Ontotext is a company established in 2000 that specializes in semantic web technologies and AI. It has attracted over 15 million euros in research funding and works with top pharmaceutical companies. The document discusses challenges in processing clinical text like a lack of multilingual medical terminologies and ontologies. It presents various clinical ontologies and knowledge bases that can help with tasks like concept normalization, data integration, and predictive modeling. The company has developed technologies for processing clinical text, linking data to ontologies, and building predictive models for adverse drug reactions.
DSS Ontotext Webinar -Examode: Extreme-scale text-based classification of med...SvetlaBoytcheva
The second meet-up from the Data science society meet-ups 2021 is under the topic of Healthcare!
Text classification is the process of applying pre-defined categories to certain portion of the data . Classes are selected from pre-selected taxonomy , which are later used for the purposes of built models.
During the event you will have a chance to learn about:
a solution , based on deep learning methods of the text-based classification problem, for the SNOMED CT, that contains more than 350,000 concepts.
This is an extreme scale multiclass multi label classification task
The importance of this task is hidden in the fact, that it will allow automatic processing of the clinical narratives and normalization of the textual descriptions of disorders, procedures and finding into medical standard classification.
Making the most of phenotypes in ontology-based biomedical knowledge discoveryMichel Dumontier
A phenotype is an observable characteristic of an individually and typically pertains to its morphology, function, and behavior. Phenotypes, whether observed at the bench or the bedside, are increasingly being used to gain insight into the diagnosis, mechanism, and treatment of disease. A key aspect of these approaches involve comparing phenotypes that are defined in multiple terminologies that often cater to altogether different organisms, such as mice and humans. In this seminar, I will discuss computational approaches for harmonizing and utilizing phenotypes for translational research. We will examine case studies which involve the computation of semantic similarity including the use of phenotypes to inform clinical diagnosis of rare diseases, to identify human drug targets using mice knock-out models, and to explore phenotype-based approaches for drug repositioning .
Envisioning a world where everyone helps solve diseasemhaendel
Keynote presented at the Semantic Web for Life Sciences conference in Cambridge, UK, December 9th, 2015
http://www.swat4ls.org/
The talk focuses on the use of ontologies for data integration to support rare disease diagnostics, and how so very many people unbeknownst to the patient or even to the researchers creating the data are involved in a diagnosis.
The Monarch Initiative: From Model Organism to Precision Medicinemhaendel
NIH BD2K all-hands meeting poster November 12, 2015.
Attempts at correlating phenotypic aspects of disease with causal genetic influences are often confounded by the challenges of interpreting diverse data distributed across numerous resources. New approaches to data modeling, integration, tooling, and community practices are needed to make efficient use of these data. The Monarch Initiative is an international consortium working on the development of shared data, tools, and standards to enable direct translation of integrated genotype, phenotype, and environmental data from human and model organisms to enhance our understanding of human disease. We utilize sophisticated semantic mapping techniques across a diverse set of standardized ontologies to deeply integrate data across species, sources, and modalities. Using phenotype similarity matching algorithms across these data enables disorder prediction, variant prioritization, and patient matching against known diseases and model organisms. These similarity algorithms form the core of several innovative tools. The Exomiser, which enables exome variant prioritization by combining pathogenicity, frequency, inheritance, protein interaction, and cross-species phenotype data. Our Phenotype Sufficiency tool provides clinicians the ability to compare patient phenotypic profiles using the Human Phenotype Ontology to determine uniqueness and specificity in support of variant prioritization. The PhenoGrid visualization widget illustrates phenotype similarity between patients, known diseases, and model organisms. Monarch develops models in collaboration with the community in support of the burgeoning genotype-phenotype disease research community. We have successfully used Exomiser to solve a number of undiagnosed patient cases in collaboration with the NIH Undiagnosed Disease Program. Ongoing development in coordination with the Global Alliance for Genetic Health (GA4GH) and other groups will catalyze the realization of our goal of a vital translational community focused on the collaborative application of integrated genotype, phenotype, and environmental data to human disease.
