Overview¶

Ontotext Platform Workbench¶

The Workbench is the web-based administration interface to the Ontotext Platform. It covers the following main functionalities:

• Schemas: Manage Platform schemas, including create, generate, activate, etc.

• Playground: Integrates CodeMirror and provides a schema editing and validation capabilities

• GraphQL: The Workbench integrates a GraphiQL Developer tool for examining GraphQL schemas and invoking queries, mutations, etc.

• Monitoring: Provides visual representation of the Semantic Objects Service health checks

• Documentation: Direct link to the official Ontotext Platform documentation page

You can learn more about the Platform’s Workbench here.

GraphDB Workbench¶

The Platform Graph database (GraphDB) provides a graphical user interface for managing the graph of RDF data directly.

The GraphDB Workbench can be used for:

• managing GraphDB repositories;

• loading and exporting data;

• executing SPARQL queries and updates;

• managing namespaces;

• managing contexts;

• viewing/editing RDF resources;

• monitoring queries;

• monitoring resources;

• managing users and permissions;

• managing connectors;

• managing a cluster;

• providing REST API for automating various tasks for managing and administering repositories.

It is open source and can be cloned/forked from GitHub.

For example, you may want to visualize a Star Wars graph within the Workbench here.

You can read and learn more about the Platform’s GraphDB Workbench component here.

Semantic Objects (GraphQL)¶

The Semantic Objects Service lowers the barrier of entry for solution teams, developers, and enterprises. It helps increase the use of knowledge graphs by providing a simple, declaratively configured API.

The Semantic Objects Service allows users to query and mutate knowledge Graphs using GraphQL to uncover deep relationships within data, whilst at the same time removing the need to be overly concerned about the complexities of underlying graph data (RDF).

Semantic Objects are declaratively configured using very simple semantic object abstractions. A simple YAML-based meta-language named Semantic Objects Modeling Language, or SOML, maps an RDF graph to automatically generated GraphQL schemas.

The automatic generation of GraphQL APIs allows domain experts such as product managers or information architects to deploy knowledge graph APIs with minimal fuss and bother.

GraphQL Federation (Apollo GraphQL Federation)¶

The Platform provides an extended Apollo Federation gateway. The gateway provides a mechanism to combine multiple GraphQL endpoints and schema into a single aggregate endpoint and composite schema.

The basic principles of GraphQL federation are as follows:

1. Collecting the schemas from multiple endpoints.

2. Combining the schemas into a single composite schema.

3. Serving the single composite schema to the user completely hiding the inner dependencies.

4. When a query is performed, the Federation Gateway calls each of the endpoints in a specific order and combines their responses.

The Platform gateway has the following extended features:

• health checks

• good to go status checks

• transaction/log aggregation and correlation

• authentication token forwarding and RBAC

You can read and learn more about the Platform’s GraphQL federation component here.

Text Analytics Service¶

The Platform’s declaratively configurable, high performance and scalable text analytics engine is currently undergoing re-development so that it becomes part of Platform 3.x.

Until then, the Platform 2.x version will continue to provide text mining functionality across unstructured and semi-structured content, utilizing formalized knowledge provided by a custom knowledge graph managed within GraphDB.

Bespoke and out of the box text mining pipelines can be deployed to the scalable text analytics service to solve:

• Document Classification (against a standard or bespoke taxonomy)

• Named Entity Recognition (People Location Organization usually is provided out of the box)

• Relationship extraction

• Recommendations

• Semantic Search

The Platform 2.x text analytics technology stack includes:

• GATE : Natural Language Processing, providing annotations that can be combined with ML approaches

• TensorFlow & Keras : for building neural networks

• Edlin (proprietary ML library): for custom classifiers

• Proprietary Gazetteer Components: providing a dynamic gazetteer, linked to GraphDB

• Proprietary disambiguation ML

The re-engineered Platform 3.x text analytics technology stack is under development and is likely to include:

• Graph Embeddings: to provide knowledge graph context and disambiguation

Annotation Service¶

The Platform’s text analytics annotation API and store is currently undergoing re-development so that it becomes part of Platform 3.x.

The 2.x Platform annotates unstructured content using JSON-LD conforming to the W3C Web Annotation Model [WA] . The JSON-LD documents convey information about target content items by using URIs that reference domain entities within a GraphDB knowledge graph.

