{"id":45475,"date":"2023-04-12T18:36:30","date_gmt":"2023-04-12T18:36:30","guid":{"rendered":"https:\/\/www.rightsdirect.com\/?post_type=blog_post&p=45475"},"modified":"2023-04-12T18:36:32","modified_gmt":"2023-04-12T18:36:32","slug":"die-bedeutung-semantischer-suchfunktionen","status":"publish","type":"blog_post","link":"https:\/\/www.rightsdirect.com\/de\/blog\/die-bedeutung-semantischer-suchfunktionen\/","title":{"rendered":"Die Bedeutung semantischer Suchfunktionen f\u00fcr Organisationen aus der Biowissenschaft\u00a0"},"content":{"rendered":"\n
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Das Folgende ist ein Auszug aus Accessing and Analysing Relevant Content in Today\u2019s Information Chaos.<\/a>\u00a0<\/p>\n\n\n\n \u00a0<\/p>\n\n\n\n To eliminate the noise and provide relevant search results, information solutions must go beyond simple keyword matching and to use search engines and algorithms that link concepts, topics, and associations to form a deeper understanding of a user\u2019s intent. <\/p>\n\n\n\n For instance, a researcher in pharmacovigilance may need to identify and list all potential Injection Site Reactions (ISRs) before an upcoming clinical trial. Searching published materials might identify traditional symptoms such as sore arm, redness, and inflammation. However, without integrating the company\u2019s Adverse Event or Safety database, the search results could miss other unknown reactions such as itching, eczema, and hives. <\/p>\n\n\n\n To tap into external and internal data sources, it becomes necessary to use biomedical vocabularies and ontologies (e.g., NIH\u2019s MeSH [MeSH Browser, n.d.]) which are semantically enriched and indexed. The result would be that a search for \u201cInjection Site Reactions\u201d could produce results from known ISRs that had been published previously and catalogued and could also draw from adverse events gleaned through internal sources. A comprehensive solution would account for a company\u2019s particular ontology as well as the various vocabularies specific to different organizations within the company. <\/p>\n\n\n\n While Google continues to evolve its search algorithms, biomedical research has its own set of challenges as noted in the article<\/a> \u201cDug: A Semantic Search Engine Leveraging Peer-Reviewed Knowledge to Span Biomedical Data Repositories\u201d by Waldrop et al: \u201cDespite the practical utility of Google\u2019s proprietary knowledge graph for general search, the provenance, depth, and quality of its biomedically relevant connections are not easily verifiable. There remains a need for a search tool capable of leveraging evidence-based biological connections to show researchers datasets useful for hypothesis generation or scientific support.\u201d <\/p>\n\n\n\n This is where functionality beyond linking key terms evolves into topic-linking (or topic co-occurrence). Like Dug, scientific communities and commercial entities are collaborating to improve semantic search. Continuing to build dictionaries and structures to organize, link, and catalog scientific data will require standardization and sustained commitment. <\/p>\n\n\n\n Life science companies should look to software solutions that embed semantic enrichment to find relevant scientific concepts faster and to accelerate new discoveries. <\/p>\n\n\n\n Keep reading<\/strong> Accessing and Analyzing Relevant Content in Today\u2019s Information Chaos.\u202f<\/a>\u00a0<\/p>\n\n\n\n Learn more<\/strong> about finding relevant content across data sources with semantic search in RightFind Navigate.\u202f<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Das Folgende ist ein Auszug aus Accessing and Analysing Relevant Content in Today\u2019s Information Chaos.\u00a0 Semantische Anreicherung ist die Zutat, …<\/p>\n","protected":false},"author":242,"featured_media":45476,"template":"","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"internal_tag":[],"topic":[],"coauthors":[],"class_list":["post-45475","blog_post","type-blog_post","status-publish","has-post-thumbnail","hentry"],"yoast_head":"\nSemantische Anreicherung ist die Zutat, um relevante Suchergebnisse zu erhalten, auch wenn sie nicht dieselbe Terminologie wie die Suchanfrage verwenden. Beispielsweise w\u00fcrde eine Abfrage nach \u201eZulassung von Arzneimitteln f\u00fcr seltene Krankheiten\u201c Ergebnisse f\u00fcr den Orphan Drug Act der FDA enthalten. Google erkennt an, dass sich \u201eArzneimittelzulassung\u201c auf \u201estaatliche Vorschriften\u201c bezieht. Es wei\u00df auch, dass \u201eArzneimittel f\u00fcr seltene Leiden\u201c und \u201eseltene Krankheit\u201c miteinander in Verbindung gebracht werden, obwohl unterschiedliche Begriffe verwendet werden. <\/h3>\n\n\n\n
Vergleichen Sie dies mit einem anderen Szenario. Sie wurden gebeten, eine wichtige Information zu finden, die Sie per E-Mail erhalten haben. Sie durchsuchen alle Ihre E-Mails, k\u00f6nnen sich aber nicht an den genauen Wortlaut oder Satz in der Betreffzeile erinnern. Es ist unwahrscheinlich, dass die textbasierte Suchfunktion der E-Mail das richtige Ergebnis zur\u00fcckgibt, wenn Sie nicht das genaue Wort verwenden, was unweigerlich zu mehreren Suchvorg\u00e4ngen und Zeitverlust beim Durchsuchen von E-Mails f\u00fchrt. <\/h3>\n\n\n\n
Die gro\u00dfen Unterschiede in den Algorithmen zwischen unseren beiden Beispielen \u2013 Google und einer einfachen E-Mail-Suche \u2013 zeigen die Macht und den Nutzen der semantischen Anreicherung in unserem t\u00e4glichen Leben. Wir verlassen uns zunehmend auf Suchwerkzeuge, um automatisch geeignete Synonyme einzuschlie\u00dfen. <\/h3>\n\n\n\n
Erfahren Sie mehr in dem nachfolgenden Blogbeitrag, der zuerst im Blog des CCC erschien.\u00a0<\/em><\/h4>\n\n\n\n
Where it comes into play <\/h4>\n\n\n\n
Challenges and opportunities <\/h4>\n\n\n\n