Guided visual exploration of patient stratifications in cancer genomicsNils Gehlenborg
Talk presented at the "Beyond the Genome 2014: Cancer Genomics" conference (10 October 2014)
http://www.beyond-the-genome.com/2014/
Cancer is a heterogeneous disease, and molecular profiling of tumors from large cohorts has enabled characterization of new tumor subtypes. This is a prerequisite for improving personalized treatment and ultimately better patient outcomes. Potential tumor subtypes can be identified with methods such as unsupervised clustering or network-based stratification, which assign patients to sets based on high-dimensional molecular profiles. Detailed characterization of identified sets and their interpretation, however, remain a time-consuming exploratory process.
To address these challenges, we have developed StratomeX (http://stratomex.caleydo.org), an interactive visualization tool that complements algorithmic approaches. StratomeX also integrates a computational framework for query-based guided exploration directly into the visualization, enabling discovery of novel relationships between patient sets and efficient generation and refinement of hypotheses about tumor subtypes. StratomeX enables analysts to efficiently compare multiple patient stratifications, to correlate patient sets with clinical information or genomic alterations, and to view the differences between molecular profiles across patient sets.
DSS Ontotext Webinar -Examode: Extreme-scale text-based classification of med...SvetlaBoytcheva
The second meet-up from the Data science society meet-ups 2021 is under the topic of Healthcare!
Text classification is the process of applying pre-defined categories to certain portion of the data . Classes are selected from pre-selected taxonomy , which are later used for the purposes of built models.
During the event you will have a chance to learn about:
a solution , based on deep learning methods of the text-based classification problem, for the SNOMED CT, that contains more than 350,000 concepts.
This is an extreme scale multiclass multi label classification task
The importance of this task is hidden in the fact, that it will allow automatic processing of the clinical narratives and normalization of the textual descriptions of disorders, procedures and finding into medical standard classification.
Making the most of phenotypes in ontology-based biomedical knowledge discoveryMichel Dumontier
A phenotype is an observable characteristic of an individually and typically pertains to its morphology, function, and behavior. Phenotypes, whether observed at the bench or the bedside, are increasingly being used to gain insight into the diagnosis, mechanism, and treatment of disease. A key aspect of these approaches involve comparing phenotypes that are defined in multiple terminologies that often cater to altogether different organisms, such as mice and humans. In this seminar, I will discuss computational approaches for harmonizing and utilizing phenotypes for translational research. We will examine case studies which involve the computation of semantic similarity including the use of phenotypes to inform clinical diagnosis of rare diseases, to identify human drug targets using mice knock-out models, and to explore phenotype-based approaches for drug repositioning .
Envisioning a world where everyone helps solve diseasemhaendel
Keynote presented at the Semantic Web for Life Sciences conference in Cambridge, UK, December 9th, 2015
http://www.swat4ls.org/
The talk focuses on the use of ontologies for data integration to support rare disease diagnostics, and how so very many people unbeknownst to the patient or even to the researchers creating the data are involved in a diagnosis.
The Monarch Initiative: From Model Organism to Precision Medicinemhaendel
NIH BD2K all-hands meeting poster November 12, 2015.
Attempts at correlating phenotypic aspects of disease with causal genetic influences are often confounded by the challenges of interpreting diverse data distributed across numerous resources. New approaches to data modeling, integration, tooling, and community practices are needed to make efficient use of these data. The Monarch Initiative is an international consortium working on the development of shared data, tools, and standards to enable direct translation of integrated genotype, phenotype, and environmental data from human and model organisms to enhance our understanding of human disease. We utilize sophisticated semantic mapping techniques across a diverse set of standardized ontologies to deeply integrate data across species, sources, and modalities. Using phenotype similarity matching algorithms across these data enables disorder prediction, variant prioritization, and patient matching against known diseases and model organisms. These similarity algorithms form the core of several innovative tools. The Exomiser, which enables exome variant prioritization by combining pathogenicity, frequency, inheritance, protein interaction, and cross-species phenotype data. Our Phenotype Sufficiency tool provides clinicians the ability to compare patient phenotypic profiles using the Human Phenotype Ontology to determine uniqueness and specificity in support of variant prioritization. The PhenoGrid visualization widget illustrates phenotype similarity between patients, known diseases, and model organisms. Monarch develops models in collaboration with the community in support of the burgeoning genotype-phenotype disease research community. We have successfully used Exomiser to solve a number of undiagnosed patient cases in collaboration with the NIH Undiagnosed Disease Program. Ongoing development in coordination with the Global Alliance for Genetic Health (GA4GH) and other groups will catalyze the realization of our goal of a vital translational community focused on the collaborative application of integrated genotype, phenotype, and environmental data to human disease.
