A Lexico-Semantic Pattern Language for Learning Ontology Instances from Text

The Semantic Web aims to extend the World Wide Web with a layer of semantic information, so that it is understandable not only by humans, but also by computers. At its core, the Semantic Web consists of ontologies that describe the meaning of concepts in a certain domain or across domains. The domain ontologies are mostly created and maintained by domain experts using manual, time-intensive processes. In this paper, we propose a rule-based method for learning ontology instances from text that helps domain experts with the ontology population process. In this method we define a lexico-semantic pattern language that, in addition to the lexical and syntactical information present in lexico-syntactic rules, also makes use of semantic information. We show that the lexico-semantic patterns are superior to lexico-syntactic patterns with respect to efficiency and effectivity. When applied to event relation recognition in text-based news items in the domains of finance and politics using Hermes, an ontology-driven news personalization service, our approach has a precision and recall of approximately 80% and 70%, respectively

Limited impact of COVID-19-related diagnostic delay on cutaneous melanoma and squamous cell carcinoma tumour characteristics: a nationwide pathology registry analysis

Background: The COVID-19 pandemic reduced the number of skin cancer diagnoses, potentially causing a progression to unfavourable tumour stages. Objectives: To identify the impact of delayed diagnostics on primary invasive melanoma and cutaneous squamous cell carcinoma (cSCC) by comparing tumour (pT) stage, Breslow thickness and invasion depth from before to after the first and second lockdown periods. Methods: In this population-based cohort study, histopathology reports registered between 1 January 2018 and 22 July 2021 were obtained from the nationwide histopathology registry in the Netherlands. The Breslow thickness of melanomas, invasion depth of cSCCs, and pT stage for both tumour types were compared across five time periods: (i) pre-COVID, (ii) first lockdown, (iii) between first and second lockdowns, (iv) second lockdown and (v) after second lockdown. Breslow thickness was compared using an independent t-test. pT-stage groups were compared using a χ2-test. Outcomes were corrected for multiple testing using the false discovery rate. Results: In total, 20 434 primary invasive melanomas and 68 832 cSCCs were included in this study. The mean primary melanoma Breslow thickness of the prepandemic era (period i) and the following time periods (ii–v) showed no significant difference. A small shift was found towards unfavourable pT stages during the first lockdown compared with the pre-COVID period: pT1 52·3% vs. 58·6%, pT2 18·9% vs. 17·8%, pT3 13·2% vs. 11·0%, pT4 9·1% vs. 7·3% (P = 0·001). No relevant changes were seen in subsequent periods. No significant change in pT stage distribution was observed between the pre-COVID (i) and COVID-affected periods (ii–v) for cSCCs. Conclusions: To date, the diagnostic delay caused by COVID-19 has not resulted in relatively more unfavourable primary tumour characteristics of melanoma or cSCC. Follow-up studies in the coming years are needed to identify a potential impact on staging distribution and survival in the long term