English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Existing approaches on Question Answering over Knowledge Graphs (KGQA) haveweak generalizability. That is often due to the standard i.i.d. assumption onthe underlying dataset. Recently, three levels of generalization for KGQA weredefined, namely i.i.d., compositional, zero-shot. We analyze 25 well-known KGQAdatasets for 5 different Knowledge Graphs (KGs). We show that according to thisdefinition many existing and online available KGQA datasets are either notsuited to train a generalizable KGQA system or that the datasets are based ondiscontinued and out-dated KGs. Generating new datasets is a costly processand, thus, is not an alternative to smaller research groups and companies. Inthis work, we propose a mitigation method for re-splitting available KGQAdatasets to enable their applicability to evaluate generalization, without anycost and manual effort. We test our hypothesis on three KGQA datasets, i.e.,LC-QuAD, LC-QuAD 2.0 and QALD-9). Experiments on re-splitted KGQA datasetsdemonstrate its effectiveness towards generalizability. The code and a unifiedway to access 18 available datasets is online athttps://github.com/semantic-systems/KGQA-datasets as well ashttps://github.com/semantic-systems/KGQA-datasets-generalization.

Knowledge Graph Question Answering Datasets and Their Generalizability: Are They Enough for Future Research?