HCLS Knowledgebase

W3C Working Draft 4 April 2008

This version:
Latest version:
M. Scott Marshall, University of Amsterdam <marshall@science.uva.nl>
Eric Prud'hommeaux, W3C <eric@w3.org>
Alan Ruttenberg, Science Commons <alanruttenberg@gmail.com>
Jonathan Rees, Science Commons <jar@creativecommons.org>
Susie Stephens, Lilly <Stephens_Susie_M@lilly.com>


The HCLS Knowledgebase (HCLS-KB) is a biomedical knowledge base that integrates 15 distinct data sources using currently available Semantic Web Technologies such as the W3C standard Web Ontology Language (OWL) and Resource Description Framework (RDF). This report outlines which resources were integrated, how the KB was constructed using freely available triple store technology, how it can be queried using the W3C Recommended RDF query language SPARQL, and what resources and inferences are involved in answering complex queries. While the utility of the KB is illustrated by identifying a set of genes involved in Alzheimer's Disease, the approach described here can be applied to any use case that integrates data from multiple domains.

Status of This Document

This section describes the status of this document at the time of its publication. Other documents may supersede this document. A list of current W3C publications and the latest revision of this technical report can be found in the W3C technical reports index at http://www.w3.org/TR/.

This is a First Public Working Draft of the Semantic Web in Health Care and Life Sciences Interest Group (HCLS), part of the W3C Semantic Web Activity. This document describes the construction and use of the HCLS Knowledgebase used in the WWW2007 Banff HCLS Demo. It describes the process for creating a bilogical database on the Semantic Web. The companion document, Experiences with the conversion of SenseLab databases to RDF/OWL, describes the process for integrating new data into this Knowledgebase.

Please send all comments on either of these documents by 21 April, to public-semweb-lifesci@w3.org, a mailing list with a public archive, though the IG does not promise explicit responses to each comment. Publication of this document as an Interest Group Note is planned for May 2008; timely comments are appreciated. Areas marked with "@@" are known to be incomplete, however, any suggestions in these areas are still appreciated.

Publication as a Working Draft does not imply endorsement by the W3C Membership. This is a draft document and may be updated, replaced or obsoleted by other documents at any time. It is inappropriate to cite this document as other than work in progress.

This document was produced by a group operating under the disclosure obligations of the 5 February 2004 W3C Patent Policy. The group does not expect this document to become a W3C Recommendation. An individual who has actual knowledge of a patent which the individual believes contains Essential Claim(s) must disclose the information to public-semweb-lifesci@w3.org [public archive] in accordance with in accordance with section 6 of the W3C Patent Policy.

Table of Contents


1 Introduction

The life sciences have a rich history of making data available on the Web, because researchers recognized the benefits of sharing data and made it available to other researchers for the benefit of greater science. However, because many of the data repositories were developed in relative isolation, they tend to use different identifier schemes, incompatible terminology, and dissimilar data formats. This makes it hard for researchers to find all data about an entity of interest and to assemble it into a useful block of knowledge. The HCLS knowledgebase was built to demonstrate how Semantic Web technologies can integrate such heterogeneous data sets and thereby help scientists to more easily answer interesting scientific questions.

The key to advancing scientific understanding is empowering scientists with the information that they need to make well-informed decisions. Scientists need to be able to easily gain access to all information about chemical compounds, biological systems, diseases, and the interactions between these entities, and this requires data to be effectively integrated in order to provide a systems level view to the user. However, achieving this goal has proven to be a formidable challenge in the life sciences, where data and models are found in a large variety of formats and scales that span from the molecular to the anatomical.

In order to overcome the challenge of gaining insight directly from the Web, a number of laboratories, organizations, and companies have built internal data warehouses from the publicly available data sources. This certainly helps scientists to more easily query for all information related to entities of interest. However, these efforts generally integrate a subset of publicly available data that is deemed to be of greatest interest, and it has proven difficult to add data sources to the data at a later point. Further, advances in scientific knowledge require regular changes to be made to the underlying data models, and this is not straightforward with a relational model. Organizations that use this approach also typically face challenges with representing data that is at different levels of abstraction, and that includes data of very different quality.

Many health care and life sciences organizations are interested in the data integration abilities promised by the Semantic Web. More specifically, the benefits include the aggregation of heterogeneous data using explicit semantics, and the expression of rich and well-defined models for data aggregation and search. Another critical aspect of the Semantic Web is the ability to more flexibly add additional data sets into the data model, and more easily reuse data in unanticipated ways. Finally, once the data has been integrated, the Semantic Web enables the application of reasoning to infer additional insights.

The HCLS Knowledgebase imports data from data sources that span multiple domains in health care and the life sciences to make cross-discipline queries and, thereby, knowledge integration possible. The use of an RDF repository to store RDF and OWL makes it possible to query, manipulate, and reason about the data with standard tools and languages such as the SPARQL Query Language for RDF, as well as OWL reasoners. Although this document addresses a specific use case, the approach described here can be applied to any use case that integrates data from multiple domains.

