Richard Waldinger from SRI International presented this for the Cognitive Systems Institute Speaker Series on April 7, 2016. To hear a replay go to http://cognitive-science.info/community/weekly-update/
Natural Language Access to Data via Deductiondiannepatricia
Richard Waldinger, principal scientist in SRI's Artificial Intelligence Center, made this presentation at the Cognitive Systems Institute Speaker Series on February 18, 2016.
Kris Kitani from Carnegie Mellon University and Chieko Asakawa, IBM Fellow, presented a “Cognitive Assistant for the Blind” as part of the Cognitive Systems Institute Speaker Series.
Tom Finin: “From Strings to Things: Populating Knowledge Bases from Text”diannepatricia
Tim Finin, Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC) gave a wonderful presentation entitled “From Strings to Things: Populating Knowledge Bases from Text,” as part of our Cognitive Systems Institute Speaker Series.
Cognitive Assistant for Data Scientists (CADS)Steven Miller
This document summarizes IBM's Cognitive Assistant for Data Scientists (CADS) project. The goals of CADS are to automate key areas of data analysis tasks to reduce the time data scientists spend on repetitive jobs and enable them to rapidly gain insights from data. CADS uses machine learning techniques to automate model training for supervised learning tasks. It demonstrates how computer-based augmentation can help data scientists by reducing the time to results by an order of magnitude and improving quality.
The document is a presentation on big data and Hadoop. It introduces the speaker, Adam Muise, and discusses the challenges of dealing with large and diverse datasets. Traditional approaches of separating data into silos are no longer sufficient. The presentation argues that a distributed system like Hadoop is needed to bring all data together and enable it to be analyzed as a whole.
The document discusses challenges related to large volumes of data, or "Big Data". Traditional technologies try to divide and separate data across different systems, but this becomes difficult to manage at scale. The presenter introduces Hadoop as an alternative approach that can handle large volumes of data in a single system and democratize access to data. Hadoop provides a framework for storage, management and processing of large datasets in a distributed manner across commodity hardware.
This document discusses big data, Hadoop, data science, and why Hadoop is useful for data science. It begins with defining big data and the 3 V's of big data. It then explains what Hadoop is and how it works using HDFS for storage and MapReduce for processing. The document defines what a data product is and provides examples. It defines data science as extracting meaning from data and building data products. Finally, it argues that Hadoop is useful for data science because it allows exploration of full datasets, mining of larger datasets, large-scale data preparation, and can accelerate data-driven innovation by removing speed barriers of traditional architectures.
Natural Language Access to Data via Deductiondiannepatricia
Richard Waldinger, principal scientist in SRI's Artificial Intelligence Center, made this presentation at the Cognitive Systems Institute Speaker Series on February 18, 2016.
Kris Kitani from Carnegie Mellon University and Chieko Asakawa, IBM Fellow, presented a “Cognitive Assistant for the Blind” as part of the Cognitive Systems Institute Speaker Series.
Tom Finin: “From Strings to Things: Populating Knowledge Bases from Text”diannepatricia
Tim Finin, Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC) gave a wonderful presentation entitled “From Strings to Things: Populating Knowledge Bases from Text,” as part of our Cognitive Systems Institute Speaker Series.
Cognitive Assistant for Data Scientists (CADS)Steven Miller
This document summarizes IBM's Cognitive Assistant for Data Scientists (CADS) project. The goals of CADS are to automate key areas of data analysis tasks to reduce the time data scientists spend on repetitive jobs and enable them to rapidly gain insights from data. CADS uses machine learning techniques to automate model training for supervised learning tasks. It demonstrates how computer-based augmentation can help data scientists by reducing the time to results by an order of magnitude and improving quality.
The document is a presentation on big data and Hadoop. It introduces the speaker, Adam Muise, and discusses the challenges of dealing with large and diverse datasets. Traditional approaches of separating data into silos are no longer sufficient. The presentation argues that a distributed system like Hadoop is needed to bring all data together and enable it to be analyzed as a whole.
The document discusses challenges related to large volumes of data, or "Big Data". Traditional technologies try to divide and separate data across different systems, but this becomes difficult to manage at scale. The presenter introduces Hadoop as an alternative approach that can handle large volumes of data in a single system and democratize access to data. Hadoop provides a framework for storage, management and processing of large datasets in a distributed manner across commodity hardware.
This document discusses big data, Hadoop, data science, and why Hadoop is useful for data science. It begins with defining big data and the 3 V's of big data. It then explains what Hadoop is and how it works using HDFS for storage and MapReduce for processing. The document defines what a data product is and provides examples. It defines data science as extracting meaning from data and building data products. Finally, it argues that Hadoop is useful for data science because it allows exploration of full datasets, mining of larger datasets, large-scale data preparation, and can accelerate data-driven innovation by removing speed barriers of traditional architectures.
