Question answering over biomedical linked data with Grammatical Framework

Anca Marginean

doi:10.3233/sw-160223

Question answering over biomedical linked data with Grammatical Framework

Semantic Web ◽

10.3233/sw-160223 ◽

2017 ◽

Vol 8 (4) ◽

pp. 565-580 ◽

Cited By ~ 13

Author(s):

Anca Marginean

Keyword(s):

Linked Data ◽

Question Answering ◽

Grammatical Framework

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Special issue on question answering for Linked Data

Semantic Web ◽

10.3233/sw-170255 ◽

2017 ◽

Vol 8 (4) ◽

pp. 509-510 ◽

Cited By ~ 1

Author(s):

Axel-Cyrille Ngonga Ngomo ◽

Christina Unger

Keyword(s):

Linked Data ◽

Question Answering ◽

Special Issue

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Towards question answering on statistical linked data

Proceedings of the 10th International Conference on Semantic Systems - SEM '14 ◽

10.1145/2660517.2660521 ◽

2014 ◽

Cited By ~ 9

Author(s):

Konrad Höffner ◽

Jens Lehmann

Keyword(s):

Linked Data ◽

Question Answering

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

Semantic Web ◽

10.3233/sw-200404 ◽

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.

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