1.2 Document Scope and Target Audience

This document attempts to succinctly describe how the HCLS Knowledgebase was constructed so that interested parties can use the core requirements to eventually create their own knowledgebase. We have attempted to write a general description but the knowledgebase makes use of unavoidably specialized resources, such as those found in the Data Sources section. Some, but not all, of the reasoning behind design decisions is explained. Several technologies such as semantic web standards were used but we are unable to explain all aspects in the depth that would be required for those new to the area. Those interested in a general introduction to semantic web should see The Semantic Web Primer. See also the CO-ODE web site for a hands-on OWL tutorial with Protégé.

1.3 Stability of Terms

This document uses URLs to identify records about biological processes. The identifiers used in this document are the same as those used in the knowledgebase and are not yet stable. However, an accompanying appendix will index the regular expressions or scripts used to update these identifiers as they evolve. Knowledgebase implementors should use these terms whenever possible.

1.4 Document Conventions

In this document, examples assume the following namespace prefix bindings unless otherwise stated:

Prefix URI Description
rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# The RDF Vocabulary
rdfs: http://www.w3.org/2000/01/rdf-schema# The RDF Schema vocabulary
xsd: http://www.w3.org/2001/XMLSchema# XML Schema
sc: http://purl.org/science/owl/sciencecommons/ Classes and properties belonging to the ad hoc Science Commons ontology.
pubmedRec: http://purl.org/commons/record/pmid/ PubMed records (not the articles themselves).
article: http://purl.org/science/article/pmid/ PubMed articles.
ncbi_gene: http://purl.org/commons/record/ncbi_gene/ Entrez Gene records (not the genes themselves).
proteinsubclass: http://purl.org/science/protein/subjects/ Proteins of a given gene participating in a given pathway.
go: http://purl.org/obo/owl/GO# temporary namespace for Gene Ontology terms
protein: http://purl.org/science/protein/bysequence/ NCBI records for Genes sequences.
ro: http://www.obofoundry.org/ro/ro.owl# (proposed update may be more complete) Relationships between members of OBO classes.
obo: http://purl.org/obo/owl/obo# @@ don't know — contains part_of @@
senselab: http://purl.org/ycmi/senselab/neuron_ontology.owl# Neuroscience ontology derived from the SenseLab NeuronDB database.
dnaGeneProduct: http://purl.org/science/owl/sciencecommons/is_protein_gene_product_of_dna_ Syntactic trick to shorten sc:is_protein...described_by

1.5 Document Outline

1 Introduction motivates and explains this document.

2 Use Case introduces an interesting scientific question that the knowledgebase can be used to address.

3 Data Sources describes the data sources that have been incorporated into the knowledgebase.

4 Design Decisions explains the reasons for several design choices.

5 Importing to RDF explains the process of translating data into RDF triples.

6 Query explains the basis query that answers the scientific question.

7 Data Model explains the basics of RDF triples.

8 Adding a New Data Source explains how the SenseLab database was integrated.

9 Named Graphs discusses the use of named graphs and query details.

10 Next Steps discusses problem areas and possible improvements.

2 Use Case

Alzheimer's is a debilitating neurodegenerative disease that affects approximately 27 million people worldwide. The cause of Alzheimer's is currently unknown and no therapy is able to halt its progression. However, insight into the mechanism and potential treatment of this debilitating disease may come from the integration of neurological, biomedical and biological resources. The HCLS knowledgebase assembles several neurology-related resources alongside an array of clinical and biological resources. This makes it possible to integrate knowledge across several research domains and potentially provide insight into the mechanisms of the disease.

The scientific question under scrutiny in our use case involves several elements of putative functional importance to Alzheimer's. CA1 Pyramidal Neurons (CA1PN) are known to be particularly damaged in Alzheimer's disease and play a key role in signal transduction. Signal transduction pathways are considered to be rich in proteins that might respond to chemical therapy. By integrating information about signal transduction, pyramidal neurons, their genes, and gene products, the query corresponding to our scientific question can provide information relevant to researchers that are looking for drug target candidates that are potentially effective against Alzheimer's Disease.

3 Data Sources

In order to incorporate data from several information sources, it was necessary to convert several exported formats, each into its own RDF bundle. The largest RDF bundle of 200M triples resulted from MeSH associations with PubMed articles. In contrast, there were a number of smaller bundles ranging from 10K to 10M triples. This resulted in a total of approximately 350M triples occupying approximately 20Gb when loaded into the RDF repository. In several cases, we extracted only a subset, for example, by selecting only human, rat, and mouse data. Click on [Details] in the table below to view provenance information such as the date of the last extraction, whether the extraction was a subset, etc.

