scholarly journals Domain-Targeted, High Precision Knowledge Extraction

2017 ◽  
Vol 5 ◽  
pp. 233-246 ◽  
Author(s):  
Bhavana Dalvi Mishra ◽  
Niket Tandon ◽  
Peter Clark
Keyword(s):  
Information Extraction ◽  
Knowledge Base ◽  
High Precision ◽  
Elementary Science ◽  
Question Answering ◽  
Learning Algorithm ◽  
Knowledge Extraction ◽  
Schema Learning ◽  
Named Entity ◽  
The World

Our goal is to construct a domain-targeted, high precision knowledge base (KB), containing general (subject,predicate,object) statements about the world, in support of a downstream question-answering (QA) application. Despite recent advances in information extraction (IE) techniques, no suitable resource for our task already exists; existing resources are either too noisy, too named-entity centric, or too incomplete, and typically have not been constructed with a clear scope or purpose. To address these, we have created a domain-targeted, high precision knowledge extraction pipeline, leveraging Open IE, crowdsourcing, and a novel canonical schema learning algorithm (called CASI), that produces high precision knowledge targeted to a particular domain - in our case, elementary science. To measure the KB’s coverage of the target domain’s knowledge (its “comprehensiveness” with respect to science) we measure recall with respect to an independent corpus of domain text, and show that our pipeline produces output with over 80% precision and 23% recall with respect to that target, a substantially higher coverage of tuple-expressible science knowledge than other comparable resources. We have made the KB publicly available.

scholarly journals NELL’s subcategories from a question answering environment

2018 ◽  
Author(s):  
Wesley W. O. Souza ◽  
Diorge Brognara ◽  
João A. Leite ◽  
Estevam R. Hruschka Jr.
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Data Storage ◽  
Language Processing ◽  
Processing Speed ◽  
Question Answering ◽  
Learning Algorithm ◽  
Conversational Agents ◽  
Learning Agent ◽  
The Web

With advances in machine learning, natural language processing, processing speed, and amount of data storage, conversational agents are being used in applications that were not possible to perform within a few years. NELL, a machine learning agent who learns to read the web, today has a considerably large ontology and while it can be used for multiple fact queries, it is also possible to expand it further and specialize its knowledge. One of the first steps to succeed is to refine existing knowledge in NELL’s knowledge base so that future communication between it and humans is as natural as possible. This work describes the results of an experiment where we investigate which machine learning algorithm performs best in the task of classifying candidate words to subcategories in the NELL knowledge base.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Impact of Day Care on the Child

PEDIATRICS ◽  
1993 ◽  
Vol 91 (1) ◽  
pp. 225-228
Author(s):  
Bettye M. Caldwell
Keyword(s):  
Child Care ◽  
Knowledge Base ◽  
Day Care ◽  
Conceptual Structure ◽  
Child Characteristics ◽  
Care Research ◽  
The Family ◽  
The World ◽  
The Status ◽  
Care Family

In the world of day-care research, the status of our knowledge is sufficiently shaky that we must continue to keep an open mind about the service. The knowledge base is growing rapidly, but the conceptual structure that supports it is flimsy and insubstantial. Fortunately, current research efforts are improving this situation. Regardless of whether we like or dislike day care, it is, like the family, here to stay. That realization alone should strengthen our resolve not to compromise on the type of service we create. We have to continue to identify parameters of quality and become good matchmakers in terms of child care, family, and child characteristics. Through such efforts, a network of educare programs that will foster favorable development in children can become a national and global reality.

scholarly journals Use of Linked Data principles for semantic management of scanned documents

2016 ◽  
Vol 28 (2) ◽  
pp. 241-251 ◽  
Author(s):  
Luciane Lena Pessanha Monteiro ◽  
Mark Douglas de Azevedo Jacyntho
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Web Application ◽  
Linked Data ◽  
World Wide ◽  
Decision Making Process ◽  
Whole Process ◽  
The World ◽  
Scanned Documents ◽  
The Web

The study addresses the use of the Semantic Web and Linked Data principles proposed by the World Wide Web Consortium for the development of Web application for semantic management of scanned documents. The main goal is to record scanned documents describing them in a way the machine is able to understand and process them, filtering content and assisting us in searching for such documents when a decision-making process is in course. To this end, machine-understandable metadata, created through the use of reference Linked Data ontologies, are associated to documents, creating a knowledge base. To further enrich the process, (semi)automatic mashup of these metadata with data from the new Web of Linked Data is carried out, considerably increasing the scope of the knowledge base and enabling to extract new data related to the content of stored documents from the Web and combine them, without the user making any effort or perceiving the complexity of the whole process.

scholarly journals QA4IE: A Question Answering Based Framework for Information Extraction

2018 ◽  
pp. 198-216 ◽  
Author(s):  
Lin Qiu ◽  
Hao Zhou ◽  
Yanru Qu ◽  
Weinan Zhang ◽  
Suoheng Li ◽  
...  
Keyword(s):  
Information Extraction ◽  
Question Answering
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