{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T22:07:32Z","timestamp":1789078052665,"version":"build-2803163510"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686868","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T00:00:00Z","timestamp":1788307200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,2]]},"abstract":"<jats:p>Symbolic reasoners offer a simple contract: provide an ontology, receive entailments. Neurosymbolic reasoners promise the same task with added scalability and tolerance to noise, but break this contract by requiring an ontology-specific training dataset, which in most cases does not exist and must be gathered from scratch. This bottleneck blocks practitioners from applying neurosymbolic reasoners to new ontologies. We present SYNTHOLOGY, an ontology-agnostic synthetic data generator that closes this gap by producing the required training data from any OWL 2 RL ontology, with no need for manual data gathering. SYNTHOLOGY engineers data through ontology-guided backward-chaining proof search, which guarantees deep multi-hop derivations by construction, and introduces proof-based negative sampling. Together, these properties force models to traverse the full reasoning chain to correctly classify triples. Our evaluation shows that a model trained on SYNTHOLOGY data outperforms models trained on baseline data; part of this advantage reflects test-set familiarity, though the gain in rejecting near-miss negatives persists under an independently generated test set (Section 6.2). Proof-based negative sampling yields consistent gains over random and constrained sampling on all learning-quality metrics, and SYNTHOLOGY scales to ontologies on which traditional (ontology specific) generators run out of memory. To our knowledge, SYNTHOLOGY is the first ontology-agnostic synthetic data generator that provides multi-hop coverage, near-miss negatives, and derivation metadata by construction, a step toward making neurosymbolic reasoners as readily deployable on a new ontology as their symbolic counterparts.<\/jats:p>","DOI":"10.3233\/ssw260018","type":"book-chapter","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T21:09:22Z","timestamp":1789074562000},"source":"Crossref","is-referenced-by-count":0,"title":["SYNTHOLOGY: Ontology-Based Data Generation for Neurosymbolic Reasoning"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1749-8110","authenticated-orcid":false,"given":"Vincent","family":"Van Schependom","sequence":"first","affiliation":[{"name":"Department of Computer Science, KU Leuven campus Kulak Kortrijk"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8419-7363","authenticated-orcid":false,"given":"Cas","family":"Proost","sequence":"additional","affiliation":[{"name":"Department of Computer Science, KU Leuven campus Kulak Kortrijk"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8931-8343","authenticated-orcid":false,"given":"Pieter","family":"Bonte","sequence":"additional","affiliation":[{"name":"Department of Computer Science, KU Leuven campus Kulak Kortrijk"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies on the Semantic Web","Bridging the Gap Between Curated and Induced Semantics"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SSW260018","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T21:09:23Z","timestamp":1789074563000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SSW260018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,2]]},"ISBN":["9781643686868"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/ssw260018","relation":{},"ISSN":["1868-1158","2215-0870"],"issn-type":[{"value":"1868-1158","type":"print"},{"value":"2215-0870","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,2]]}}}