At the time of publication, the following information sources have been (sometimes partially) incorporated into the knowledgebase. This set will continue to be extended in depth (i.e., more complete inclusion of partially represented data sets) and in breadth (i.e., novel data sets):

Allen Brain Atlas (ABA) Allen Brain Atlas is an interactive, genome-wide image database of gene expression in the mouse brain. A combination of RNA in situ hybridization data, detailed Reference Atlases and informatics analysis tools are integrated to provide a searchable digital atlas of gene expression. Together, these resources present a comprehensive online platform for exploration of the brain at the cellular and molecular level. [Details]
Addgene A catalog of plasmids from Addgene [Details]
BAMS The Brain Architecture Management System (BAMS) is designed to be a repository of information about brain structures from different species, and has a set of inference engines for processing the neurobiological data. BAMS contains to date five interrelated modules: Brain Parts (brain regions, major fiber tracts, and ventricles), Cell Types, Molecules, Relations (between structures from different neuroanatomical atlases), and Connections. [Details]
GALEN GALEN is an advanced terminology of medical concepts for clinical information systems. More on GALEN. We imported the GALEN ontology in OWL from CO-ODE [Details]
NCBI gene_info NCBI gene_info was imported into OWL. [Details]
Gene Ontology (GO) The Gene Ontology project provides a controlled vocabulary to describe gene and gene product attributes in any organism. GO terms are often used to annotate gene and protein records. [Details]
GOA GO annotations from NCBI and EBI. [Details]
HomoloGene Homologene is a system for automated detection of homologs among the annotated genes of several completely sequenced eukaryotic genomes. [Details]
MEDLINE/PubMed PubMed is a service of the U.S. National Library of Medicine that includes over 17 million citations from MEDLINE and other life science journals for biomedical articles back to the 1950s. PubMed includes links to full text articles and other related resources. [Details]
MeSH Medical Subject Headings. 2008 MeSH includes the subject descriptors appearing in MEDLINE/PubMed, the NLM catalog database, and other NLM databases. See the MeSH introduction. [Details]
Neurocommons Text Mining Pilot Protein/gene associations/interactions extracted from Temis software applied to 7% of Medline records (SC). Annotations were captured in RDF using the Neurocommons Annotations Schema [Details]
BerkeleyBop OBO ontologies All Open Biomedical Ontologies (OBO) available from BerkelyBop. [Details]
Science Commons Ontology An ad hoc ontology from Science Commons used by the output of several of the conversion scripts. [Details]
SenseLab There will be a reference here to another W3C document. [Details]
SWAN Semantic Web Applications in Neuromedicine @@ no bundle yet @@
SKOS Simple Knowledge Organization System: specifications and standards to support the use of knowledge Organization systems (KOS) such as thesauri, classification schemes, subject heading systems and taxonomies within the framework of the Semantic Web -

4 Design Decisions

A number of design decisions were made during the construction of the HCLS Knowledgebase. Many of the decisions were pragmatic in nature, as a consequence of the need to implement the solution on a commodity PC within a two-month period for a demonstration at WWW2007.

5 Importing to RDF

A number of different approaches were used for the conversion of data into RDF/OWL. The most commonly used approach was the use of Lisp code to read text exports of the data and create OWL or RDF documents. We will focus on the example of importing data from Homologene.

The general steps required to import from an existing data source into RDF are:

In the case of Homologene, we start with a text file that contains the exported information. The original tab delimited file is ftp://ftp.ncbi.nih.gov/build54/homologene.data. The Lisp code for the homologene conversion is also available.

It looks like this:

3       9606    34      ACADM   4557231 NP_000007.1
3       9598    469356  ACADM   114557331       XP_524741.2
3       9615    490207  ACADM   73960161        XP_547328.2
3       10090   11364   Acadm   6680618 NP_031408.1
3       10116   24158   Acadm   8392833 NP_058682.1 

We are interested in the first 3 fields. The first field identifies the homologous cluster. The second field is the species taxon. The third field is the EntrezGene id. We are only interested in human, mouse, rat, taxon ids: "9606" "10116" "10090".

We first iterate over the lines in the file, creating a table mapping cluster id to the pairs of taxon id, entrez id in the cluster. This is the variable homologene, created by the function read-homologene. For each of these clusters we will create an individual to represent the cluster e.g for cluster 99949:

  <sciencecommons:orthology_record rdf:about="http://purl.org/science/record/homologene/cluster_r54_99949">
    <sciencecommons:has_homologous_gene_record rdf:resource="http://purl.org/commons/record/ncbi_gene/678753"/>
    <sciencecommons:has_homologous_gene_record rdf:resource="http://purl.org/commons/record/ncbi_gene/727759"/>
    <sciencecommons:has_supporting_evidence rdf:resource="http://purl.org/science/evidence/homologene/cluster_r54_99949"/>

There are two things to note about this conversion. The first is that HTTP URIs were adopted as the mechanism to identify records. Importantly, these can also be resolved using standard web technology (web browsers). PURLS for a specific format of database records redirect to web pages that describe those records in the format. It has been our experience that URIs for such web pages can change over time. PURLS were chosen because we can change what web page a PURL redirects to. Thus PURLs provide a more stable URI for a resource than the provider resources, and place control enabling one to make repairs in such situations into the hands of the HCLS community. The second is that we mapped equivalent terms from different resources to a common base URI i.e. the http://purl.org/commons/record/ncbi_gene/ prefix was used to consistently identify Entrez Gene records. This allows for trivial data integration between different resources and simplifies queries involving Entrez Gene records.

