{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:44:50Z","timestamp":1781534690307,"version":"3.54.5"},"reference-count":32,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202210"],"award-info":[{"award-number":["62202210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62202210"],"award-info":[{"award-number":["62202210"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Recent studies increasingly leverage pre-trained language models (PLMs) for patent relevance assessment. In practice, whether a query patent and the candidate patent share the technical domain is a critical factor in relevance assessment. Existing PLM-based rerankers usually ignore domain information, leading to domain-insensitive semantic modeling. In particular, distributional discrepancies between a query and the candidate with different domain labels introduce domain bias. To address these issues, we propose a Domain-Sensitive Semantic Decomposition Network (DSSDNet), including native semantic decomposition, multi-field fusion regression and pairwise ranking with hard negative mining, for patent relevance assessment. It takes a query and the candidate to generate the technical representation which is decomposed into domain-sensitive and domain-insensitive parts via the gating mechanism with three constraints. And a domain-balanced focal loss is designed to remove domain bias existing in the domain-sensitive part. In addition, multi-field fusion regression is introduced to model the overall technical semantics by incorporating both domain-sensitive and domain-insensitive parts, along with domain information. As for pairwise ranking with hard negative mining, it optimizes the re-ranking objective from a holistic ranking perspective through increasing the margin between positive and negative instances. Experiments on the public CLEF-IP 2011 demonstrate that DSSDNet consistently outperforms strong baselines, achieving gains of 2.5\u201317% in Recall, 2\u20137% in MAP, and 3\u201315% in PRES at different cut-off levels. These results indicate that explicitly modeling domain-sensitive and domain-insensitive semantics is an effective way to mitigate domain bias and enhance patent re-ranking performance.<\/jats:p>","DOI":"10.3390\/info17050403","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T08:08:02Z","timestamp":1777018082000},"page":"403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Domain Balance Based on Semantic Decomposition for Patent Relevance Assessment"],"prefix":"10.3390","volume":"17","author":[{"given":"Fei","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9108-4965","authenticated-orcid":false,"given":"Jianjun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yates, A., Nogueira, R., and Lin, J. 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