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For querying across the entire Knowledgebase dataset, Phenoscape is using the Bigdata RDF triplestore. We selected Bigdata for several reasons:

  • Top SPARQL query performance among open-source triplestores
  • Support for SPARQL 1.1 query language. This is required for aggregates such as COUNT. Property paths also provide basic transitivity reasoning at query time.
  • Embedded full-text index available within SPARQL queries.
  • Concise bounded description mode for SPARQL DESCRIBE queries. When blank nodes are included in a DESCRIBE result, this recursively describes them until the graph terminates in named nodes in all directions. This is useful for grabbing the necessary and sufficient RDF graph needed to reconstruct OWL class expressions.
  • Easy installation as Java WAR within Apache Tomcat.

The data that go into Bigdata are assembled via the KB build process into a graph data model.

Phenoscape machine configurations

  • SPARQL endpoint using the Bigdata NanoSparqlServer within Apache Tomcat. This machine runs Ubuntu Linux, with 4 CPUs and 8 GB RAM. Tomcat is allowed 6 GB RAM.
    • This database holds about 35 million triples.
  • owlet service which expands SPARQL queries using the ELK reasoner and submits them to the preceding Bigdata endpoint. This machine runs Ubuntu Linux, with 4 CPUs and 16 GB RAM. Tomcat is allowed 12 GB RAM.
    • The ELK instance holds about 750,000 logical axioms.
    • This web application also contains the beginnings of a few web services. These query the SPARQL endpoint over HTTP. Perhaps this application should be revised to directly load Bigdata, as our standalone compute scripts do (below).
  • Direct loading of Bigdata to answer long-running, high-memory queries on the DSCR cluster. We have machines with many CPUs and over 100 GB RAM.

Querying with OWL semantics

While OWL can be stored as RDF, thinking at the OWL axiom level can be quite different from thinking at the RDF triple level. While OWL matches up nicely with RDF and SPARQL when dealing with object property assertions for individuals, querying using complex class descriptions is much more straightforward using DL queries to an OWL reasoner rather than via SPARQL, which is designed for matching triple patterns. Querying SPARQL patterns which match pieces of the OWL-to-RDF serialization is error prone and will likely provide incomplete results. It is best to consider that triples using predicates from the OWL namespace are a private implementation detail (e.g. owl:onProperty, owl:someValuesFrom, etc.).

Since Phenoscape annotations are composed largely of complex OWL class descriptions, we use two approaches to make use of OWL semantics within SPARQL queries:

  • Generated sets of named classes for useful expression patterns. These include annotation property relations which make it easier to query these "class-level relationships" from SPARQL.
    • Existential restriction hierarchies, such as (part_of some X), where X is every anatomical structure
    • Absence classes
  • owlet, which allows embedding of arbitrary DL queries into SPARQL. This provides for an infinite number of possible expressions, but precludes using variables in the class expression.

Issues we've encountered

  • Full text searches are quite a bit slower when more than one property is considered. It would be really nice to be able to search ?s rdfs:label|obo:related_synonym ?label, but this is not really feasible.
  • Some simple queries can be quite slow, for example counting the number of triples in the database. It seems like there is some special optimization for this in Virtuoso, which returns the answer instantly.
  • Overall query speed can be a little too slow to run a user-facing web application. The current machines we are using for this may not be optimal configurations.

Future applications

Semantic similarity

For several applications we would like to query for data items which are semantically similar to each other, meaning they are each annotated with sets of class descriptions which share semantics to a greater degree than with other data items. For example, a gene may be associated with a set of phenotypic expressions such as round and inheres_in some tail; small and inheres_in some eye. What other genes are annotated most similarly? Perhaps another also affects the tail but with a different shape. These semantic profiles could potentially contain just a few or dozens of expressions.

Current implementations of semantic similarity compute scores for profile pairs and can run for several hours. It would be nice to develop a system which could query for similar entities on the fly, providing some sort of match score. Could MPGraph play a role here?

  • A difficulty of implementing semantic similarity search using SPARQL queries is the need to find the nearest common ancestor along the subclass hierarchy. A graph traversal API could possibly facilitate this. There may be multiple hierarchies of interest, according to various dimensions along which similarity can be considered (normal subclass hiearchy, existential restriction hiearchies, more complex expression templates).
  • Scores could also take into account information content of matches, giving more weight to higher information matches.