BEGIN:VCALENDAR VERSION:2.0 PRODID:-//hacksw/handcal//NONSGML v1.0//EN METHOD:PUBLISH BEGIN:VEVENT DTSTAMP:20240328T134931Z DESCRIPTION:Click for Latest Location Information: http://graphorum2019.dat aversity.net/sessionPop.cfm?confid=132&proposalid=11006\n
The modern ente rprise must do more than manage volumes of data – they need to g ain insight from it. Organizations are challenged by a variety of data spre ad across silos, key information locked in text, and the lack of semantics that can be leveraged to harmonize information into actionable intelligence .
\nThis talk will show how using a model-driven approach combi ned with machine learning strategies and NLP techniques can process enterpr ise information (i.e. content, documents, and data) and build knowledge gra phs that contain meaningful data.
\nUsing a real-world success story – capturing adverse events from free-form text to support diagno sis – we’ll demonstrate how models built on W3C semantic w eb technology can be used to create a set of classification rule-bases that automatically analyze and tag information using entity and fact extraction . The resulting metadata is consumed in a graph-based semantic data hub for analysis and consumption.
\nTopics to be discussed will includ e:
\n\n Discussion of traditional NLP classification strategies and model-driven, r ule-based classification\n Model-driven entity recognition\n Fact extraction using a model-based approach\n Leveraging model information for disambiguation, synonyms, and language sup port\n\n DTSTART:20191016T101500 SUMMARY:Knowledge Graphs and Model-Driven Classification DTEND:20191016T111459 LOCATION: See Description END:VEVENT END:VCALENDAR