Will JSON, NoSQL, and graph databases save the Semantic Web?

OK, so the title is pure click bait, but here's the thing. It seems to me that the Semantic Web as classically conceived (RDF/XML, SPARQL, triple stores) has had relatively little impact outside academia, whereas other technologies such as JSON, NoSQL (e.g., MongoDB, CouchDB) and graph databases (e.g., Neo4J) have got a lot of developer mindshare.

In biodiversity informatics the Semantic Web has been a round for a while. We've been pumping out millions of RDF documents (mostly served by LSIDs) since 2005 and, to a first approximation, nothing has happened. I've repeatedly blogged about why I think this is (see this post for a summary).

I was an early fan of RDF and the Semantic Web, but soon decided that it was far more hassle than it was worth. The obsession with ontologies, the problems of globally unique identifiers based on HTTP (http-14 range, anyone?), the need to get a lot of ducks in a row all mad it a colossal pain. Then I discovered the NoSQL document database CouchDB, which is a JSON store that features map-reduce views rather than on the fly queries. To somebody with a relational database background this is a bit of a headfuck:

But CouchDB has a great interface, can be replicated to the cloud, and is FUN (how many times can you say that about a database?). So I starting playing with CouchDB for small projects, then used it to build BioNames and more recently moved BioStor to CouchDB hosted both locally and in the cloud.

Then there are graph databases such as Neo4J, which has some really cool things such as GraphGists which is a playground where you can create interactive graphs and query them (here's an example I created). Once again, this is FUN.

Another big trend over the last decade is the flight from XML and its hideous complexities (albeit coupled with great power) to the simplicity of JSON (part of the rise of JavaScript). JSON makes it very easy to pass around data in simple key-value documents (with more complexity such as lists if you need them). Pretty much any modern API will serve you data in JSON.

So, what happened to RDF? Well, along with a plethora of formats (XML, triples, quads, etc., etc.) it adopted JSON in the form of JSON-LD (see JSON-LD and Why I Hate the Semantic Web for background). JSON-LD lets you have data in JSON (which both people and machines find easy to understand) and all the complexity/clarity of having the data clearly labelled using controlled vocabularies such as Dublin Core and schema.org. This complexity is shunted off into a "@context" variable where it can in many cases be safely ignored.

But what I find really interesting is that instead of JSON-LD being a way to get developers interested in the rest of the Semantic Web stack (e.g. HTTP URIs as identifiers, SPARQL, and triple stores), it seems that what it is really going to do is enable well-described structured to get access to all the cool things being developed around JSON. For example, we have document databases such as CouchDB which speaks HTTP and JSON, and search servers such as ElasticSearch which make it easy to work with large datasets. There are also some cool things happening with graph databases and Javascript, such as Hexastore (see also Weiss, C., Karras, P., & Bernstein, A. (2008, August 1). Hexastore. Proc. VLDB Endow. VLDB Endowment. http://doi.org/10.14778/1453856.1453965, PDF here) where we create the six possible indexes of the classic RDF [subject,predicate,object] triple (this is the sort of thing can also be done in CouchDB). Hence we can have graph databases implemented in a web browser!

So, when we see large-scale "Semantic Web" applications that actually exist and solve real problems, we may well be more likely to see technologies other than the classic Semantic Web stack. As an example, see the following paper:

Szekely, P., Knoblock, C. A., Slepicka, J., Philpot, A., Singh, A., Yin, C., … Ferreira, L. (2015). Building and Using a Knowledge Graph to Combat Human Trafficking. The Semantic Web - ISWC 2015. Springer Science + Business Media. http://doi.org/10.1007/978-3-319-25010-6_12

There's a free PDF here, and a talk online. The consortium behind this project researchers did extensive text mining, data cleaning and linking, creating a massive collection of JSON-LD documents. Rather than use a triple store and SPARQL, they indexed the JSON-LD using ElasticSearch (notice that they generated graphs for each of the entities they care about, in a sense denormalising the data).

I think this is likely to be the direction many large-scale projects are going to be going. Use the Semantic Web ideas of explicit vocabularies with HTTP URIs for definitions, encode the data in JSON-LD so it's readable by developers (no developers, no projects), then use some of the powerful (and fun) technologies that have been developed around semi-structured data. And if you have JSON-LD, then you get SEO for free by embedding that JSON-LD in your web pages.

In summary, if biodiversity informatics wants to play with the Semantic Web/linked data then it seems obvious that some combination of JSON-LD with NoSQL, graph databases, and search tools like ElasticSearch are the way to go.

