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  • Citation: J. Chen (2016-05-03): RESTful API and trained model to implement a neuro-linguistic algorithms for semantic query suggestion. DOI:10.5447/IPK/2016/2
  • Abstract: The query suggestion API enables a real-time, semantic query suggestion for keyword based query systems. It has been implemented as RESTful service and can easily be integrated into third-party applications. Compared to popular search frameworks, like Apache Lucene, the suggested queries consider biological background knowledge that was extracted from biomedical literature. The service was successfully applied in the LAILAPS plant science search engine. In particular we were able to discover inferred associations between traits and genes and use them to automatically reformulate search phrases. The training dataset consits of 13,930,050 documents from PubMed articles, and gene and protein function describtions from UniProt, Plant Ontology and Gene Ontology.
  • License: CC BY 4.0 (Creative Commons Attribution)
  • DOI: 10.5447/IPK/2016/2

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//chenj@IPK-GATERSLEBEN.DE/RESTful API and trained model to implement a neuro-linguistic algorithms for semantic query suggestion

//chenj@IPK-GATERSLEBEN.DE/RESTful API and trained model to implement a neuro-linguistic algorithms for semantic query suggestion

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CHECKSUM: none CONTRIBUTOR: Matthias Lange, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland OT Gatersleben, Corrensstraße 3, 06466, Germany, 0000-0002-4316-078X
COVERAGE: none CREATOR: Jinbo Chen, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland OT Gatersleben, Corrensstraße 3, 06466, Germany, 0000-0003-3586-8160
DATE: Event: event
UPDATED: TimePoint: Tue May 03 14:50:09 CEST 2016
CREATED: TimePoint: Tue May 03 14:41:11 CEST 2016
DESCRIPTION: The query suggestion API enables a real-time, semantic query suggestion for keyword based query systems. It has been implemented as RESTful service and can easily be integrated into third-party applications. Compared to popular search frameworks, like Apache Lucene, the suggested queries consider biological background knowledge that was extracted from biomedical literature. The service was successfully applied in the LAILAPS plant science search engine. In particular we were able to discover inferred associations between traits and genes and use them to automatically reformulate search phrases. The training dataset consits of 13,930,050 documents from PubMed articles, and gene and protein function describtions from UniProt, Plant Ontology and Gene Ontology.
FORMAT: none IDENTIFIER: Unknown_ID
LANGUAGE: en PUBLISHER: Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland OT Gatersleben, Corrensstraße 3, 06466, Germany
RELATION: none RIGHTS: CC BY 4.0 (Creative Commons Attribution)
SIZE: 1 SOURCE: none
SUBJECT: Information Retrieval in Life Sciences, Query Suggestion, Big Data Mining TITLE: RESTful API and trained model to implement a neuro-linguistic algorithms for semantic query suggestion
TYPE: directory