Also, the individual http://purl.org/science/evidence/homologene/cluster_r54_99949 serves as a link to the "evidence", which is not elaborated in this translation, but would include the blast scores and other evidence used to establish the orthology in future work. (see http://www.ncbi.nlm.nih.gov/sites/entrez?cmd=Retrieve&db=homologene&dopt=AlignmentScores&list_uids=99949)

6 Query

The scientific question can be answered with the following query, which searches for gene names and processes from four data sources within the knowledgebase. The data sources include: MeSH (Pyramidal Neurons), PubMed (Journal Articles), Entrez Gene (Genes), Gene Ontology (Signal Transduction). The query example selects the gene name of the genes involved in signal transduction that are related to pyramidal neurons. Some of the complexity in this query comes from the need to capture relevant anatomical and functional detail at the subcellular and molecular level. The portion probing the Gene Ontology queries a set of classes describing processes at the molecular level. Our query employs the SPARQL RDF query language to perform knowledge integration across the sources of the knowledgebase. Details on SPARQL can be found in the References.

[Note: The query below will not work verbatim at SPARQL endpoints. We have simplified the actual Banff demonstration query for explanatory purposes in our example below. The Banff demonstration query is discussed in more detail in Named Graphs Section. You can try running the query at the DERI installation.]

SELECT ?genename ?processname

  # PubMeSH includes ?gene_records mentioned in ?articles which are identified by pmid in ?pubmed_records .
  ?pubmed_record sc:has-as-minor-mesh mesh:D017966 .
  ?article sc:identified_by_pmid ?pubmed_record .
  ?gene_record sc:describes_gene_or_gene_product_mentioned_by ?article .

  # The Gene Ontology has a set of ?proteins such that foreach ?protein, ?protein ro:has_function [ ro:realized_as ?process ].
  ?protein rdfs:subClassOf ?restriction1 .
  ?restriction1 owl:onProperty ro:has_function .
  ?restriction1 owl:someValuesFrom ?restriction2 .
  ?restriction2 owl:onProperty ro:realized_as .
  ?restriction2 owl:someValuesFrom ?process .
  # Also, foreach ?protein, ?protein has a parent class which is linked by some predicate to ?gene_record.
  ?protein rdfs:subClassOf ?protein_superclass .
  ?protein_superclass owl:equivalentClass ?restriction3 .
  ?restriction3 owl:onProperty dnaGeneProduct:described_by .
  ?restriction3 owl:hasValue ?gene_record .
  # Each ?process (that we are interested in) is a subclass of the signal transduction process.
  ?process obo:part_of go:GO_0007166 .

  ?gene_record rdfs:label ?genename .

  ?process rdfs:label ?processname .

The following shows a few of the results from the query:

gene_record_name processname
Entrez Gene record for human DRD1, 1812 adenylate cyclase activation
Entrez Gene record for human ADRB2, 154 adenylate cyclase activation

The following section describes the RDF data model and how we employed it to make our query possible.

7 Data Model

The data in the knowledgebase is modeled in OWL-DL, which has been expressed as RDF triples. Briefly, an RDF triple consists of a subject, predicate, and object. The predicate is also known as the property of the triple. Subjects and objects in the data unify to create an RDF Graph, with subjects and objects as nodes and predicates as edges. For more information about RDF and OWL, see the References section in the Appendix.

Nodes labeled with a leading "_:", e.g. proteinsubclass:p1812_7190_1, are called RDF blank nodes [CONCEPTS]. These frequently have machine-generated and therefore typically opaque to a human reader (e.g., the set of all nodes that represent protein entities linked to the GO molecular function XXX), but the purposes of explanation, here, they have been named to convey meaning to the reader. Blank nodes ending in "_1" in this document indicate this blank node is one of many in this class.

Triples in Solution

Figure 1. Triples in Solution [SVG image PNG image]

The application of a commercial text mining tool to neuroscience-related PubMed abstracts results in a set of annotations that link MeSH terms to genes (for more details on MeSH, see the table in Data Sources. An article with PubMed id 10698743 mentions ncbi_gene:1812 and that the corresponding PubMed record has a MeSH term mesh:D017966. The following three triples express this:

subject predicate object
pubmedRec:10698743 sc:has-as-minor-mesh mesh:D017966 .
article:10698743 sc:identified_by_pmid pubmedRec:10698743 .
ncbi_gene:1812 sc:describes_gene_or_gene_product_mentioned_by article:10698743 .

A set of genes or gene products in human bodies are described by ncbi_gene:1812. Here, we call this set _:equiv1812.

_:equiv1812 owl:onProperty dnaGeneProduct:described_by .
_:equiv1812 owl:hasValue ncbi_gene:1812 .

protein:ncbi_gene.1812 has the same extension (members) as the OWL restriction _:equiv1812.

protein:ncbi_gene.1812 owl:equivalentClass _:equiv1812 .

The expression

NamedClass equivalentClass R .
R onProperty SomeProperty .
R hasValue SomeClass

is an owl idiom to say that for every X such that

X SomeProperty SomeClass .

X is a member of the class NamedClass. See OWL Web Ontology Language Semantics and Abstract Syntax Section 4. Mapping to RDF Graphs for a formal treatment of this.

