Multilingual XML-Based Named Entity Recognition for E-Retail Domains

We describe the multilingual Named Entity Recognition and Classification (NERC) subpart of an e-retail product comparison system which is currently under development as part of the EU-funded project CROSSMARC. The system must be rapidly extensible, both to new languages and new domains. To achieve this aim we use XML as our common exchange format and the monolingual NERC components use a combination of rule-based and machine-learning techniques. It has been challenging to process web pages which contain heavily structured data where text is intermingled with HTML and other code. Our preliminary evaluation results demonstrate the viability of our approach

SLC22A3 polymorphisms do not modify pancreatic cancer risk, but may influence overall patient survival

Expression of the solute carrier (SLC) transporter SLC22A3 gene is associated with overall survival of pancreatic cancer patients. This study tested whether genetic variability in SLC22A3 associates with pancreatic cancer risk and prognosis. Twenty four single nucleotide polymorphisms (SNPs) tagging the SLC22A3 gene sequence and regulatory elements were selected for analysis. Of these, 22 were successfully evaluated in the discovery phase while six significant or suggestive variants entered the validation phase, comprising a total study number of 1,518 cases and 3,908 controls. In the discovery phase, rs2504938, rs9364554, and rs2457571 SNPs were significantly associated with pancreatic cancer risk. Moreover, rs7758229 associated with the presence of distant metastases, while rs512077 and rs2504956 correlated with overall survival of patients. Although replicated, the association for rs9364554 did not pass multiple testing corrections in the validation phase. Contrary to the discovery stage, rs2504938 associated with survival in the validation cohort, which was more pronounced in stage IV patients. In conclusion, common variation in the SLC22A3 gene is unlikely to significantly contribute to pancreatic cancer risk. The rs2504938 SNP in SLC22A3 significantly associates with an unfavorable prognosis of pancreatic cancer patients. Further investigation of this SNP effect on the molecular and clinical phenotype is warranted

Linguistically motivated ontology mapping for the Semantic Web

Knowledge Sharing is a crucial issue in the Semantic Web: SW services expose and share knowledge content (expressed through ontologies and related knowledge bases) arising from distinct languages, locales, and personal perspectives; in this scenario, semantic alignment approaches play a pivotal role, providing viable solutions for integrating heterogeneous resources, still maintaining their local independence. We focus here on a 3-step approach to ontology mapping, which is strongly based on the exploitation of (monolingual and multilingual) linguistic resources for content publishing and discovery, and on a human intervention for supervising the process and assessing semantic links between mapped resources. Our methodology is also being supported by the development of dedicated tools for accompanying knowledge engineers and users across the different steps of creating and integrating ontology resources