Guided visual exploration of patient stratifications in cancer genomicsNils Gehlenborg
Talk presented at the "Beyond the Genome 2014: Cancer Genomics" conference (10 October 2014)
http://www.beyond-the-genome.com/2014/
Cancer is a heterogeneous disease, and molecular profiling of tumors from large cohorts has enabled characterization of new tumor subtypes. This is a prerequisite for improving personalized treatment and ultimately better patient outcomes. Potential tumor subtypes can be identified with methods such as unsupervised clustering or network-based stratification, which assign patients to sets based on high-dimensional molecular profiles. Detailed characterization of identified sets and their interpretation, however, remain a time-consuming exploratory process.
To address these challenges, we have developed StratomeX (http://stratomex.caleydo.org), an interactive visualization tool that complements algorithmic approaches. StratomeX also integrates a computational framework for query-based guided exploration directly into the visualization, enabling discovery of novel relationships between patient sets and efficient generation and refinement of hypotheses about tumor subtypes. StratomeX enables analysts to efficiently compare multiple patient stratifications, to correlate patient sets with clinical information or genomic alterations, and to view the differences between molecular profiles across patient sets.
Artifical intelligence in medical diagnostics 2019 patent landscape flyerKnowmade
Report’s Key Features
• PDF >170 slides
• Excel file >22,600 patents
• IP trends, including time-evolution of published patents, and countries of patent filings
• Ranking of main patent assignees
• Identifications of over 90 start-up firms and IP newcomers
• Summary of the IP related to the medical exams: ECG, EEG, EMG, MRI, CT scan, PET scan, facial analysis, speech analysis, OCT, etc.
• Summary of the IP related to the clinical areas involved: Cardiology, Oncology, Diabetes, Osteology, etc.
• Key patents & main litigations analysis
• Excel database containing all patents analyzed in this report, including technology and application segmentations
An overview over the use of AI for medical research and health care and the ethical and sustainability issues that arise in this context. Based on a lecture at the EUGLOH summer school "Artificial Intelligence" on 2022-07-07.
Overview of Estonian opportunities to move towards more personalized medicine with help of Estonian Biobank, national E-Health solutions and state-wide information exchange framework.
Presented at international conference "E-health - integration of IT and medicine" in Tartu 15.09.2013. http://mug.ee/ehealth/
Biomedical natural language processing in drug developmentSonja Aits
A lecture about natural language processing and its applications in drug development. Given on 2022-10-17 for the MSc course "Artificial Intelligence in Drug Discovery" at Uppsala University, Sweden.
5 Cutting-Edge Trends in Molecular DiagnosticsBruce Carlson
Despite the focus on novelty in this field, it is near 2 decades old. Yet a lot is changing. A look at a few trends that could change molecular diagnostics.
The benefits of patient involvement in research and development (RE:ACT Congr...jangeissler
Presentation of Jan Geissler, Director EUPATI and Co-Founder CML Advocates Network, about the benefits of involving patients in research and development, and about EUPATI. Held at RE:ACT Conress 2016 on Research of Rare and Orphan Diseases, organized by the Blackswan Foundation on 12 March 2016 in Barcelona, Spain
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
di Riccardo Bellazzi
Università di Pavia
ICS Maugerio Pavia
Slide per l'incontro dal titolo "Big data, machine learning e medicina di precisione."
10 maggio 2018, Milano, Fondazione Giannino Bassetti
Video integrale: https://www.fondazionebassetti.org/it/focus/2018/08/big_data_machine_learning_e_me.html
Haapalinna the new modalities ecosystem project what is there for meAntti Haapalinna
The aim of this New Modalities Ecosystem is enable improved understanding of disease pathology related to the symptoms and disease progression and better treatments by applying large molecular drugs and diagnostic tools as well as digital wearable patient tools for disease symptom recording, to have real world evidence for treatment efficacy
this show talks about some new technologies in medicine including visual reality , some mobile medical apps , and few about databases
this focuses more on the pharmacology.
