Sold by: Mphasis
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
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Delivery method
Latest version
Deployed on AWS
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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 |
Dimensions summary
You pay by the hour for each host you run, based on the SageMaker instance type you choose. Pricing splits into two modes. Batch inference runs the model on data in bulk. Real-time inference serves live requests. Each mode offers many instance types across compute-optimized (c4, c5, c5d), general-purpose (m4, m5, m5d), memory-optimized (r5, r5d), and GPU (p2, p3) families. Larger sizes within a family carry higher hourly rates. Real-time mode adds more instance options than batch mode. You are billed only for the hours each instance runs.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a single SageMaker instance runs the model. You are billed per instance, per hour, at the rate for the instance type you pick. The clock counts running time only, so partial usage still meters by the hour the instance is active.
Batch mode runs the model over data in bulk, so you launch an instance, process the job, then stop it. Real-time mode keeps an instance running to serve live requests. Both meter per instance-hour, but real-time typically accrues continuous hours while batch accrues only during each job.
Software charges meter running instance-hours only. If you stop an instance after a batch job finishes, that instance stops accruing software charges. Underlying AWS storage or resource fees may still apply separately, but the model usage fee counts active running time.
www.mphasis.com
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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
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