A few months ago I gave a presentation in the Connections: Life Sciences & Healthcare virtual event. It was about building a Knowledge Graph using public RDF resources. You can watch the recording here or even reproduce the whole session following the instructions in this repository.
I went through the content again recently and I found one particular bit of that session that was specially interesting and worth spending a QuickGraph on. I’m talking of course of the reconciliation of taxonomies. Let’s dive in.
Continue reading “QuickGraph#19 Taxonomy reconciliation” →
You’ve probably heard that there are billions of pages on the web that embed structured data describing products, events, people, organisations… One of the most popular mechanisms for doing this is JSON-LD which is one of the many ways of serialising triples. Since you’re here, I’m sure you know that triples form graphs and that I like exploring graphy things…
In this QuickGraph I’ll have a look at the brand new White House pages and use Neo4j and neosemantics to analyse the structured data they embed.
Continue reading “QuickGraph#15 Analysing the structured data embedded in web pages” →
The UNESCO Thesaurus is a controlled and structured list of terms in the areas of education, culture, natural sciences, social and human sciences, communication and information. It’s used used to annotate documents and publications like the ones in the UNESDOC digital library.
The Thesaurus is available as a multilingual SKOS concept scheme and at the time of writing, the available languages were English, Spanish, French, Russian and Arabic (download link).
Continue reading “QuickGraph#12 Working with a Multilingual Thesaurus” →
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” →