Making your organization’s growing library of digital assets requires a highly contextual search solution. With Neo4j, you augment your enterprise search capabilities with graph-based search capabilities to deliver only relevant results. For example, a simple keyword search can be augmented with additional results that are related to the keyword without being explicitly requested in the search.
Neo4j based graph-based search is used by companies to improve search capabilities of product, services, content and knowledge catalogs.
White Paper: The Power of Graph-Based Search
Learn how graph-based search empowers your organization and customers to receive highly relevant answers to more precise and intelligent questions.Read the white paper
GraphGist: The Publication Graph Example
In the world of publications and CMSs, metadata about different articles, authors, issues and other entities lends itself to a searchable graph. This example models just a small subset of a fictive domain in this area.Explore the GraphGist
Graph Databases in the Enterprise: Graph-Based Search
Discover how giants like Facebook, Google and adidas used graph database search applications to upend their industries – and how your enterprise can too.Read more
Improved access to information
With the power of graph-based search, users and customers are more likely to find the product, service or digital asset they need most, improving their access to the information they need to make the best decision regarding your business.
Higher user engagement and satisfaction
When users are given the most relevant (and possibly unexpected) search results, they are more likely to engage further with your company, brand or product. No more endless searches that yield only useless results.
Effective cataloging and efficient queries
Assign a flexible variety of rich metadata to your searchable data points or assets for faster search and retrieval at query time.
Large and growing volume of data assets
Your big datasets grow bigger every moment, and without a data store that effectively stores the exponential number of relationships between data points, your search capabilities will always be limited to discrete queries. Any technology solution should accommodate the growing size, variety, metadata and relationships inherent in your dataset.
Difficult-to-construct precise search queries
Without the context of relationships and metadata, any search solution will fail to provide the precise results a user is looking for. Yet, to remain competitive today’s search engines need to zero in on user history, intent and context.
Harder to disambiguate search queries
As the volume of data increases, the likelihood of overlapping but irrelevant search results increases significantly. Discrete search engines merely provide a larger quantity of results, while today’s searches require a greater focus on quality and relevance.
Native graph store
Unlike relational databases, Neo4j stores interconnected master data that is neither linear nor purely hierarchical. Neo4j’s native graph storing makes it easier to decipher your data by not forcing intermediate indexing at every turn.
Neo4j’s versatile property graph model makes it easier for organizations to evolve master data models.
Performance and scalability
Neo4j’s native graph processing engine supports high-performance graph queries on large master datasets to enable real-time decision making.
The built-in, high-availability features of Neo4j ensure your master data is always available for mission-critical applications.
Discover how companies like Facebook and Google tapped into the power of graph search tools and learn how your application can easily implement graph-based search with Neo4j.Download the White Paper
Wanderu already stored its data in a MongoDB NoSQL database, but it needed Neo4j to perform the “path finding” – the complex search and discovery required to recommend to customers their best travel options. Find out how they did so.Read the case study
Creating Business Value through Data Relationships
Where does sustainable competitive advantage come from? It’s not from data volume or velocity, but from the knowledge of relationships in your data.Download Now