RDF is seductive.  I can’t get away from it.  Something about the ability to represent anything and everything in one consistent model just tugs at my engineer’s heartstrings.

The problem with RDF, as I’ve discovered through painful experience, is that the ability to represent everything sacrifices the ability to represent anything efficiently.  Certainly that is partly the fault of existing toolkits, but it’s still a fault.

But maybe I’m simply missing the point of RDF.  Its stated design goals do not include “efficient storage and retrieval.”  The W3C’s RDF recommendations make no mention of performance.  Tim Berners-Lee doesn’t mention it in his Semantic Web talks, except in airy references to future general purpose RDF engines.

RDF, it seems, is intended not as so much as a data storage model but as a communication model.  In short, it’s a protocol.  One of the goals Berners-Lee does talk about is exposing the information currently trapped inside relational databases.  So maybe I shouldn’t think about storing RDF and think instead about presenting it as a view of my own domain-specific data structures.

There are still some benefits to be had from storing and analyzing data in a graph-like structure.  But RDF is not necessarily the be-all and end-all of graphical models either.

There’s a school of thought that URIs should be opaque identifiers with no inherent meaning or structure. I think this is clearly a bad idea on the human-facing web, but it is more reasonable for computer-facing web services.

However, I’ve been generating a lot of RDF lately, trying to organize piles of metadata in AltLaw. I use URIs that have meaning to me, although they aren’t formally specified anywhere. I realized that a URI can represent a lot of information — not just the identity of a resource, but also its type, version, date, etc. — in a compact form. I can write code that manipulates URIs to get key information about a resource more quickly than I could by querying the RDF database.

Unfortunately, RDF query languages like SPARQL assume that URIs themselves are just pointers that do not contain any information. I could easily generate additional RDF statements containing all the same information encoded in the URI, but that would triple the size of my database and slow down my queries. (My experience so far with Sesame 2.0 is that complex queries are slow.)

What I need is a rule-based language for describing the structure of a URI and what it means. This would be similar to URI templates, and would map parts of the URI to RDF statements about that URI.

So if I make a statement about a resource at
http://altlaw.org/cases/US/1204
(note: not a real URI), the RDF database automatically infers the following (in Turtle syntax):

<http://altlaw.org/cases/US/2008-02-14/1204>
        rdf:type        <http://altlaw.org/rdf/Case> ;
        dc:identifier   "1204" ;
        dc:jurisdiction "US" ;
        dc:date         "2008-02-14" .

Let’s have the database models strut down the runway:

Relational (SQL):

• Data consist of rows in tables.
• Each row has multiple columns.
• Each column has a fixed type.
• Queries use filters and joins.
• Fixed schema is defined separately from the data.
• User-defined indexes improve query performance.
• Robust transaction/data-integrity support.

Graph (RDF):

• Data consist of nodes and labeled links between them.
• Nodes may have type information.
• Optional schema is defined in the same model (e.g. RDFS, OWL).
• Queries use graph-based constraints and filters.
• Indexes are not optimized for specific datasets.
• Limited transaction support.

Tree (XML, filesystem):

• Data are identified by a name and a position within a hierarchy.
• Queries use hierarchies and filters.
• Data may have type information.
• Optional schema may be defined in the same model (XML-Schema) or outside it (DTD).
• Transaction support varies by implementation.

Hash (BDB, memcached):

• Data are untyped binary blobs.
• Data are identified by unique keys in a flat namespace.
• Only key-based lookups are possible.
• Limited transaction support.

Inverted Index (Lucene/Solr):

• Data are text documents.
• Queries are free-form text with some special operators.
• Text queries are fast.
• Query results can be scored/ranked.
• Joins are inefficient; denormalized data are preferred.
• Limited transaction support.

Document-Oriented (CouchDB):

• Data are identified by unique keys in a flat namespace.
• Data have named fields.
• Optional schema defined by application code.
• Denormalized data are preferred.
• Limited type information.

Column-Oriented (Hbase, BigTable):

• Data consist of rows in tables.
• Each row has multiple columns, rows are assumed to be sparse.
• Data from a single column are stored contiguously to improve performance.
• Designed for distributed processing environments such as Hadoop and MapReduce.

Since I started working on AltLaw.org a little over a year ago, I’ve struggled with the data model. I’ve got around half a million documents, totally unstructured, from a dozen different sources. Each source has its own quirks and problems that have to be corrected before it’s useful.

When I started, I was using MySQL + the filesystem + Ferret. Keeping all that in sync, even with Rails’ ActiveRecord, was tricky, and searches were slow. Now I’ve got everything stored directly in a Solr index, which is very fast for searching. But it’s not so great when I want to change, say, how I convert the source PDFs into HTML, and I have to re-index half a million documents. And it can’t store structured data very well, so I augment it with serialized hash tables and SQLite.

What I want is a storage engine that combines properties of a relational database and a full-text search engine. It would put structured fields into SQL-like tables and unstructured fields into an inverted index for text searches. I want to be able to do queries that combine structured and unstructured data, like “cases decided in the Second Circuit between 1990 and 2000 that cite Sony v. Universal and contain the phrase ‘fair use’.” I also want to store metadata about my metadata, like where it came from, how accurate I think it is, etc.

I’ve been exploring some interesting alternatives: CouchDB is probably the closest to what I want, but it’s still under heavy development. DSpace is oriented more towards archival than search. Then there’s the big RDF monster, maybe the most flexible data model ever, which makes it correspondingly difficult to grasp.

I’m come to the conclusion that there is no perfect data model. Or, to put it another way, no storage engine is going to do my work for me. What I need to do is come up with a solid, flexible API that provides just the features I need, then put that API in front of one or more back-ends (Lucene, SQL, the filesystem) that handle whatever they’re best at.