More Neo4J tests of GBIF taxonomy: Using IPNI to find objective synonyms

Following on from Testing the GBIF taxonomy with the graph database Neo4J I've added a more complex test that relies on linking taxa to names. In this case I've picked some legume genera (Coursetia and Poissonia) where there have been frequent changes of name. By mapping the GBIF taxa to IPNI names (and associated LSIDs) we can build a graph linking taxa to names, and then to objective synonyms (by resolving the IPNI LSIDs and following the links to the basionym), see http://gist.neo4j.org/?4df5af75d42e0f963e5d.

In this example we find species that occur twice in the GBIF taxonomy, which logically should not happen as the names are objective synonyms. We can detect these problems if we have access to nomenclatural data. in this case, because IPNI has tracked the names changes, we can infer that, say, Coursetia heterantha and Poissonia heterantha are synonyms, and hence only one of these should appear in the GBIF classification. This is an example that illustrates the desirability of separating names and taxa, see Modelling taxonomic names in databases.

Testing the GBIF taxonomy with the graph database Neo4J

I've been playing with the graph database Neo4J to investigate aspects of the classification of taxa in GBIF's backbone classification. Neo4J is a graph database, and a number of people in biodiversity informatics have been playing with it. Nicky Nicolson at Kew has a nice presentation using graph databases to handle names Building a names backbone, and the Open Tree of Life project use it in their tree machine.

One of the striking things about Neo4J is how much effort has gone in to making it easy to play with. In particular, you can create GraphGists, which are simple text documents that are transformed into interactive graphs that you can query. This is fun, and I think it's also a great lesson in how to publicise a technology (compare this with RDF and SPARQL, which is in no way fun to work with).

I created some GraphGists that explore various problems with the current GBIF taxonomy. The goal is to find ways to quickly test the classifications for logical errors, and wherever possible I want to use just the information in the GBIF classification itself.

The first example is a version of the "papaya plots" that I played with in an earlier post (see also an unfinished manuscript Taxonomy as impediment: synonymy and its impact on the Global Biodiversity Information Facility's database). For various reasons, GBIF has ended up with the same species occuring more that once in its backbone classification, usually because none of its source databases has enough information on synonymy to prevent this happening.

As an example, I've grabbed the classification for the bat family Molossidae, converted it to a Neo4J graph, and then tested for the existence of species in different genera that have the same specific epithet. This is a useful (but not foolproof test) of whether there are undetected synonyms, especially if the generic placement of a set of species has been in flux (this is certainly true for these bats). If you visit the gist you will see a list of species that are potential synonyms.

A related test catches cases where one classification treats a taxon as a subspecies whereas another treats it as a full species, and GBIF has ended up with both interpretations in the same classification (e.g., the butterfly species Heliopyrgus margarita and the subspecies Heliopyrgus domicella margarita).

Another GraphGist tests that the genus name for a species matches the genus it is assigned too. This seems obvious (the species Homo sapiens belongs in the genus Homo) but there are cases where GBIF's classification fails this test, such as the genus Forsterinaria. Typically this test fails due to problematic generic names (e.g., homonyms), incorrect spellings, etc.

The last test is slightly more pedantic, but revealing nevertheless. It relies on the convention in zoology that when you write the authorship of a species name, if the name is not in the original genus then you enclose the authorship in parentheses. For example, it's Homo sapiens Linnaeus, but Homo erectus (Dubois, 1894) because Dubois originally called this species Pithecanthropus erectus.

Because you can only move a species to a genus that has been named, it follows that if a species is described before the genus name was published, then if the species is in that newer genus the authorship must be in parentheses. For example, the lepidopteran genus Heliopyrgus was published in 1957, and includes the species willi Plötz, 1884. Since this species was described before 1957, it must have been originally placed in a different genus, and so the species name should be Heliopyrgus willi (Plötz, 1884). However, GBIF has this as Heliopyrgus willi Plötz, 1884 (no parentheses). The GraphGist tests for this, and finds several species of Heliopyrgus that are incorrectly formed. This may seem pendantic, but it has practical consequences. Anyone searching for the original description of Heliopyrgus willi Plötz, 1884 might think that they should be looking for the text string "Heliopyrgus willi" in literature from 1884, but the name didn't exist then and so the search will be fruitless.

I think there's a lot of scope for deveoping tests like these, inclusing some that m make use of external data as well. In an earlier post (A use case for RDF in taxonomy ) I mused about using RDF to perform tests like this. However Neo4J is so much easier to work with I suspect that it makes better sense to develop standard queries in it's query language (CYPHER) and use those.