Using our other supplied constant, we note that adenylate cyclase activation, go:GO_0007190, is part of signal transduction, go:GO_0007166. Note: this simplified query matches only processes that are a sub-process of go:GO_0007166; the actual query, described in §9 Named Graphs, looks also for subclasses. The part_of relationships were inferred from the OWL class restrictions described in §7.1 Precomputing Inferences. The class of functions that are realized_as adenylate cyclase activation is here labeled _:activateAdenylCyclase.

go:GO_0007190 obo:part_of go:GO_0007166 .
_:activateAdenylCyclase owl:onProperty ro:realized_as .
_:activateAdenylCyclase owl:someValuesFrom go:GO_0007190 .

There are many possible classes of substance participating in molecular signaling, one of which (called here _:molecularSignalers_1) is defined by the ability to activate adenyl cyclase.

_:signalingParticipants_1 owl:onProperty ro:has_function .
_:signalingParticipants_1 owl:someValuesFrom _:activateAdenylCyclase .

The class of proteins in the intersection of _:signalingParticipants_1 and protein:ncbi_gene.1812 is here abbreviated proteinsubclass:p1812_7190_1, though the actual identifier is proteinsubclass:product_of_ncbi_gene.1812_that_participates_in_GO_0007190_fbc49f20524727a24c7b7effa29bad4a. Note: the Venn diagram reveals that this set is potentially empty (like the intersection of cars and ice cream stands), theoretically permitting the query to range over pairs of gene/process that aren't related through any known protein. However, OWL-DL reasoners will not infer new classes, so the proteins in the intersection of ncbi_gene:1812 and the substances participating in molecular signaling is restricted to the set which have already been entered into the knowledgebase, e.g. like proteinsubclass:p1812_7190_1

proteinsubclass:p1812_7190_1 rdfs:subClassOf _:signalingParticipants_1 .
proteinsubclass:p1812_7190_1 rdfs:subClassOf protein:ncbi_gene.1812 .

ncbi_gene:1812 and go:GO_0007190 have human-readable labels.

ncbi_gene:1812 rdfs:label "Entrez Gene record for human DRD1, 1812" .
go:GO_0007190 rdfs:label "adenylate cyclase activation" .

The addition (@@curation (from a text media)?@@) of another MeSH record gives us another solution:

pubmedRec:11441182 sc:has-as-minor-mesh mesh:D017966 .
article:11441182 sc:identified_by_pmid pubmedRec:11441182 .
ncbi_gene:1812 sc:describes_gene_or_gene_product_mentioned_by article:11441182 .

7.1 Precomputing Inferences

obo:part_of Rule

Figure 2. obo:part_of Rule [SVG image PNG image]

The demonstration query depends on the existence of an obo:part_of (or rdfs:subClassOf) relationship between any part (i.e. any subclass of any step in the sequence) of molecular signaling, and the general identifier for molecular signaling, go:GO_0007166:

?process obo:part_of go:GO_0007166 .

This part_of relationship between _:subPart and _:parentClass is inferred from the following OWL restriction:

_:subPart owl:onProperty obo:part_of .
_:subPart owl:allValuesFrom _:subClass .
_:subClass owl:onProperty rdfs:subClassOf .
_:subClass owl:hasValue _:parentClass .

The symmetric property for rdfs:subClassOf need not be explicitly modeled because the RDF Schema Specification defines subClassOf, including its transitivity. Note that if _:subClass is a subClassOf _:parentClass, then all members of _:subClassOf are of type _:parentClass (as well as _:subClass):

_:subClass owl:onProperty rdf:type .
_:subClass owl:hasValue _:parentClass .

Because the knowledgebase used does not do inferencing, these triples have been pre-computed (forward-chained) and inserted into the knowledgebase. This also simplifies the query; were these triples not pre-computed, the obo:part-of part of the query would be expressed:

?process rdfs:subClassOf ?what .
?what owl:onProperty obo:has_part .
?what owl:someValuesFrom go:GO_0007166 .

would need to query over a transitive closure over the union of the obo:part-of and rdfs:subClassOf rules.

8 Adding a New Data Source

The last data source listed above, SenseLab, was added to an already used database. An accompanying document, Experiences with the conversion of SenseLab databases to RDF/OWL, describes the details of adding it to the KB. With this new data incorporated, the example query could be extended to extract data from the new data source, in this case, discovering the names of receptor proteins associated with the genes discovered in the previous query. In an integrative query of this sort, we can use the results as a starting point for more detailed queries of a particular repository, such as in this case SenseLab.

SELECT ?genename ?processname ?receptor_protein_name

  # PubMeSH includes ?gene_records mentioned in ?articles which are identified by pmid in ?pubmed_records .
  ?pubmed_record sc:has-as-minor-mesh mesh:D017966 .
  ?article sc:identified_by_pmid ?pubmed_record .
  ?gene_record sc:describes_gene_or_gene_product_mentioned_by ?article .

  # The Gene Ontology asserts that foreach ?protein, ?protein ro:has_function [ ro:realized_as ?process ].
  ?protein rdfs:subClassOf ?restriction1 .
  ?restriction1 owl:onProperty ro:has_function .
  ?restriction1 owl:someValuesFrom ?restriction2 .
  ?restriction2 owl:onProperty ro:realized_as .
  ?restriction2 owl:someValuesFrom ?process .
  # Also, foreach ?protein, ?protein has a parent class which is linked by some predicate to ?gene_record.
  ?protein rdfs:subClassOf ?protein_superclass .
  ?protein_superclass owl:equivalentClass ?restriction3 .
  ?restriction3 owl:onProperty dnaGeneProduct:described_by .
  ?restriction3 owl:hasValue ?gene_record .
  # Each ?process (that we are interested in) is a subclass of the signal transduction process.
  ?process obo:part_of go:GO_0007166 .

