Tarification

Optimize your data with semantic filtering using BabelNet

Swiftask integrates BabelNet to enable your AI agents to understand the deep meaning of your information, beyond simple keywords.

Resultat:

Improve the precision of your business processes and reduce information noise through contextual semantic analysis.

Keyword filtering is no longer enough

Traditional filtering tools rely on exact term matching. In multilingual or technical environments, this approach generates false positives, misses crucial synonyms, and fails against the ambiguities of language.

Les principaux impacts négatifs :

Significant information noise: You receive too much irrelevant data because the system does not understand the actual context of the query.
Language barriers: Data in different languages is treated in silos, preventing a global and coherent view of the information.
Lack of contextual precision: Polysemous terms are misinterpreted, leading to costly classification errors for your business.

Swiftask connects your agents to the BabelNet knowledge base. The AI performs semantic filtering that identifies concepts, relationships, and real meaning, ensuring results of unparalleled precision.

AVANT / APRÈS

Ce qui change avec Swiftask

Without semantic filtering

A system searches for the word 'avocado'. It returns results for both the fruit and the legal profession indiscriminately. The user must manually sort the results.

With Swiftask + BabelNet

The AI agent analyzes the context. It instantly understands whether the request concerns the legal or food domain, and filters the information with perfect relevance.

Setting up semantic filtering in 4 steps

ÉTAPE 1 : Configure your agent in Swiftask

Define the data streams your agent needs to process and analyze.

ÉTAPE 2 : Activate the BabelNet connector

Integrate BabelNet as a semantic reference engine to enrich your agent's understanding.

ÉTAPE 3 : Define filtering rules

Set the concepts or semantic domains the agent should prioritize or exclude.

ÉTAPE 4 : Deployment and learning

The agent processes data in real-time, leveraging BabelNet's ontology for increased precision.

Semantic analysis capabilities

The agent uses BabelNet to navigate relationships between concepts, identify synonyms in dozens of languages, and disambiguate technical terms.

Connecteur cible : L'agent exécute les bonnes actions dans babelnet selon le contexte de l'événement.
Actions automatisées : Contextual document filtering. Automatic classification based on concepts. Semantic query translation. Rich named entity extraction.
Gouvernance native : All filtering steps are documented in Swiftask logs for total transparency regarding AI decisions.

Chaque action est contextualisée et exécutée automatiquement au bon moment.

Chaque agent Swiftask utilise une identité dédiée (ex. agent-babelnet@swiftask.ai ). Vous gardez une visibilité complète sur chaque action et chaque message envoyé.

À retenir : L'agent automatise les décisions répétitives et laisse à vos équipes les actions à forte valeur.

Operational benefits

1. Increased precision

Drastic reduction of false positives thanks to conceptual understanding.

2. Multilingual reach

Analyze and filter data in over 200 languages with a unified approach.

3. Time saving

Automate the sorting and qualification of complex data.

4. Evolving intelligence

Benefit from the constant richness of the BabelNet knowledge base.

5. Business compliance

Ensure only relevant information reaches your teams.

Security and privacy

Swiftask applique des standards de sécurité enterprise pour vos automatisations babelnet.

Secure processing: Your data is processed in an isolated environment.
Access control: Granular permission management in Swiftask.
Full traceability: Filtering history for audit.
Independence: You maintain full control over your business rules.

Pour aller plus loin sur la conformité, consultez la page gouvernance Swiftask et ses détails d'architecture de sécurité.

RÉSULTATS

Filtering performance

Métrique	Avant	Après
Result relevance	60% (keyword-based)	95%+ (concept-based)
Processing time	Manual (hours)	Automatic (milliseconds)
Supported languages	Limited	200+

Passez à l'action avec babelnet

Improve the precision of your business processes and reduce information noise through contextual semantic analysis.