A Visual Approach to Data Exploration
Leverage the power of natural language queries to populate Axon’s knowledge graphs with answers from structured and unstructured data sources stored in a Layar Data Fabric. Axon harnesses powerful BERT-modeled deep learning text analytics to derive greater value and insights from the Vyasa Biomedical Reference Data Fabric as well as internal document repositories, data storages, and real-time data streams. Drill down deeper by overlaying filters for named-entities, ontologies, or common answers across multiple questions asked in the knowledge graph.
Learn more about how Axon can fundamentally change the way you explore data assets within your organization.
Dynamic Knowledge Graphs
Visualize Novel Data Relationships
The knowledge graph offers a streamlined way to visualize key terms and evidence related to each user inquiry. A user can ask multiple questions in the application and find connections across their data fabric, presenting an opportunity for novel relationship discovery.
Natural Language Question Answering
Ask Questions of Your Integrated Data Fabric
Axon enables users to ask questions and retrieve deep learning A.I. answers from integrated content in Layar Data Fabrics. Axon uses a dynamic approach to continuously update the graph with new information, and all answers are linked to their original evidence.
Named Entity Recognition (NER)
Identify & Categorize Data Fabric Concepts
Axon leverages NER to identify and categorize terms and phrases from content integrated in Layar Data Fabrics. Axon identifies a wide array of life science NER concepts (proteins, cells lines, diseases, etc.) as well as several business development concepts (organizations, people, locations). NER concepts are continuously updated and refined by our deep learning models to reflect the current language of the domain, and novel terms previously not mentioned in literature (such as COVID-19).
Life Sciences Reference Data Fabric
Access to Pre-Built Sources
Connect to millions of life-science and legal texts for research and analytics, continuously analyzed by Layar using advanced deep learning text analytics algorithms. Retrieve answers from documents within PubMed, Clinical Trials, the US Patent and Trademark Office, and more.
Organize Knowledge Graph
Filter Graph Based on Node Data
Organize the nodes in your knowledge graph by filtering based on database, NER concepts, ontologies, dates, and more.
Filter Answers With Ontologies
A top down approach to ontologies, users can upload ontologies into the knowledge graph and apply them as a filter on top of the deep learning derived answers. This enables users to observe answers that are consistent between a known ontology and the novel answers coming from the system. The application already provides Gene Ontology (GO), SNOMED Clinical Terms (SNOMED CT), and the Human Protein Ontology (HPO) out-of-box.
Diverse Export Options
Connect to Third-Party Applications
Export all metadata, literature, and answers from Axon into an HTML, GraphML, or CSV for further downstream analysis. These options offer users an easy solution to connecting their Axon knowledge graphs into third-party applications.
Import Data from External Graph Databases
Pull structured data from a third party vendor application into Axon. If a question yields an answer that is also present in the graph database, any properties listed for that entity are displayed directly in Axon.
View Supporting Evidence for Answers Derived from Vyasa Question Answering
Drill down into the scientific articles that Axon uses as supporting evidence for each answer-node it generated in the knowledge graph. Axon’s deep learning A.I. agents collect information from files across your data fabrics to return the these supporting documents for you to quickly the assess the relevance of the evidence for a given node.
Rare diseases are those that affect less than 200,000 people and, as can be expected, studying these elusive diseases can be difficult. Synapse allows efficient and deep exploration of massive amounts of text data that might otherwise remain hidden.
Automated identification of emergent technologies, patents, and companies from unstructured text can be challenging. Synapse can uncover even the most obscured similarities between technologies hidden deep in documents, making discovery efficient and effective.
Business intelligence focuses on the acquisition of huge quantities of data and leveraging that data into actionable insights. Synapse Radar and Trendlines enable deep, efficient research to uncover information hidden in text.
Manual research is time consuming and modern search tools are limited in scope. Synapse utilizes its understanding of semantics to drive efficient discovery and allows the focus to return to the current case.
Dynamic Knowledge Graph Application
Axon enables derivation of dynamically generated knowledge graphs directly from integrated data and documents sources integrated in a Layar Data Fabric.
Find more support in our help center about:
- The Basics of Axon
- Narrowing Down Results with Filters
- Understanding Evidence View & Supporting Documents