Artifical intelligence in medical diagnostics 2019 patent landscape flyerKnowmade
Report’s Key Features
• PDF >170 slides
• Excel file >22,600 patents
• IP trends, including time-evolution of published patents, and countries of patent filings
• Ranking of main patent assignees
• Identifications of over 90 start-up firms and IP newcomers
• Summary of the IP related to the medical exams: ECG, EEG, EMG, MRI, CT scan, PET scan, facial analysis, speech analysis, OCT, etc.
• Summary of the IP related to the clinical areas involved: Cardiology, Oncology, Diabetes, Osteology, etc.
• Key patents & main litigations analysis
• Excel database containing all patents analyzed in this report, including technology and application segmentations
An overview over the use of AI for medical research and health care and the ethical and sustainability issues that arise in this context. Based on a lecture at the EUGLOH summer school "Artificial Intelligence" on 2022-07-07.
Overview of Estonian opportunities to move towards more personalized medicine with help of Estonian Biobank, national E-Health solutions and state-wide information exchange framework.
Presented at international conference "E-health - integration of IT and medicine" in Tartu 15.09.2013. http://mug.ee/ehealth/
Biomedical natural language processing in drug developmentSonja Aits
A lecture about natural language processing and its applications in drug development. Given on 2022-10-17 for the MSc course "Artificial Intelligence in Drug Discovery" at Uppsala University, Sweden.
5 Cutting-Edge Trends in Molecular DiagnosticsBruce Carlson
Despite the focus on novelty in this field, it is near 2 decades old. Yet a lot is changing. A look at a few trends that could change molecular diagnostics.
The benefits of patient involvement in research and development (RE:ACT Congr...jangeissler
Presentation of Jan Geissler, Director EUPATI and Co-Founder CML Advocates Network, about the benefits of involving patients in research and development, and about EUPATI. Held at RE:ACT Conress 2016 on Research of Rare and Orphan Diseases, organized by the Blackswan Foundation on 12 March 2016 in Barcelona, Spain
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
di Riccardo Bellazzi
Università di Pavia
ICS Maugerio Pavia
Slide per l'incontro dal titolo "Big data, machine learning e medicina di precisione."
10 maggio 2018, Milano, Fondazione Giannino Bassetti
Video integrale: https://www.fondazionebassetti.org/it/focus/2018/08/big_data_machine_learning_e_me.html
Haapalinna the new modalities ecosystem project what is there for meAntti Haapalinna
The aim of this New Modalities Ecosystem is enable improved understanding of disease pathology related to the symptoms and disease progression and better treatments by applying large molecular drugs and diagnostic tools as well as digital wearable patient tools for disease symptom recording, to have real world evidence for treatment efficacy
this show talks about some new technologies in medicine including visual reality , some mobile medical apps , and few about databases
this focuses more on the pharmacology.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Are There Any Natural Remedies To Treat Syphilis.pdf
Turning Data into Knowledge - Semantic Technologies in Healthcare
1. making sense of text and data
Turning Data into Knowledge - Semantic Technologies in Healthcare
Todor Primov
Nikola Tulechki
Svetla Boytcheva
June 2021
2. Why Ontotext?
Company profile –
Sirma AI (Ontotext)
Established in year 2000, as a
Research Lab of Sirma Holding
Unique mix of knowledge graphs and
advanced NLP
Leader in semantic web technology
and AI
Solid experience in
research projects
Attracted more than 15M Euro in
innovation funding through more than
50 research projects, funded by
H2020, FP7, FP6, and FP5.
Key competences include ontologies
and knowledge graphs, graph
embeddings, NLP and semantic
normalization, ML/DL, data analytics
High-profiled
commercial clients Life
sciences
Use cases: Enterprise KG; Enterprise
Semantic Search; Insights Platforms;
Decision support systems
Clients: from start-ups to top 10
pharma companies; from smart apps
to large hospitals and health
insurance companies
3. About 80% of
Electronic Health
Records are in
unstructured format
Need for NLP tools for
processing clinical text
Lack of multilingual
terminology
resources and
domain specific
ontologies
The automatic processing and knowledge extraction from
medical records is a task with public importance
4. Clinical text
OPERATIONS / PROCEDURES :Dobutamine stress test , cardiac
ultrasound , EGD , chest x-ray , PICC placement .The patient is a
62-year-old female with a history of diabetes mellitus ,
hypertension , COPD , hypercholesterolemia , depression and CHF
5.
6.
7.
8.
9.
10.
11.
12.
