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An NLP based approach to identify relationship among entities in a corpus of text and present them as triplets of Subject-Predicate-Object.
Overview
DeepInsights Semantic Triplet Generator is a novel approach of summarizing/converting an unstructured data corpus into query-able triplets of Subject-Predicate-Object using NLP. This creates the basic blocks of Resource Description Framework (RDF) data model which forms the basis of Q&A systems, chatbots and virtual assistants. It helps in semantic understanding of the unstructured data and create a Knowledge Graph. The algorithm takes English text data as input and generates the triplets.
Highlights
- 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 etc.
- 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 structured format signifying the associated entities along with their relationship.
- 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.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large 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.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge 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 |
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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 1000 words.
Output
- Content type: text/csv.
- The csv will have the triplets (Subject-Predicate-Object).
- Sample output:
- |----subject----|----relation----|-----object-----|
- |---company---|-----serves-----|----telecom---|
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 text/csv output.csvSubstitute 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.csv - 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, please reach out to:
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.
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Customer reviews
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1 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
Banking
Very good
Reviewed on Nov 22, 2024
Review provided by G2
What do you like best about the product?
I like the 3- D object design. It looks like very attractive
What do you dislike about the product?
Triplet loss can be used on images which have different sizes and shapes, but it might require some preprocessing. Since the model expects a fixed-size input, images would need to be resized or padded to the same size
What problems is the product solving and how is that benefiting you?
It is very helpful for me to explain the issue