Last winter I had the opportunity to meet Katariina Kari at a Neo4j event in Helsinki. We had a conversation about graphs, RDF, LPG… we agreed on some things… and disagreed on others 🙂 but I remember telling her that I had found very interesting a post she had published on how they were using Ontologies to drive semantic searches on the Zalando web site.
I’ll use her example from that post and show how you can implement semantic searches/recommendations in Neo4j and leverage existing Ontologies (public standards or your own). That’s what this QuickGraph is about.
I assume you have some level of familiarity with RDF and semantic technologies. Continue reading “QuickGraph#9 The fashion Knowledge Graph. Inferencing with Ontologies in Neo4j”
I have two Neo4j instances: let’s call them instance-one and instance-two. My problem is simple, I want an easy way to copy fragments of the graph stored in instance-one to instance-two. In this post, I’ll explain here how to use:
- Cypher to define the subgraph to be cloned and
- RDF as the model for data exchange (serialisation format)
All with the help of the neosemantics plugin. Continue reading “QuickGraph#8 Cloning subgraphs between Neo4j instances with Cypher+RDF”
In this instalment of the QuickGraph series, I’ll show how to map a graph stored in Neo4j to an ontology (or schema, or vocabulary…) using the neosemantics extension. Continue reading “QuickGraph#7 Creating a schema.org linked data endpoint on Neo4j”
After last week’s Neo4j online meetup, I thought I’d revisit QuickGraph#2 and update it a bit to include a couple new things:
- How to load not only categories but also pages (as in Wikipedia articles) and enrich the graph by querying DBpedia. In doing this I’ll describe some advanced usage of APOC procedures.
- How to batch load the whole Wikipedia hierarchy of categories into Neo4j
Continue reading “QuickGraph#6 Building the Wikipedia Knowledge Graph in Neo4j (QG#2 revisited)”
Say we have a dataset of multi-tagged items: books with multiple genres, articles with multiple topics, products with multiple categories… We want to organise logically these tags -the genres, the topics, the categories…- in a descriptive but also actionable way. A typical organisation will be hierarchical, like a taxonomy.
But rather than building it manually, we are going to learn it from the data in an automated way. This means that the quality of the results will totally depend on the quality and distribution of the tagging in your data, so sometimes we’ll produce a rich taxonomy but sometimes the data will only yield a set of rules describing how tags relate to each other.
Finally, we’ll want to show how this taxonomy can be used and I’ll do it with an example on content recommendation / enhanced search. Continue reading “QuickGraph#5 Learning a taxonomy from your tagged data”
For this example I am going to use my browser history data. Most browsers store this data in SQLite. This means relational data, easy to access from Neo4j using the apoc.load.jdbc stored procedure. Continue reading “QuickGraph#4 Explore your browser history in Neo4j”
For this example, I am going to use a sample movie dataset from the Cayley project. It’s a set of half a million triples about actors, directors and movies that can be downloaded here. Continue reading “QuickGraph#3 A step-by-step example of RDF to Property Graph transformation”
For this QuickGraph I’ll use data about Wikipedia Categories. You may have noticed at the bottom of every Wikipedia article a section listing the categories it’s classified under. Every Wikipedia article will have at least one category, and categories branch into subcategories forming overlapping trees. It is sometimes possible for a category (and the Wikipedia hierarchy is an example of this) to be a subcategory of more than one parent category, so the hierarchy is effectively a graph. Continue reading “QuickGraph#2 How is Wikipedia’s knowledge organised”
The first of a series of quick graphs in Neo4j built from public data. Watch this space! I’ll analyse a dataset on European politics by building a graph and querying across a number of dimensions. Continue reading “QuickGraph #1 European Politics from DBpedia. Loading data from an RDF triple store into Neo4j via SPARQL”