Knowledge Graph Platform with Percuro - Revision history

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Dipesh: /* Toolkit */

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‎Toolkit

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‎Collaborations

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‎Background and Motivation

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‎Background and Motivation

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‎Background and Motivation

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‎Toolkit

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‎Toolkit

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‎People

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‎GitHub

← Older revision		Revision as of 13:59, 31 May 2022
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−	~~=Background and Motivation=~~
−	~~'''Knowledge Graph''' (KG) is an encapsulation of structured knowledge in a graphical representation & used for a variety of information processing and management tasks such as~~
−	* Data & knowledge integration from diverse sources
−	* Improve automation
−	* Enabling new generation of applications
−	* Empowering machine learning (ML) & NLP techniques with domain knowledge
−	~~and applications such as question answering, summarization, text simplification, and Named Entity Recognition (NER).~~

−	~~Most existing KG platforms & tools are limited in~~
−	* Provenance
−	* Dynamicity (ie: static schema vs schema generation)
−	* Temporal
−	* Domain specificity
−	* Modularity
−
−	~~The '''AIISC Knowledge Graph''' (KG) effort involves the development of a comprehensive tool and platform for KG development with the following aims~~
−
−	~~1. Develop a KG development platform capable of instantiating KGs in any domains from '''structured''', '''semi-structured''', and '''unstructured''' data:~~
−	** Biomedical & pharmaceutical domain with '''Percuro'''
−
−	~~2. Improve & address the limitations of existing KG platforms~~
−
−	~~3. Constructs a Knowledge Graph (based on a combination of)~~
−	** Enrich an existing Knowledge Graph '''(Top-down declarative)'''
−	** Construct a Knowledge Graph out of given entities '''(Bottom-up data driven)'''
−
−	~~=Goals & Use-Cases=~~
−	~~The goals of KGs are to provide~~
−	* Contextualization
−	** [https://www.google.com/url?q=https://www.semanticscholar.org/paper/Context-Enriched-Learning-Models-for-Aligning-in/650e79de9f4a4a123d559240387db0e3c3d1f867&sa=D&source=editors&ust=1654007896106276&usg=AOvVaw2iUUH6T-CCQl6mV6oNlmMN Context-Enriched Learning Models for Aligning Biomedical Vocabularies in the UMLS Metathesaurus]
−	* Personalization
−	* Abstraction
−	* Explainability
−
−	~~=Collaborations=~~
−	~~'''Percuro''' is a collaborative research project involving WIPRO, The AI Institute at University of South Carolina (AIISC), and IIT-Patna~~
−	(IIT-P). It involves development of semantic (i.e., knowledge graph enhanced) approach to natural language processing (NLP), natural language generation (NLG) and natural language understanding (NLU) targeted at the pharmaceutical domain. It will involve techniques for NLP/NLG/NLU on biomedical and clinical documents relevant to pharmaceutical markets.
−
−	'''Percuro''' aims to solve tasks such as (a) text simplification, (b) summarization and (c) question answering. These are tasks that are not straightforward and require more information that what the text provides.
−
−	~~'''An example:''' Which hormone reduces blood sugar level?~~
−
−	~~This question requires additional context on what the word hormone means before finding an answer.~~
−
−	~~=Overview=~~
−	~~<html>~~
−	~~<center>~~
−	<iframe src="https://docs.google.com/presentation/d/1EOqyl5fK6tUecrjOpslcd9IY8US3fGqKgRZQIttGD3A/embed?start=false&loop=false&delayms=3000" frameborder="0" width="600" height="375" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"></iframe>
−	~~</center>~~
−	~~</html>~~
−
−	~~=Toolkit=~~
−
−	~~The Knowledge Graph Toolkit with Percuro V1.0 currently supports knowledge sources from:~~
−	* PharmKG (base)
−	* Open-domain: '''DBpedia''' & '''Wikidata'''
−	* iomedical & pharmaceutical domain: '''Drugbank''' & '''UMLS'''
−
−	~~and has an extensive coverage over the domains of '''Drugs''', '''Chemicals''', '''Diseases''' entities and heir associated relations:~~
−	* '''Chemical''' Properties: drug interactions, diseases cured, etc.
−	* '''Physical''' Properties: lethal dosage, boiling point, pressure, solubility, etc
−	* '''Disease''': symptoms, treatments, differential diagnosis, etc.
−	* '''Aliases''': Common names, chemical names and external identifiers.
−
−	~~=GitHub=~~
−	* [https://github.com/Anirudh-Sundar/APGC AIISC Knowledge Graph Development Tool]
−
−	~~=Demo=~~
−	~~<embedvideo service="youtube">https://www.youtube.com/watch?v=ggvfAo-yp5g</embedvideo>~~
−
−	~~=People=~~
−	*'''Artificial Intelligence Institute, University of South Carolina'''
−	**[https://www.linkedin.com/in/joeyyip/ Hong Yung (Joey) Yip]
−	**[https://www.linkedin.com/in/thilini-w/ Thilini Wijesiriwardene]
−	**[https://sc.edu/study/colleges_schools/engineering_and_computing/faculty-staff/amitsheth.php Dr. Amit P. Sheth]
−
−	*'''WiPRO'''
−	**[http://www.amitavadas.com/ Amitava Das]