2014 feb 24_big_datacongress_hadoopsession2_moderndataarchitectureAdam Muise
An introduction to Hadoop's core components as well as the core Hadoop use case: the Data Lake. This deck was delivered at Big Data Congress 2014 in Saint John, NB on Feb 24.
The document discusses the challenges of managing large volumes of data from different sources. Traditional approaches of separating data into isolated data silos are no longer effective. The emerging solution is to bring all data together into a unified platform like Hadoop that can store, process, and analyze large amounts of diverse data in a distributed manner. This allows organizations to gain deeper insights by asking new questions of all their combined data.
The document provides a summary of Venkata Naresh's career experience working as an SAP BW/BI Consultant over 5 years. It details his technical skills and responsibilities on various projects for clients like Adidas, Unilever, DSM, Kraft Foods, Shell Power & Gas, and Starwood Hotels & Resorts. His experiences include data modeling in HANA, ETL processes, building data flows, query development, system support and maintenance.
Big Data: selling the Business Case to the businessJ On The Beach
Big Data: selling the Business Case to the business by Eline Brandt & Javier de la Torre Medina
Big Data, every company loves the idea of it, but often, selling the Business Case is a challenge. So how to build a successful Business Case for your Big Data initiative for the Business Users? This presentation is based on the most common objections one gets, and how to deal with them. We'll go through one of my most surprising projects, look at the lessons learned and how can we optimize the Business Case?
The document discusses the challenges of dealing with large volumes of data from different sources. Traditional approaches of separating data into isolated silos are inadequate for analyzing today's vast amounts of data. The presenter argues that a better approach is to bring all available data together into a unified system so it can be analyzed and queried as a whole to generate useful insights. This approach treats all data as an integrated whole rather than separate, disconnected parts.
5 Tips to Building a Successful Big Data StrategyWestern Digital
Watch the full webinar here: http://bit.ly/1Yqr5Lz
Companies are seeking ways to leverage big data and analytics to improve business operations or create new revenue streams. But where do you begin? Join Janet George, SanDisk Chief Data Scientist, as she shares the biggest challenges companies face when first analyzing their data, common mistakes and 5 tips on how to build a successful big data strategy.
Santa Claus and other North Pole Inc. representatives are presenting at an ODI conference to discuss improving their toy delivery operations. They aim to make Christmas delivery more efficient, accurate, and safe by connecting legacy delivery databases, monitoring their fleet in real time, and increasing transparency. However, they face risks like errors, duplication, and overworked reindeer. Santa seeks help with managing their elvish supply chain, embracing new technologies like the Internet of Toys, and determining what IoT data can be shared openly or under limited access.
DATALYTICSOLUTIONS provides data science, machine learning, applied statistics, and data visualization services to help clients learn from their data. They have experience analyzing brain data from over 1,200 subjects and using deep learning to differentiate between those with and without Huntington's disease without explicit labels. They also helped a call center client optimize operations and improve employee satisfaction by answering questions about call patterns and volumes using statistical analysis. Their services include developing custom web applications and databases to meet specific client needs like an audit-ready compliance tracking system.
[DSC Europe 22] The (Swiss cheese) data conundrum: Sourcing, curating and int...DataScienceConferenc1
Companies focus a lot on developing models, while data is assumed to be accessible and complete. However, in reality, teams spend an inordinate amount of time and money sourcing, curating, integrating and processing data. Without that work, the data landscape looks more like a Swiss cheese, with different data gaps across datasets. Swiss Re Institute's Research Commercialization team combines internal data (from across the group's business units and functions) with a variety external datasets to plug data gaps and develop end-to-end risk views for impactful risk analytics and products. Introducing: "Company Hierarchy End 2 End Structure Extraction = C.H.E.E.S.E." Come to the talk to learn more and share your views on how to effectively empower business through quality data for impact.
Third presentation in our seminar on business intelligence dashboards. Derek Murphy works for National Grid and related learning points from over 30 years experience of delivering business intelligence projects
Presentation also available on YouTube https://www.youtube.com/watch?v=Er90qIA2S7U
The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
Working Capital Manager: Discover your hidden cash.
The "Working Capital Manager" enables you to find your hidden cash and reduce your working capital in a visual and intuitive way.
How will this tool help you?
- Get a clear overview of the components of your working capital
- Save time on reporting & perform root cause analyses in a few clicks
- Follow up payment behaviour and inventory rotation
- Simulate your actions and meet your working capital target
How does it work?
The Working Capital Manager simply connects to your ERP system (SAP, NAV...): the implementation is done in 3-6 days.
Look for more information on our website www.discover-edge.com or the special product website www.workingcapitalmanager.com.
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Good decisions make great companies. That's why the data-driven mantra keeps gaining momentum. Increasingly, smart business people are taking a data-first approach for both strategic planning and tactical decision-making. They spend ample time exploring their data to better understand their options. In doing so, they capitalize on real opportunities, while avoiding low-value projects.
Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor explain why a data-first mindset can help companies optimize their resources and thus make better decisions. He'll be briefed by Rishi Patel and Erin Haselkorn of
The Briefing Room with Dr. Robin Bloor and Experian
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Anzo Smart Data Lake 4.0 - a Data Lake Platform for the Enterprise Informatio...Cambridge Semantics
Only with a rich and interactive semantic layer can your data and analytics stack deliver true on-demand access to data, answers and insights - weaving data together from across the enterprise into an information fabric. In this webinar we introduce Anzo Smart Data Lake 4.0, which provides that rich and interactive semantic layer to your data.
This document provides an introduction and overview of a course on data warehousing. It lists reference books and additional materials for the course. It then summarizes the course topics, which include introduction and background, de-normalization, OLAP, dimensional modeling, ETL, data quality management, performance techniques, data mining, implementation steps, a case study, lab usage, and others. It also describes a semester project where students will develop a data warehouse application for an organization and outlines what should be included in the project report.
This document provides an overview of IBM's Watson cognitive computing system. It discusses Watson's development from an IBM research project that won Jeopardy! in 2011 to a growing commercial enterprise. Key points include:
- Watson understands natural language, generates hypotheses, finds and evaluates evidence from massive amounts of data to answer questions.
- Watson combines machine learning, natural language processing, information retrieval, knowledge representation and reasoning algorithms.
- Watson is helping usher in a new "third era" of computing focused on cognitive systems that can perceive, reason, learn and interact with humans more naturally.
1. Introduction and how to get into Data
2. Data Engineering and skills needed
3. Comparison of Data Analytics for statistic and real time streaming data
4. Bayesian Reasoning for Data
This document provides an extract from a statistical thinking course offered by Red Olive, including an overview of the course contents and modeling techniques. The 2-day course covers topics such as the CRISP-DM process, reporting versus modeling, basic statistical analysis, and best practices for sharing results. Attendees will learn how to get their data to speak through statistical analysis and translating findings into actionable business insights.
This document provides an overview and agenda for Week 8 of the Data Scientist Enablement (DSE) 400 program. It outlines the week's discussions on ethics in big data, recommended learning materials, activities including exploring datasets and starting a blog, and an assignment to cleanse and visualize a sensor dataset or complete an alternative task. The timeline for the full DSE program and options for adaptive learning and proficiency certification are also summarized.
Teaching cognitive computing with ibm watsondiannepatricia
Ralph Badinelli, Lenz Chair in the Department of Business Information Technology, Pamplin College of Business of Virginia Tech. presented "Teaching Cognitive Computing with IBM Watson" as part of the Cognitive Systems Institute Speaker Series.
Cognitive systems institute talk 8 june 2017 - v.1.0diannepatricia
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Watch the full webinar here: http://bit.ly/1Yqr5Lz
Companies are seeking ways to leverage big data and analytics to improve business operations or create new revenue streams. But where do you begin? Join Janet George, SanDisk Chief Data Scientist, as she shares the biggest challenges companies face when first analyzing their data, common mistakes and 5 tips on how to build a successful big data strategy.
Santa Claus and other North Pole Inc. representatives are presenting at an ODI conference to discuss improving their toy delivery operations. They aim to make Christmas delivery more efficient, accurate, and safe by connecting legacy delivery databases, monitoring their fleet in real time, and increasing transparency. However, they face risks like errors, duplication, and overworked reindeer. Santa seeks help with managing their elvish supply chain, embracing new technologies like the Internet of Toys, and determining what IoT data can be shared openly or under limited access.
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[DSC Europe 22] The (Swiss cheese) data conundrum: Sourcing, curating and int...DataScienceConferenc1
Companies focus a lot on developing models, while data is assumed to be accessible and complete. However, in reality, teams spend an inordinate amount of time and money sourcing, curating, integrating and processing data. Without that work, the data landscape looks more like a Swiss cheese, with different data gaps across datasets. Swiss Re Institute's Research Commercialization team combines internal data (from across the group's business units and functions) with a variety external datasets to plug data gaps and develop end-to-end risk views for impactful risk analytics and products. Introducing: "Company Hierarchy End 2 End Structure Extraction = C.H.E.E.S.E." Come to the talk to learn more and share your views on how to effectively empower business through quality data for impact.
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Good decisions make great companies. That's why the data-driven mantra keeps gaining momentum. Increasingly, smart business people are taking a data-first approach for both strategic planning and tactical decision-making. They spend ample time exploring their data to better understand their options. In doing so, they capitalize on real opportunities, while avoiding low-value projects.
