Pricing

Map relationships between your data using BabelNet and Swiftask

Swiftask integrates BabelNet to enable your AI agents to understand and link complex concepts across millions of entities, automatically.

Result:

Transform raw data into a structured and actionable knowledge graph with no manual effort.

Linking complex concepts is a major challenge

Most companies have siloed data where relationships between entities are implicit or lost. Manually mapping these links is impossible at scale.

Main negative impacts:

Disconnected data: Without relational insights, your data remains isolated, limiting the relevance of your analyses.
Semantic inconsistency: Different terms can refer to the same entity, creating errors in your reports.
High processing costs: Manual analysis or developing proprietary models is extremely costly.

Swiftask automates relationship mapping by leveraging BabelNet's linguistic richness to intelligently link your data.

BEFORE / AFTER

What changes with Swiftask

Without Swiftask + BabelNet

A team of data analysts spends weeks cleaning data and manually mapping entities. The result is static, hard to maintain, and often obsolete by the time it is finished.

With Swiftask + BabelNet

Your AI agent analyzes your data streams in real time, uses BabelNet to disambiguate terms, and dynamically builds an accurate, up-to-date relationship map.

How to automate your mapping in 4 steps

STEP 1 : Configure your AI agent

Define your agent's goals in Swiftask and select the BabelNet connector.

STEP 2 : Connect your data sources

Connect your databases or documents to Swiftask to feed the analysis.

STEP 3 : Define mapping rules

Configure BabelNet settings to identify the types of relationships to extract.

STEP 4 : Generate and export your graphs

Visualize detected relationships and export them to your BI tools or graph databases.

Capabilities of your AI agent with BabelNet

The agent performs multilingual disambiguation and identifies hierarchical, synonymic, and associative relationships between your concepts.

Target connector: The agent performs the right actions in babelnet based on event context.
Automated actions: Entity extraction, synonym resolution, semantic enrichment, creation of relationship triples (subject-predicate-object).
Native governance: All detected relationships are logged in Swiftask to ensure decision traceability.

Each action is contextualized and executed automatically at the right time.

Each Swiftask agent uses a dedicated identity (e.g. agent-babelnet@swiftask.ai ). You keep full visibility on every action and every sent message.

Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.

Benefits for your data strategy

1. Semantic precision

BabelNet ensures deep contextual understanding of terms.

2. Scalability

Analyze millions of data points without human intervention.

3. Interoperability

Easily connect your results to your existing data ecosystem.

4. Massive time savings

Go from weeks of work to minutes of automated processing.

5. Data governance

Maintain control over mapping rules and the origin of relationships.

Security and confidentiality

Swiftask applies enterprise-grade security standards for your babelnet automations.

Data encryption: Your data is processed via secure channels with Swiftask.
Access control: Restricted access to agents and mapping configurations.
Full audit: Every created relationship is tracked for compliance.
Independence: You retain ownership of your knowledge graphs.

To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.

RESULTS

Impact on your operations

Metric	Before	After
Mapping time	Several days	A few minutes
Mapping precision	Variable (human)	Standardized (BabelNet)
Data volume processed	Limited	Massive (AI scale)
Graph maintenance	Manual and slow	Automatic and continuous

Take action with babelnet

Transform raw data into a structured and actionable knowledge graph with no manual effort.

Supercharge your AI training with BabelNet's semantic power

Next use case