediction ◽  
Linked Data ◽  
Question Answering ◽  
Empirical Evaluation ◽  
Entity Linking ◽  
Relational Information ◽  
Structured Information ◽  
Low Dimensional ◽  
And Storage ◽  
Unstructured Information

Knowledge Graphs (KGs) are composed of structured information about a particular domain in the form of entities and relations. In addition to the structured information KGs help in facilitating interconnectivity and interoperability between different resources represented in the Linked Data Cloud. KGs have been used in a variety of applications such as entity linking, question answering, recommender systems, etc. However, KG applications suffer from high computational and storage costs. Hence, there arises the necessity for a representation able to map the high dimensional KGs into low dimensional spaces, i.e., embedding space, preserving structural as well as relational information. This paper conducts a survey of KG embedding models which not only consider the structured information contained in the form of entities and relations in a KG but also its unstructured information represented as literals such as text, numerical values, images, etc. Along with a theoretical analysis and comparison of the methods proposed so far for generating KG embeddings with literals, an empirical evaluation of the different methods under identical settings has been performed for the general task of link prediction.

Special issue on exploiting semantic technologies with particularization on linked data over grid and cloud architectures

2014 ◽  
Vol 32 ◽  
pp. 260-262 ◽  
Author(s):  
Ricardo Colomo-Palacios ◽  
Vladimir Stantchev ◽  
Alejandro Rodríguez-González
Keyword(s):  
Linked Data ◽  
Semantic Technologies ◽  
Special Issue

scholarly journals HAWK – Hybrid Question Answering Using Linked Data

2015 ◽  
pp. 353-368 ◽  
Author(s):  
Ricardo Usbeck ◽  
Axel-Cyrille Ngonga Ngomo ◽  
Lorenz Bühmann ◽  
Christina Unger
Keyword(s):  
Linked Data ◽  
Question Answering

Question answering systems: the story till the Arabic linked data

2017 ◽  
Vol 6 (1) ◽  
pp. 24
Author(s):  
Noureddine Doumi ◽  
Mimoun Malki ◽  
Djelloul Bouchiha ◽  
Abdelghani Bouziane
Keyword(s):  
Linked Data ◽  
Question Answering ◽  
Question Answering Systems

Introduction to the special issue on question answering

2011 ◽  
Vol 47 (6) ◽  
pp. 805-807
Author(s):  
Marie-Francine Moens ◽  
Patrick Saint-Dizier
Keyword(s):  
Question Answering ◽  
Special Issue
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