13. Why the task for concept normalization
is so important?
o Disambiguation
o Usage of URI
o Data integration
o Reasoning
o Similarity search
o Phenotypes
31. #31
Figure 1. The growth of the LOD Cloud diagram
over time. Diagrammatic representation of the
number of the LOD Cloud total datasets (blue
line) and LOD Cloud Life Sciences domain
datasets (red line) from 2009 to 2017 (top).
Evolution of the graphical view of the LOD
Cloud from 2007 to 2017 (bottom).
Published in 2018
Life Sciences Linked Open Data Datasets Connections to SNOMED CT, RxNORM & GO
Artemis Chaleplioglou, Sozon Papavlasopoulos, Marios Poulos
37. GraphDB Empowers Scientific Projects to Fight COVID-19 and Publish Knowledge Graphs
Ontotext’s GraphDB is used by Mayo Clinic to Publish CORD-19 with Semantic Annotations and by Cochranе for COVID-19 Study Register
https://www.ontotext.com/blog/graphdb-empowers-scientific-projects-to-fight-covid19-and-publish-knowledge-graphs/
44. Data
● Four types of input text segments
a. manufacturer - company names like
ALLERGAN --- Allergan --- Allergan plc --- Allergan, Inc --- Allergan, Inc.
GlaxoSmithKline --- GlaxoSmithKline Biologicals ---
GlaxoSmithKline Biologicals Dresden --- GlaxoSmithKline Pharmaceuticals
a. indication - disease, condition like Back pain, Hypertension, Osteoarthritis
b. adverse event - disease, condition
c. intervention - drugs like FLUCLOXACILLIN, HYDROCORTISONE SODIUM SUCCINATE,
NORVIR SOFT GELATIN CAPSULES
● Data sources manufacturer indication adverse
event
intervention
ClinicalTrials.gov 12 313 604 506 2 468 553 100 000
FDAERS 9 032 469 22 206 659 26 570 878 32 575 511
45. Standard Classifications and Ontologies
Snomed CT - Systematized Nomenclature of Medicine Clinical
Terms
UMLS - Unified Medical Language System
CHEBI - Chemical Entities of Biological Interest Ontology
MESH - Medical Subject Headings
46. Concepts Normalization. Gazetteer Creation
● Use of existing ontologies and databases - UMLS (disease), Drug Central (drugs),
Wikidata (company)
● Re-writing rules to adjust the ontology labels to our needs
original ontology term re-written new label
Oppenheim's Disease Oppenheim Disease
Congenital chromosomal disease Congenital chromosomal disorder
Congenital ocular coloboma (disorder) Congenital ocular coloboma
Choledochal Cyst, Type I Type I Choledochal Cyst
Abnormality of the pulmonary artery the pulmonary artery Abnormality
51. EXtreme-scale Analytics via Multimodal Ontology Discovery & Enhancement
Develop weakly supervised
knowledge discovery
algorithms for extreme
scale data, to associate:
• visual content of clinical
images
• semantic content included
in the diagnoses
Develop prediction &
analysis tools for clinical
settings & research
52. Clinical data are highly heterogeneous
• EHR
• Clinical Notes
• Biomedical data
• Medical Imaging Results
Data
57. Annotations for image regions WSI
Implemented annotation system Virtum that transforms
the input to variety of standard output formats
58. Development of new ontologies
Development of new ontologies - ExaMode OWL 2
Ontology, including all the four diseases treated in the
project: colon carcinoma, cervix carcinoma, lung
carcinoma, and celiac disease.
• The ontology models the clinical cases for all the
diseases, including their intervention and locations.
• The ontology can be accessed through Mappings for
some standard ontologies in Healthcare domain
(available at:
https://zenodo.org/record/4081387#.YEoaymgzaHs )
61. Acknowledgements
This work was carried out under the FROCKG project:
Fact Checking For Large Enterprise Knowledge Graphs
https://www.ontotext.com
This work was carried out under the ExaMode project: EXtreme-scale
Analytics via Multimodal Ontology Discovery & Enhancement
https://frockg.eu/
62. Thank you!
See Ontotext demos
Patient Insights: http://patient.ontotext.com/
Linked Life Data:
https://www.ontotext.com/knowledgehub/de
moservices/linked-life-data/
ExaMode: http://examode.ontotext.com/