Ontology matching for patent classification

Christoph Quix, Sandra Geisler, Rihan Hai, Sanchit Alekh

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Interdisciplinary research and development projects in medical engineering bene_t from well selected collaboration partners. The process of _nding such partners from often unfamiliar _elds is di_cult, but can be supported by an expert pro_le that is based on patent analysis and classifying the patents to competence _elds in medical engineering. Patent analysis and categorization are di_cult and require the analysis of the semantic content. Hence, we propose a twofold approach using a large controlled vocabulary, a smaller competence _eld ontology, and an alignment between them to assign patents to a certain competence _eld. The approach has two parts: a Topic Map approach and a Publi- cation approach. We evaluate these approaches and its components in several ways. Furthermore, we compare four di_erent ways to assign a patent to a competence _eld and show that the semantic wealth of a large biomedical ontology is bene_cial to the classi_cation task
Original languageEnglish
Title of host publicationProceedings of the 12th International Workshop on Ontology Matching (OM 2017) co-located with the 16th International Semantic Web Conference (ISWC 2017)
EditorsPavel Shvaiko
Number of pages12
Publication statusPublished - 2017
Externally publishedYes

Publication series



Dive into the research topics of 'Ontology matching for patent classification'. Together they form a unique fingerprint.

Cite this