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What is Wikimedia Enterprise?
Collect data from Wikimedia projects in multiple languages, employ metadata tailored for Wikimedia Enterprise, and detect instances of vandalism or notable alterations at the article level. By harnessing Wikimedia Enterprise, your organization can unlock a multitude of opportunities, such as constructing knowledge graphs, creating voice assistants or bots, training advanced models, and producing rich datasets, among various other uses. With access to one of the largest public data collections available, you can enjoy a unified framework and guaranteed accessibility. This valuable resource is perfect for boosting the capabilities of voice assistants, enhancing the quality of search engine results, training machine learning models, and enriching proprietary datasets. Moreover, equip your organization to establish a knowledge graph that fosters collaboration among different teams, ultimately driving increased efficiency and innovation across all departments. This interconnected approach not only streamlines processes but also cultivates a culture of shared knowledge and continuous improvement.
What is AllegroGraph?
AllegroGraph stands out as a groundbreaking solution that facilitates limitless data integration, employing a proprietary method to consolidate fragmented data and information into an Entity Event Knowledge Graph framework designed for extensive big data analysis. By leveraging its distinctive federated sharding features, AllegroGraph delivers comprehensive insights and supports intricate reasoning over a distributed Knowledge Graph. Additionally, users of AllegroGraph can access an integrated version of Gruff, an intuitive browser-based tool for graph visualization that aids in uncovering and understanding relationships within enterprise Knowledge Graphs. Moreover, Franz's Knowledge Graph Solution not only encompasses advanced technology but also offers services aimed at constructing robust Entity Event Knowledge Graphs, drawing upon top-tier products, tools, expertise, and experience in the field. This comprehensive approach ensures that organizations can effectively harness their data for strategic decision-making and innovation.
Integrations Supported
Apache Solr
Cloudera
Docker
Hackolade
Kubernetes
MongoDB
Openverse
PoolParty
Swarm
Integrations Supported
Apache Solr
Cloudera
Docker
Hackolade
Kubernetes
MongoDB
Openverse
PoolParty
Swarm
API Availability
Has API
API Availability
Has API
Pricing Information
$.01 per request
Free Version
Free Trial Offered?
Pricing Information
Pricing not provided
Free Version
Free Trial Offered?
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Wikimedia Enterprise
Company Website
enterprise.wikimedia.com
Company Facts
Organization Name
Franz Inc.
Company Location
San Francisco Bay Area
Company Website
www.franz.com
Categories and Features
API Management
API Design
API Lifecycle Management
Access Control
Analytics
Dashboard
Developer Portal
Testing Management
Threat Protection
Traffic Control
Version Control
Categories and Features
Data Visualization
Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery
Database
Backup and Recovery
Creation / Development
Data Migration
Data Replication
Data Search
Data Security
Database Conversion
Mobile Access
Monitoring
NOSQL
Performance Analysis
Queries
Relational Interface
Virtualization
Knowledge Management
Artificial Intelligence (AI)
Cataloging / Categorization
Collaboration
Content Management
Decision Tree
Discussion Boards
Full Text Search
Knowledge Base Management
Self Service Portal
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization
Natural Language Processing
Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization
NoSQL Database
Auto-sharding
Automatic Database Replication
Data Model Flexibility
Deployment Flexibility
Dynamic Schemas
Integrated Caching
Multi-Model
Performance Management
Security Management