  ?gene_record rdfs:label ?genename .

  ?process rdfs:label ?processname .

  # Foreach ?gene, ?gene senselab:has_nucleotide_sequence_described_by ?gene_record .
  ?gene owl:equivalentClass ?restriction4 .
  ?restriction4 owl:onProperty senselab:has_nucleotide_sequence_described_by .
  ?restriction4 owl:hasValue ?gene_record .

  # Foreach ?receptor_protein, ?receptor_protein senselab:proteinGeneProductOf ?gene .
  ?receptor_protein rdfs:subClassOf ?restriction5 .
  ?restriction5 owl:onProperty senselab:proteinGeneProductOf .
  ?restriction5 owl:someValuesFrom ?gene .

  # Find the labels of all such ?receptor_proteins.
  ?receptor_protein rdfs:label ?receptor_protein_name

yielding another variable in our results:

gene_record_name processname receptor_protein_name
Entrez Gene record for human DRD1, 1812 adenylate cyclase activation D1 receptor
Entrez Gene record for human ADRB2, 154 adenylate cyclase activation NULL

The additional triples this matched in the SenseLab knowledgebase connect to the existing data by talking about the same genes, e.g. ncbi_gene:1812.

Additional Triples from SenseLab

Figure 3. Additional Triples from SenseLab [SVG image PNG image]

A nucleotide sequence is also described by ncbi_gene:1812. Here, we call this _:nucleo1812.

subject predicate object
_:nucleo1812 owl:onProperty nucleotideSequence:described_by .
_:nucleo1812 owl:hasValue ncbi_gene:1812 .

The class senselab:DRD1_Gene has the same members as the OWL restriction _:nucleo1812.

senselab:DRD1_Gene owl:equivalentClass _:nucleo1812 .

This _:protGeneProd_1 is defined by being a product of DRD1_Gene.

_:protGeneProd_1 owl:onProperty senselab:proteinGeneProductOf .
_:protGeneProd_1 owl:someValuesFrom senselab:DRD1_Gene .

Our solution is a subclass of _:protGeneProd_1 called senselab:D1. @@What other subclasses of _:protGeneProd_1 are there motivating this extra subclassof relationship?@@

senselab:D1 rdfs:subClassOf _:protGeneProd_1 .
senselab:D1 rdfs:label "D1" .

9 Named Graphs

In the Banff Demo, the resulting knowledgebase partitioned the assertions into groups called Named Graphs. This process basically consists of associating a distinct URI with a connected graph of triples, and then referring to that graph via the URI. At the time of publication, any query would be expected to include SPARQL GRAPH constraints, e.g.:

prefix go: <http://purl.org/obo/owl/GO#>
prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>
prefix owl: <http://www.w3.org/2002/07/owl#>
prefix mesh: <http://purl.org/commons/record/mesh/>
prefix sc: <http://purl.org/science/owl/sciencecommons/>
prefix ro: <http://www.obofoundry.org/ro/ro.owl#>
prefix senselab: <http://purl.org/ycmi/senselab/neuron_ontology.owl#>
prefix obo: <http://purl.org/obo/owl/obo#>

SELECT ?genename ?processname ?receptor_protein_name

  # PubMeSH includes ?gene_records mentioned in ?articles which are identified by pmid in ?pubmed_records .
GRAPH <http://purl.org/commons/hcls/pubmesh> {
  ?pubmed_record sc:has-as-minor-mesh mesh:D017966 .
  ?article sc:identified_by_pmid ?pubmed_record .
  ?gene_record sc:describes_gene_or_gene_product_mentioned_by ?article

  # The Gene Ontology asserts that foreach ?protein, ?protein ro:has_function [ ro:realized_as ?process ].
GRAPH <http://purl.org/commons/hcls/goa> {
  ?protein rdfs:subClassOf ?restriction1 .
  ?restriction1 owl:onProperty ro:has_function .
  ?restriction1 owl:someValuesFrom ?restriction2 .
  ?restriction2 owl:onProperty ro:realized_as .
  ?restriction2 owl:someValuesFrom ?process .
  # Also, foreach ?protein, ?protein has a parent class which is linked by some predicate to ?gene_record.
  ?protein rdfs:subClassOf ?protein_superclass .
  ?protein_superclass owl:equivalentClass ?restriction3 .
  ?restriction3 owl:onProperty sc:is_protein_gene_product_of_dna_described_by .
  ?restriction3 owl:hasValue ?gene_record .
  # Each ?process (that we are interested in) is a subclass of the signal transduction process.
  # @@ nested graph constraint
  GRAPH <http://purl.org/commons/hcls/20070416/classrelations> {
      { ?process obo:part_of go:GO_0007166 }
      { ?process rdfs:subClassOf go:GO_0007166 }

GRAPH <http://purl.org/commons/hcls/gene> {
  ?gene_record rdfs:label ?genename

GRAPH <http://purl.org/commons/hcls/20070416> {
  ?process rdfs:label ?processname

GRAPH <http://purl.org/ycmi/senselab/neuron_ontology.owl> {
  # Foreach ?gene, ?gene senselab:has_nucleotide_sequence_described_by ?gene_record .
  ?gene owl:equivalentClass ?restriction4 .
  ?restriction4 owl:onProperty senselab:has_nucleotide_sequence_described_by .
  ?restriction4 owl:hasValue ?gene_record .