@@ Line 63: / Line 63: @@
 * '''Aliases''': Common names, chemical names and external identifiers.
-'''GitHub'''
+=GitHub=
 * [https://github.com/Anirudh-Sundar/APGC AIISC Knowledge Graph Development Tool]

@@ Line 37: / Line 37: @@
 (IIT-P). It involves development of semantic (i.e., knowledge graph enhanced) approach to natural language processing (NLP), natural language generation (NLG) and natural language understanding (NLU) targeted at the pharmaceutical domain. It will involve techniques for NLP/NLG/NLU on biomedical and clinical documents relevant to pharmaceutical markets.
-Percuro aims to solve tasks such as (a) text simplification, (b) summarization and (c) question answering. These are tasks that are not straightforward and require more information that what the text provides.
+'''Percuro''' aims to solve tasks such as (a) text simplification, (b) summarization and (c) question answering. These are tasks that are not straightforward and require more information that what the text provides.
 '''An example:''' Which hormone reduces blood sugar level?

@@ Line 5: / Line 5: @@
 * Enabling new generation of applications
 * Empowering machine learning (ML) & NLP techniques with domain knowledge
-and applications such as
+and applications such as question answering, summarization, text simplification, and Named Entity Recognition (NER).
-* Question answering
-* Summarization
-* Text simplification
-* Named Entity Recognition (NER)
 Most existing KG platforms & tools are limited in

@@ Line 20: / Line 20: @@
 The '''AIISC Knowledge Graph''' (KG) effort involves the development of a comprehensive tool and platform for KG development with the following aims
-. Develop a KG development platform capable of instantiating KGs in any domains:
+. Develop a KG development platform capable of instantiating KGs in any domains from '''structured''', '''semi-structured''', and '''unstructured''' data:
 ** Biomedical & pharmaceutical domain with '''Percuro'''

@@ Line 22: / Line 22: @@
 . Develop a KG development platform capable of instantiating KGs in any domains:
 ** Biomedical & pharmaceutical domain with '''Percuro'''
 . Improve & address the limitations of existing KG platforms
 . Constructs a Knowledge Graph (based on a combination of)
 ** Enrich an existing Knowledge Graph '''(Top-down declarative)'''

@@ Line 60: / Line 60: @@
 and has an extensive coverage over the domains of '''Drugs''', '''Chemicals''', '''Diseases''' entities and heir associated relations:
-'''Chemical''' Properties: drug interactions, diseases cured, etc.
+* '''Chemical''' Properties: drug interactions, diseases cured, etc.
-'''Physical''' Properties: lethal dosage, boiling point, pressure, solubility, etc
+* '''Physical''' Properties: lethal dosage, boiling point, pressure, solubility, etc
-'''Disease''': symptoms, treatments, differential diagnosis, etc.
+* '''Disease''': symptoms, treatments, differential diagnosis, etc.
-'''Aliases''': Common names, chemical names and external identifiers.
+* '''Aliases''': Common names, chemical names and external identifiers.
 '''GitHub'''

← Older revision		Revision as of 13:54, 31 May 2022
Line 53:		Line 53:

	=Toolkit=		=Toolkit=
		+
		+	The Knowledge Graph Toolkit with Percuro V1.0 currently supports knowledge sources from:
		+	* PharmKG (base)
		+	* Open-domain: '''DBpedia''' & '''Wikidata'''
		+	* iomedical & pharmaceutical domain: '''Drugbank''' & '''UMLS'''
		+
		+	and has an extensive coverage over the domains of '''Drugs''', '''Chemicals''', '''Diseases''' entities and heir associated relations:
		+	'''Chemical''' Properties: drug interactions, diseases cured, etc.
		+	'''Physical''' Properties: lethal dosage, boiling point, pressure, solubility, etc
		+	'''Disease''': symptoms, treatments, differential diagnosis, etc.
		+	'''Aliases''': Common names, chemical names and external identifiers.

	'''GitHub'''		'''GitHub'''

← Older revision		Revision as of 13:52, 31 May 2022
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	**[https://www.linkedin.com/in/thilini-w/ Thilini Wijesiriwardene]		**[https://www.linkedin.com/in/thilini-w/ Thilini Wijesiriwardene]
	**[https://sc.edu/study/colleges_schools/engineering_and_computing/faculty-staff/amitsheth.php Dr. Amit P. Sheth]		**[https://sc.edu/study/colleges_schools/engineering_and_computing/faculty-staff/amitsheth.php Dr. Amit P. Sheth]
		+
		+	*'''WiPRO'''
		+	**[http://www.amitavadas.com/ Amitava Das]