Enhancing the Accessibility of Knowledge Graph Question Answering Systems through Multilingualization

Perevalov, Aleksandr; Ngomo, Axel-Cyrille Ngonga; Both, Andreas

doi:10.1109/icsc52841.2022.00048

Cited by 6 publications

(11 citation statements)

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“…Hence, we expect that new KGQA benchmarks will utilize Wikidata and existing benchmarks will migrate away from Freebase and DBpedia to Wikidata. This becomes evident in the distribution of benchmarks represented in the curated KGQA leaderboard [24] and is supported by a few publications in which KGQA benchmarks have already been moved to Wikidata [11,34,48]. One approach is to map the Freebase topics to Wikidata items using automatically generated mappings, for subjects as well as objects of triples [11,34].…”

Section: The Rise Of Wikidata In Kgqamentioning

confidence: 82%

“…The F-measure is one of the most commonly used metrics to evaluate KGQA systems, according to an up-to-date leaderboard [24]. It is calculated based on Precision and Recall and, thus, indicates a system's capacity to retrieve the right answer in terms of quality and quantity [21].…”

Section: Evaluation Metricmentioning

confidence: 99%

“…Newer systems must handle more volume and variety of knowledge. Finally, improved multilingual capabilities are urgently needed to increase the accessibility of KGQA systems to users around the world [23]. In the face of these requirements, we introduce QALD-10 as the newest successor of the Question Answering over Linked Data (QALD) benchmark series to facilitate the standardized evaluation of KGQA approaches.…”

Section: Introductionmentioning

confidence: 99%

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QALD-10 – The 10th challenge on question answering over linked data

Usbeck,

Yan,

Perevalov

et al. 2023

Self Cite

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Knowledge Graph Question Answering (KGQA) has gained attention from both industry and academia over the past decade. Researchers proposed a substantial amount of benchmarking datasets with different properties, pushing the development in this field forward. Many of these benchmarks depend on Freebase, DBpedia, or Wikidata. However, KGQA benchmarks that depend on Freebase and DBpedia are gradually less studied and used, because Freebase is defunct and DBpedia lacks the structural validity of Wikidata. Therefore, research is gravitating toward Wikidata-based benchmarks. That is, new KGQA benchmarks are created on the basis of Wikidata and existing ones are migrated. We present a new, multilingual, complex KGQA benchmarking dataset as the 10th part of the Question Answering over Linked Data (QALD) benchmark series. This corpus formerly depended on DBpedia. Since QALD serves as a base for many machine-generated benchmarks, we increased the size and adjusted the benchmark to Wikidata and its ranking mechanism of properties. These measures foster novel KGQA developments by more demanding benchmarks. Creating a benchmark from scratch or migrating it from DBpedia to Wikidata is non-trivial due to the complexity of the Wikidata knowledge graph, mapping issues between different languages, and the ranking mechanism of properties using qualifiers. We present our creation strategy and the challenges we faced that will assist other researchers in their future work. Our case study, in the form of a conference challenge, is accompanied by an in-depth analysis of the created benchmark.

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Section: The Rise Of Wikidata In Kgqamentioning

confidence: 82%

Section: Evaluation Metricmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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QALD-10 – The 10th challenge on question answering over linked data

Usbeck,

Yan,

Perevalov

et al. 2023

Self Cite

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“…Datasets are important, especially for ML-based systems, because such systems often have to be trained on a sample of data before they can be used on a similar test set. To this end, several KGQA datasets exist [6]. However, not all datasets contain a mapping of natural language questions to the logical form (e.g.…”

Section: Introductionmentioning

confidence: 99%

DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph

Banerjee¹,

Awale²,

Usbeck³

et al. 2023

Preprint

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In this work we create a question answering dataset over the DBLP scholarly knowledge graph (KG). DBLP is an on-line reference for bibliographic information on major computer science publications that indexes over 4.4 million publications published by more than 2.2 million authors. Our dataset consists of 10,000 question answer pairs with the corresponding SPARQL queries which can be executed over the DBLP KG to fetch the correct answer. DBLP-QuAD is the largest scholarly question answering dataset.

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“…We therefore migrate the dataset to such a KB, namely, Wikidata, in §3. Moreover, only a few studies have evaluated semantic parsers' performance in a multilingual setting, due to the scarcity of multilingual KBQA datasets (Perevalov et al, 2022b). No comparable benchmark exists for languages other than English, and it is therefore not clear whether results are generalizable to other languages.…”

Section: Introductionmentioning

confidence: 99%

Compositional Generalization in Multilingual Semantic Parsing over Wikidata

Cui

Aralikatte

Lent

et al. 2022

Transactions of the Association for Computational Linguistics

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Semantic parsing (SP) allows humans to leverage vast knowledge resources through natural interaction. However, parsers are mostly designed for and evaluated on English resources, such as CFQ (Keysers et al., 2020), the current standard benchmark based on English data generated from grammar rules and oriented towards Freebase, an outdated knowledge base. We propose a method for creating a multilingual, parallel dataset of question-query pairs, grounded in Wikidata. We introduce such a dataset, which we call Multilingual Compositional Wikidata Questions (MCWQ), and use it to analyze the compositional generalization of semantic parsers in Hebrew, Kannada, Chinese, and English. While within- language generalization is comparable across languages, experiments on zero-shot cross- lingual transfer demonstrate that cross-lingual compositional generalization fails, even with state-of-the-art pretrained multilingual encoders. Furthermore, our methodology, dataset, and results will facilitate future research on SP in more realistic and diverse settings than has been possible with existing resources.

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Enhancing the Accessibility of Knowledge Graph Question Answering Systems through Multilingualization

Cited by 6 publications

References 0 publications

QALD-10 – The 10th challenge on question answering over linked data

QALD-10 – The 10th challenge on question answering over linked data

DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph

Compositional Generalization in Multilingual Semantic Parsing over Wikidata

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