More information can be found here:

• Knowledge Quality via Efficient Annotation at Scale

• A Global view across Knowledge Graphs and Content Annotations

• Semantic Annotation Quality Assurance & Inter-Annotator Agreement

MongoDB is used for storing Web Annotation JSON-LD.

Graph Database (GraphDB)¶

This is Ontotext’s knowledge graph database, optimized for GraphQL queries, including SPARQL extensions to avoid N+1 GraphQL->SPARQL queries.

GraphDB is a highly efficient and robust graph database with RDF and SPARQL support. Its documentation is a comprehensive guide that explains every feature of GraphDB, as well as topics such as setting up a repository, loading and working with data, tuning its performance, scaling, etc.

The GraphDB database supports a highly available replication cluster, which has been proven in a number of enterprise use cases that required resilience in data loading and query answering.

You can read and learn more about the Platform’s GraphDB component here.

Semantic Object Schema Storage (MongoDB)¶

The MongoDB database is used as the storage layer for:

• Semantic Object schemas

• Semantic Object schema RBAC

The Platform provides a dockerized version of MongoDB that comes pre-configured with a data volume, databases, and collections required for Semantic Object schema and RBAC.

You can read and learn more about the Platform’s Schema storage and management component here.

Semantic Objects for MongoDB¶

Semantic Object for Elasticsearch¶

FusionAuth¶

A dockerized version of FusionAuth provides the Platform with identity and authentication FusionAuth.

Platform tenants, applications, users, and roles are managed by FusionAuth. These can be provisioned by using the Platform’s command-line tool. For more information see OPCTL.

FusionAuth provides OAuth authentication whilst also issuing JWT tokens that include the role and user claims.

FusionAuth issued JWT tokens are used to authorize GraphQL queries and mutations using declarative Semantic Object RBACs.

Semantic Objects RBAC¶

The Semantic Objects Service provides declarative schema level Role Based Access Controls. Allowing a schema to define those Roles that have access to Objects, properties, and relationships.

Tokens that are provided by FusionAuth (during authentication) include role claims. These role claims are used to control GraphQL query and mutation access based on the Semantic RBAC.

You can read and learn more about the Platform’s Authentication and Authorization components here.

Ingress and GW¶

Kong Ingress and API GW¶

The Platform provides a dockerized version of Kong’s Ingress Controller that implements Authentication Token authorization, service routing, throttling, and caching across a Kubernetes cluster.

The Platform point of entry also provides TLS termination, cert management, and load balancing: Kong API GW.

Helm¶

The Platform uses the Helm packaging format called charts. The Platform Helm chart is a collection of files that describe all the related Kubernetes resources. The Platform Helm chart can be used to deploy a Semantic Objects, GraphDB, MongoDB, etc pod as a full Platform stack.

Health Checking¶

All Platform services provide endpoints for:

• health checking - the service status and service dependencies

• good to go - the service good to go (use) status

• troubleshooting - guides for error states and topology diagrams

Telegraf¶

The Platform provides a dockerized Telegraf server for collecting metrics and stats over statsd from the following Platform services:

• Semantic Objects Service

• Federation Gateway

• GraphDB

• Elasticsearch

This allows an aggregated view across all Platform services that can be visualized using Grafana.

InfluxDB¶

The Platform provides a dockerized InfluxDB - time series database for storing logs, stats, and metrics.

All Platform logs are aggregated, correlated using X-Request-ID ids, filterable and searchable using Grafana

Grafana¶

Time series visualization for metrics/stats/logs exploration.

K6¶

The Platform provides a dockerized performance test injector and a set of acceptance performance tests. Performance test metrics are collated within InfluxDB and can be visualized within Grafana.

Find out more about setting up the monitoring here.

Source Code Management¶

The Platform uses a secure internal GitLab repository for the licensed software and a GitHub repository for open source Platform component source code.

Automated Builds and Tests¶

All Platform components are continuously built as Docker images and tested using Jenkins build pipelines.

Docker Images and Repository¶

All Platform components are built and released as Docker images on Docker Hub.

Security Vulnerabilities¶

All docker images are scanned for open source vulnerabilities using Trivy.

License Chart¶

All source code dependencies (JAVA, Javascript, Python, etc) licenses are collated and provided as a license report.