  # Foreach ?receptor_protein, ?receptor_protein senselab:proteinGeneProductOf ?gene .
  ?receptor_protein rdfs:subClassOf ?restriction5 .
  ?restriction5 owl:onProperty senselab:proteinGeneProductOf .
  ?restriction5 owl:someValuesFrom ?gene .

  # Find the labels of all such ?receptor_proteins.
  ?receptor_protein rdfs:label ?receptor_protein_name

The named graphs help with both provenance and scaling. In the current approach, each RDF bundle is imported into its own named graph. This is useful for a number of reasons. First, we know the source of each named graph, so we can control and review which data sources are being accessed by our queries. Additionally, the association of a named graph with a data source serves as data provenance and can also be employed by schemes that exploit knowledge about the data source to assign confidence measures in a model of trust. For example, one of the knowledgebase data sources resulted from text mining experiments to find protein associations. Users of the knowledgebase can choose to view this evidence of association differently than the associations provided from a protein-protein interaction database. Also, named graphs support scaling by making it possible to update selected parts of the knowledgebase, for example when the data source has new information or related ontologies are changed.

10 Next Steps

The knowledgebase was initially designed for the purposes of a live demo. The data warehousing that was performed, several design choices in the data, and the resulting queries were all aimed at simplicity and maximal performance. Many choices were guided by the desire for transparency for a broader audience of biomedical informaticists. Several areas of possible improvement are noted here:

There are also a number of open issues that should be addressed in future research:


A RDF Sources

A table of the RDF sources used to create the Knowledgebase:

RDF bundle name Last modifiedSize Description RDF conversion by Terms
aba-2007-08-07.tgz 22-Sep-2007   51M SC's extract of Allen Brain Atlas metadata from their web site. Web site was read on 26 Feb 2007 or shortly before SC terms of use
addgene.ttl 16-May-2007 1.1M Addgene catalog (tab-delimited file) SC provided to Science Commons by Addgene
bams-from-swanson-98-4-23-07.owl 23-Apr-2007 5.6M BAMS HCLS/NIST (John Barkley) released without contract
galen.tgz 22-Sep-2007 1.9M Galen from co-ode.org - released without contract
gene-owl.tgz 08-May-2007 7.7M Extract from ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene_info.gz HCLS/HP (Ray Hookway) NCBI Copyright and Disclaimers
gene-pubmed.ttl.tgz 08-May-2007 1.5M Extract from Entrez Gene HCLS/HP NCBI Copyright and Disclaimers
goa-in-owl.tgz 16-May-2007 73M GO annotations from NCBI and EBI HCLS/SC NCBI Copyright and Disclaimers; EBI terms of use
homologene.tgz 16-May-2007 626K Homologene HCLS/SC NCBI Copyright and Disclaimers
(contact Medline for use terms)
16-May-2007 758M List of all associations of MeSH headings to papers indexed by Medline extracted from 2007 Medline baseline distribution HCLS/SC License Agreement to Lease NLM Databases in Machine-Readable Form - see below
(contact Medline for use terms)
16-May-2007 670M Extracted from 2007 Medline baseline distribution HCLS/SC see below
mesh-qualified-headings.ttl.gz 30-Apr-2007 13M NLM 2007 MeSH descriptor/qualifier pairs HCLS/SC MeSH MOU
mesh-skos.tgz 16-May-2007 13M NLM 2007 MeSH van Assem et al MeSH MOU
mesh07-eswc06.rdfs 28-Jun-2007 2.2K van Assem et al's ontology (used by output of MeSH to SKOS conversion) - released without contract
neurocommons-text-mining.tgz 05-May-2007 24M Neurocommons text mining pilot - extracted from Temis software applied to 7% of Medline records (SC) - released without contract
obo-all.tgz 22-Sep-2007 36M All OBO ontologies, from berkeleypop - released without contract
obo-in-owl.tgz 16-May-2007 2.6M selected OBO ontologies, downloaded ~21 April 2007, augmented with inferred relations HCLS/SC released without contract
sciencecommons.owl 28-Jun-2007 19K ad hoc ontology from Science Commons used by the output of of several of the conversion scripts - released without contract
senselab.tgz 16-May-2007 216K From Yale Senselab HCLS/Yale (Cheung, Samwald, et al.) released without contract

B Schema

This table describes the classes and properties used in the knowledgebase:

sc:describes_gene_or_gene_product_mentioned_by sc:Article
rdfs:label Gene Label
sc:Article .
sc:identified_by_pmid sc:Paper
sc:Paper .
sc:has-as-minor-mesh sc:PubMedId
sc:PubMedId .

