The provided baselines demonstrate the feasibility of the cross-lingual approach in KBQA, but at the same time indicate there is ample room for improvements.
In parallel, we created a Wikidata sample containing all entities with Russian labels. For instance, the answer for We applied entity linking described above to the 9,655 questions with verified answers and obtained 8.56 candidate entities per question on average. your coworkers to find and share information. A training dataset is a dataset of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier.. We could match only a fraction of those answers with Wikidata: Wikidata’s standard formatted literals may look completely different even if representing the same value. Wikidata is a free linked database for the structured data of its Wikimedia sister projects including Wikipedia, Wikivoyage, Wikisource and others. In addition, knowledge base structure is inherently language-independent – entities and predicates are assigned unique identifiers that are tied to specific languages through labels and descriptions, – which makes KBs more suitable for multilingual QA. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set. The work was performed by the authors of the paper.After sending queries to the Wikidata endpoint, we were able to find chains of length one or two for 3,194 questions; the remaining 6,461 questions were left unmatched. Each text string (question or answer) produces three types of queries to the Elasticsearch index: 1) all token trigrams; 2) capitalized bigrams (many named entities follow this pattern, e.g. The dataset is of interest for a wide community of researchers in the fields of Semantic Web, Question Answering, and Semantic Parsing.In the future, we plan to explore other data sources and approaches for RuBQ expansion: search query suggest APIs as for WebQuestions We thank Mikhail Galkin, Svitlana Vakulenko, Vladimir Kovalenko, Yaroslav Golubev, and Rishiraj Saha Roy for their valuable comments and fruitful discussion on the paper draft. SPARQL queries, and their subsequent in-house verification. For 1,154 questions the answers are Wikidata entities, and for 46 questions the answers are literals.Inspired by a taxonomy of query complexity in LC QuAD 2.0 Taking into account RuBQ’s modest size, we propose to use the dataset primarily for testing For each entry in the dataset, we provide: the Free 30 Day Trial Training dataset.
These adversarial examples are akin to unanswerable questions in the second edition of SQuAD dataset Our dataset has 1,500 unique questions in total. Out of 1,255 date and numerical answers, 683 were linked to a Wikidata entity such as a particular year. In addition, the smaller dataset lowers the threshold for KBQA experiments. The dataset is accompanied by a Wikidata sample of 212M triples that contain 8.1M entities with Russian and English labels, and an evaluation script. By clicking “Post Your Answer”, you agree to our To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Next, we generated candidate subgraphs spanning question and answer entities, restricting the length between them by two hops. entities with Russian labels. The dataset is collected from 159 Critical Role episodes transcribed to text dialogues, consisting of 398,682 turns. To increase the share of complex questions in the dataset, we manually constructed SPARQL queries for them.Finally, we added 300 questions marked as non-answerable over Wikidata, although their answers are present in the knowledge base.
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