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This document provides an introduction and overview of a course on data warehousing. It lists reference books and additional materials for the course. It then summarizes the course topics, which include introduction and background, de-normalization, OLAP, dimensional modeling, ETL, data quality management, performance techniques, data mining, implementation steps, a case study, lab usage, and others. It also describes a semester project where students will develop a data warehouse application for an organization and outlines what should be included in the project report.
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I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
"Natural Language Access to Data: Where Reasoning Makes Sense"
1. Natural Language Access to Data:
Where Reasoning Makes Sense
Richard Waldinger
Artificial Intelligence Center
SRI International
Cognitive Science Institute
Speaker Series
7 April 2016
1
2. natural language access to data
joint work
Cleo Condoravdi, Stanford University
Kyle Richardson, Stuttgart
University
Asuman Suenbuel, SAP
Vishal Sikka, SAP (now Infosys)
2
3. natural language access to data
the problem
accessing knowledge
from structured data sources.
via questions in natural
language.
3
4. natural language access to data
why is this hard?
natural language uncontrolled.
we want answers, not websites.
answers deduced or computed.
multiple databases.
sequence of ongoing queries.
4
5. natural language access to data
what makes it easier?
we restrict ourselves to a well-understood
subject domain.
business enterprise
we use already known databases.
access to SAP’s HANA database.
“Quest”
5
6. waldinger natural language access to datawaldinger natural language access to data
sample query sequence
Show a company with a long-term debt within
the last two years.
The debt is more than 5 million Euros.
It must be Swiss.
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7. waldinger natural language access to datawaldinger natural language access to data
why does this require reasoning?
query may be logically complex.
to resolve ambiguities in the query.
differences in vocabularies.
bridge the inferential leap.
compose the answer.
7
8. waldinger natural language access to datawaldinger natural language access to data
approach (nl+deduction)
semantic parsing ⇒ semantic representation
transform ⇒ logical form
proof ⇒ answers
proof conducted in an axiomatic theory
theory contains links to databases.
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9. waldinger natural language access to datawaldinger natural language access to data
implementation of Quest
natural language processing by SAPL
(Cascade Parser)
reasoning by SRI’s SNARK.
data from SAP’s HANA, Currency
Conversion, Nationality Tables, etc.
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11. waldinger natural language access to datawaldinger natural language access to data
axiomatic subject domain theory
defines concepts in queries.
expresses capabilities of the databases.
provides background knowledge to relate
them.
sort (type) structure
axioms
11
12. waldinger question answering/ deductionwaldinger question answering/ deduction
sort structure
entity
agent
company
time interval
debt
number
money
size
12
14. waldinger natural language access to datawaldinger natural language access to data
parsing
based on PARC natural language
technology (XLE + Bridge)
new parser (SAPL) written for Quest.
parser knows sort structure and sorts of
relations.
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15. waldinger question answering/ deductionwaldinger question answering/ deduction
semantic parsing
query: Show a company with a high debt
within the last two years.
semantic representation (partial):
(quant exists company7 sort company)
(quant exists debt3 sort debt)
(scopes-over company7 debt3)
(in nscope debt3
(company-has-debt company7 debt3))
15
17. waldinger question answering/ deductionwaldinger question answering/ deduction
axiom: definition of high debt
high(debt-record(?company, ?money,…))
⇔
?money > dollars(1000000)
i.e., a debt is high if its money amount is
greater than 1 million dollars.
17
19. waldinger nl access to datawaldinger nl access to data
sample data
name money location date
SL Foods Inc. $105263551.70 CH 2007 Sept. 1
19
name: SL Foods Inc.
amount of debt: $105,263,551.70.
date debt incurred: Sept 1, 2007.
nationality: CH (Switzerland)
...
20. waldinger question answering/ deductionwaldinger question answering/ deduction
the answer(s)
the debt of sl food inc. is high,
the debt of sl food inc. is within the interval
from 9/1/2006 to 9/1/2008,
the duration of the interval from 9/1/2006
to 9/1/2008 is 2 years,
the interval from 9/1/2006 to 9/1/2008 is
last.
20
21. waldinger question answering/ deductionwaldinger question answering/ deduction
reasoning resolves ambiguity.
Show me a client with a high debt.
It was within the last 2 years.
(“It” must be the debt).
It should be Swiss.
(“It” must be the client)
21
22. waldinger question answering/ deductionwaldinger question answering/ deduction
crowd-sourced axiomatic theories
we currently translate english
questions into logical form.
we could also translate declarative
sentences into logical form.
develop axiomatic theory from text.
domain experts need not know logic.
22
23. waldinger question answering/ deductionwaldinger question answering/ deduction
other future work
other domains.
spoken input.
efficiency.
changing data bases.
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24. waldinger question answering/ deductionwaldinger question answering/ deduction
reference
Natural Language Access to Data:
It Takes Common Sense
AAAI Symposium:
Logical Formalizations of
Common Sense Reasoning
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