C References

Primer: Getting into RDF and Semantic Web using N3, http://www.w3.org/2000/10/swap/Primer .
[OWL Overview]
OWL Web Ontology Language Overview, Deborah L. McGuinness and Frank van Harmelen, Editors, W3C Recommendation, 10 February 2004, http://www.w3.org/TR/2004/REC-owl-features-20040210/ . Latest version available at http://www.w3.org/TR/owl-features/ .
[OWL Guide]
OWL Web Ontology Language Guide, Michael K. Smith, Chris Welty, and Deborah L. McGuinness, Editors, W3C Recommendation, 10 February 2004, http://www.w3.org/TR/2004/REC-owl-guide-20040210/ . Latest version available at http://www.w3.org/TR/owl-guide/ .
[OWL Semantics and Abstract Syntax]
OWL Web Ontology Language Semantics and Abstract Syntax, Peter F. Patel-Schneider, Patrick Hayes, and Ian Horrocks, Editors, W3C Recommendation, 10 February 2004, http://www.w3.org/TR/2004/REC-owl-semantics-20040210/ . Latest version available at http://www.w3.org/TR/owl-semantics/ .
Resource Description Framework (RDF) Model and Syntax Specification , Ora Lassila, Ralph R. Swick, Editors. World Wide Web Consortium Recommendation, 1999,
Latest version available at http://www.w3.org/TR/REC-rdf-syntax/.
Resource Description Framework (RDF): Concepts and Abstract Syntax , G. Klyne, J. J. Carroll, Editors, W3C Recommendation, 10 February 2004, http://www.w3.org/TR/2004/REC-rdf-concepts-20040210/ . Latest version available at http://www.w3.org/TR/rdf-concepts/ .
RDF Vocabulary Description Language 1.0: RDF Schema , Dan Brickley and R.V. Guha, Editors. W3C Recommendation, 10 February 2004,
http://www.w3.org/TR/2004/REC-rdf-schema-20040210/ .
Latest version available at http://www.w3.org/TR/rdf-schema/.
[RDF Semantics]
RDF Semantics, Pat Hayes, Editor, W3C Recommendation, 10 February 2004, http://www.w3.org/TR/2004/REC-rdf-mt-20040210/ . Latest version available at http://www.w3.org/TR/rdf-mt/ .
[RDF Vocabulary]
RDF Vocabulary Description Language 1.0: RDF Schema, Dan Brickley and R. V. Guha, Editors, W3C Recommendation, 10 February 2004, http://www.w3.org/TR/2004/REC-rdf-schema-20040210/ . Latest version available at http://www.w3.org/TR/rdf-schema/ .
SPARQL Query Language for RDF, E. Prud'hommeaux, A. Seaborne, Editors. World Wide Web Consortium. 19 April 2005. Work in progress. This version is http://www.w3.org/TR/2005/WD-rdf-sparql-query-20050419/. The latest version of SPARQL Query Language for RDF is available at http://www.w3.org/TR/rdf-sparql-query/.
A relational algebra for SPARQL, Richard Cyganiak, 2005
Semantics of SPARQL, Jorge Pérez, Marcelo Arenas, and Claudio Gutierrez, 2006

D Additional Resources

The knowledgebase has been installed at several locations. Below are locations that also provide SPARQL query access:

Below are a few visual interfaces that make it possible to browse the results of a search on the knowledgebase:

We used the open source edition of the Openlink Virtuoso repository from http://sourceforge.net/projects/virtuoso/.

The actions and scripts that were used to create the knowledgebase on a commodity PC have been documented by several HCLS members. The necessary instructions and scripts that were used will be listed here as completely as possible:

The following resources may be of interest for future work:

E Acknowledgements (Informative)

Special thanks to Alan Ruttenberg who coordinated the assembly of the data sets and presented the initial version of the knowledgebase at the Banff demo.


Many contributed to the knowledgebase and its documentation, as well as the thoughts behind it, including John Barkley (NIST), Olivier Bodenreider (NLM, NIH), William Bug (School of Medicine, UCSD), Huajun Chen (Zhejiang University), Paolo Ciccarese (SWAN), Kei Cheung (SenseLab, Yale), Tim Clark (SWAN), Don Doherty (Brainstage Research Inc.), Michel Dumontier (Carleton University), Kerstin Forsberg (AstraZeneca), Ray Hookaway (HP), Vipul Kashyap (Partners Healthcare), June Kinoshita (AlzForum), Joanne Luciano (Harvard Medical School), M. Scott Marshall (University of Amsterdam), Chris Mungall (NCBO), Eric Neumann (Clinical Semantics Group), Eric Prud’hommeaux (W3C), Jonathan Rees (Science Commons), Alan Ruttenberg (Science Commons), Matthias Samwald (Medical University of Vienna), Susie Stephens (Eli Lilly), Mike Travers, Gwen Wong (SWAN), Elizabeth Wu (SWAN)

Data providers:

Judith Blake (MGD.), Mikail Bota (BAMS), David Hill (MGD), Oliver Hoffman (CL), Minna Lehvaslaiho (CL), Colin Knep (Alzforum), Maryanne Martone (CCDB), Susan McClatchy (MGD), Simon Twigger (RGD), Allen Brain Institute.