Fostering Natural Language Question Answering Over Knowledge Bases in Oncology EHR

2019 ◽  
Author(s):  
Marco Antonio Schwertner ◽  
Sandro Jose Rigo ◽  
Denis Andrei Araujo ◽  
Alan Barcelos Silva ◽  
Bjoern Eskofier
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Knowledge Bases ◽  
Natural Language Question ◽  
Language Question

Industry Watch

2007 ◽  
Vol 13 (2) ◽  
pp. 185-189
Author(s):  
ROBERT DALE
Keyword(s):  
Natural Language ◽  
Search Engine ◽  
Question Answering ◽  
University Of California ◽  
News Item ◽  
News Sources ◽  
Language Technology ◽  
Natural Language Question ◽  
The University ◽  
Language Question

“Powerset Hype to Boiling Point”, said a February headline on TechCrunch. In the last installment of this column, I asked whether 2007 would be the year of question-answering. My query was occasioned by a number of new attempts at natural language question-answering that were being promoted in the marketplace as the next advance upon search, and particularly by the buzz around the stealth-mode natural language search company Powerset. That buzz continued with a major news item in the first quarter of this year: in February, Xerox PARC and PowerSet struck a much-anticipated deal whereby PowerSet won exclusive rights to use PARC's natural language technology, as announced in a VentureBeat posting. Following the scoop, other news sources drew the battle lines with titles like “Can natural language search bring down Google?”, “Xerox vs. Google?”, and “Powerset and Xerox PARC team up to beat Google”. An April posting on Barron's Online noted that an analyst at Global Equities Research had cited Powerset in his downgrading of Google from Buy to Neutral. And, all this on the basis of a product which, at the time of writing, very few people have actually seen. Indications are that the search engine is expected to go live by the end of the year, so we have a few more months to wait to see whether this really is a Google-killer. Meanwhile, another question remaining unanswered is what happened to the Powerset engineer who seemed less sure about the technology's capabilities: see the segment at the end of D7TV's PartyCrasher video from the Powerset launch party. For a more confident appraisal of natural language search, check out the podcast of Barney Pell, CEO of Powerset, giving a lecture at the University of California–Berkeley.

An application of automated reasoning in natural language question answering

2010 ◽  
Vol 23 (2-3) ◽  
pp. 241-265 ◽  
Author(s):  
Ulrich Furbach ◽  
Ingo Glöckner ◽  
Björn Pelzer
Keyword(s):  
Natural Language ◽  
Automated Reasoning ◽  
Question Answering ◽  
Natural Language Question ◽  
Language Question

scholarly journals Introducing External Knowledge to Answer Questions with Implicit Temporal Constraints over Knowledge Base

2020 ◽  
Vol 12 (3) ◽  
pp. 45
Author(s):  
Wenqing Wu ◽  
Zhenfang Zhu ◽  
Qiang Lu ◽  
Dianyuan Zhang ◽  
Qiangqiang Guo
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Question Answering ◽  
Knowledge Bases ◽  
Temporal Information ◽  
Temporal Constraints ◽  
External Knowledge ◽  
Question Answering Systems ◽  
Natural Language Question ◽  
Applied Knowledge

Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answers from the knowledge base (KB). More and more studies have applied knowledge bases to question answering systems, and when using a KB to answer a natural language question, there are some words that imply the tense (e.g., original and previous) and play a limiting role in questions. However, most existing methods for KBQA cannot model a question with implicit temporal constraints. In this work, we propose a model based on a bidirectional attentive memory network, which obtains the temporal information in the question through attention mechanisms and external knowledge. Specifically, we encode the external knowledge as vectors, and use additive attention between the question and external knowledge to obtain the temporal information, then further enhance the question vector to increase the accuracy. On the WebQuestions benchmark, our method not only performs better with the overall data, but also has excellent performance regarding questions with implicit temporal constraints, which are separate from the overall data. As we use attention mechanisms, our method also offers better interpretability.

Natural Language Front-End for a Database

2005 ◽  
pp. 403-407 ◽  
Author(s):  
Boris Galitsky
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Information Access ◽  
Multiple Choice ◽  
Data Access ◽  
Plain Language ◽  
Front End ◽  
Natural Language Question ◽  
Essential Questions ◽  
Language Question

Whatever knowledge a database contains, one of the essential questions in its design and usability is how its users will interact with it. If these users are human agents, the most ordinary way to query a database would be in the natural language (Gazdar, 1999; Popescu, Etzioni, & Kautz, 2003; Sabourin, 1994). Natural language question answering (NL Q/A), wherein questions are posed in a plain language, may be considered the most universal but not always the best (i.e., fastest) way to provide the information access to a database. One should be aware that approaches to data access, such as visualization, menus and multiple choice, FAQ lists, and so forth, have been successfully employed long before the NL Q/A systems came into play. In the following, I discuss situations in which a particular information access approach is optimal.

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