Sold by: MphasisÂ
Deployed on AWS
An NLP based approach to identify relationship among named entities in a corpus of text and present the same as a knowledge graph
Overview
Mphasis Knowledge Graph is a novel approach of summarizing or converting unstructured data into query-able triplets of Subject-Predicate-Object using NLP. It helps in semantic understanding of the unstructured data. The algorithm takes English text data as input and generates two outputs, the triplets (Subject-Predicate-Object) and graphical representation of these triplets.
Highlights
- The solution uses English text as input and uses NLP to understand and convert input into semantically correct triplets of Subject-Predicate-Object. The solution summarizes the unstructured data into graphical format signifying the associated entities along with their relationship.
- The solution can be leveraged to import unstructured text data to graph Data Bases that can ease information retrieval process. This enables user to build dialogue systems such as question-answer systems, chatbots, knowledge discovery, compliance, customer 360, KYC etc.
- Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction & predictive analytics capabilities. Need customized Machine Learning and Deep Learning solutions? Get in touch!
Details
Sold by
Delivery method
Latest version
Deployed on AWS
Unlock automation with AI agent solutions
Fast-track AI initiatives with agents, tools, and solutions from AWS Partners.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.2xlarge instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
ml.c4.2xlarge Inference (Batch) | Model inference on the ml.c4.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Amazon SageMaker
Input
- Supported content type: text/plain.
- The input file has to be in utf-8 encoding only
- The algorithm works with any English text data with a word limit in range 100 to 250 words.
Output
- Content type: application/zip.
- A zipped folder contains two output files, a “.csv” file with the Triplets and a “.png” file with the knowledge graph.
- The csv will have the triplets {Subject-Predicate-Object} and a graphical representation of these triplets.
- In the knowledge graph (.png), the nodes represent Subject & Object and edges represent Predicate.
- The nodes in the knowledge graph is color coded based on NER tags:
- Location: Green
- Org: Maroon
- Date: Red
- Person: Blue
- Other NER tags:
- Skyblue
- Non NER tags:
- Yellow
Invoking endpoint
AWS CLI Command If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://Input.txt --content-type text/plain --accept application/zip output.zipSubstitute the following parameters:
- "endpoint-name" - name of the inference endpoint where the model is deployed.
- Input.txt - Input file.
- text/plain - MIME type of the given input file.
- output.zip - filename where the inference results are written to.
Resources
- Input MIME type
- text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
An NLP and knowledge graph based approach to create company profile by aggregating data from multiple sources.
The Mphasis AI for Text Processing service enables enterprises to generate actionable insights from Unstructured text content from a variety of sources such as annual reports, customer reviews, documents, forms, emails, tweets, chat transcripts, news articles etc. We leverage our patented 'AI for Text Processing' solution to engage with clients through assessments, implementations and managed services offerings. We help enterprises target impactful use cases to improve so that the derived insights can drive business benefits. Our Assessment, Workshops and Implementations focus on specific parts of the most relevant use cases and help define the exact benefits to be derived from the initiative such as efficiency, cost, customer experience etc.
Mphasis DeepInsights Text Summarizer helps in summarizing text documents.
Deep Learning based solution which predicts positive, negative and neutral sentiments from text.
A Deep Learning approach for address extraction from unstructured documents.
Machine Learning based solution which extracts important Key phrases/words from a corpus of text.
Mphasis DeepInsights Table Detection helps in detecting and locating the tabulated information from scanned images.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.