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In addition to being natively interpretable, they have provided competitive accuracy for various tasks. In this paper, we increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics. The resulting TM is<jats:italic>relational<\/jats:italic>and can take advantage of logical structures appearing in natural language, to learn rules that represent how actions and consequences are related in the real world. The outcome is a logic program of Horn clauses, bringing in a structured view of unstructured data. In closed-domain question-answering, the first-order representation produces 10 \u00d7 more compact KBs, along with an increase in answering accuracy from 94.83<jats:italic>%<\/jats:italic>to 99.48<jats:italic>%<\/jats:italic>. The approach is further robust towards erroneous, missing, and superfluous information, distilling the aspects of a text that are important for real-world understanding<\/jats:p>","DOI":"10.1007\/s10844-021-00682-5","type":"journal-article","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T07:16:29Z","timestamp":1641021389000},"page":"121-148","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A relational tsetlin machine with applications to natural language understanding"],"prefix":"10.1007","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3006-5249","authenticated-orcid":false,"given":"Rupsa","family":"Saha","sequence":"first","affiliation":[]},{"given":"Ole-Christoffer","family":"Granmo","sequence":"additional","affiliation":[]},{"given":"Vladimir I.","family":"Zadorozhny","sequence":"additional","affiliation":[]},{"given":"Morten","family":"Goodwin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"682_CR1","doi-asserted-by":"crossref","unstructured":"Abeyrathna, K. 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Measuring the Novelty of Natural Language Text Using the Conjunctive Clauses of a Tsetlin Machine Text Classifier. In 13Th international conference on agents and artificial intelligence (ICAART 2021). INSTICC.","DOI":"10.5220\/0010382204100417"},{"key":"682_CR5","doi-asserted-by":"crossref","unstructured":"Blakely, C. D., & Granmo, O.C. (2020). Closed-Form Expressions for Global and Local Interpretation of Tsetlin Machines with Applications to Explaining High-Dimensional Data. arXiv:2007.13885.","DOI":"10.1007\/978-3-030-79457-6_14"},{"key":"682_CR6","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008). Freebase: a collaboratively created graph database for structuring human knowledge. 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