In the life sciences, high-throughput technologies deliver ever-growing amounts of heterogeneous (meta) data at different scales, which are produced, stored and analysed in both structured and semi-structured formats. Systems Biology is an integrative discipline that uses various integration strategies to model and discover properties of biological systems. Integration and analysis of heterogeneous biological data and knowledge require efficient information retrieval and management systems and Semantic Web technologies are designed to meet this challenge [1].

The RDF data model is a mature W3C standard [2, 3] designed for the integrated representation of heterogeneous information from disparate sources and it is proving effective for creating and sharing biological data. RDF is not a data format, but a data model for describing resources in the form of self-descriptive subject, predicate and object triples that can be linked in an RDF-graph. Integration of heterogeneous data from different sources in a single graph relies on using retrievable controlled vocabularies, which is essential to access and analyse integrated data [4]. Once data sources are converted into the semantic Web, SPARQL [5, 6] can be used to query multiple of these resources, simultaneously or consecutively, without further modifying any of them.

Widely used biological resources such as Reactome, ChEBI and UniProt, among others, [7–10] have been transformed into the RDF data model and the Bio2RDF [11] project has transformed a large set of additional sources, such as the NCBI GenBank files [12], DrugBank [13] and InterPro database [14]. Additionally, there are on-going efforts to develop tools providing results in this data model, such as the Semantic Annotation Platform for Prokaryotes, SAPP, (J. Koehorst, J. van Dam et al. personal communication) that provides genome functional annotation in the RDF data model.

These RDF resources can be readily queried with SPARQL. Constructing SPARQL queries requires that the user has a mental representation of the underlying structure of the resource. The structure of a resource is the set of object types and their relationships, i.e. the explicit representation of the predicates linking different classes. This structure represents the set of semantic constraints embedded in the resource. In a biological database containing information on biochemical reactions, genes and their identifiers are linked to proteins; proteins are linked to EC numbers; EC numbers are connected to reactions that involve metabolites as products and substrates. To retrieve information pertaining metabolites and genes, the SPARQL query has to obey the specific network topology linking these types of objects. RDF data sources do not need a predefined scheme so that new data types can be added at any time expanding the underlying structure. If the modifications in the underlying structure generated by this new data are not known, linked information cannot be retrieved. Not having a clear idea of the underlying structure makes querying an RDF resource inefficient, time consuming, or even impossible.

The structure of a resource can be either retrieved through manual queries or it can be provided by the data publishers in the documentation of the resource. This structural information can be encoded using Web Ontology Language (OWL) files [15]. OWL was created as a description logic language and it is intended for automatic reasoning; nevertheless, its axioms can also be used to construct a graphical overview of the described resource [16]. However, the OWL standard does not require all axioms necessary for such reconstruction. Examples of necessary axioms not obligated by this standard are object all values from and data all values from. In some of the resources created by the Bio2RDF project these axioms (object all values from and data all values from) are not provided. Furthermore, the ontology generation process is, at best, semi-automatic, time consuming and error-prone. Errors might also accumulate due to the conversion code used to generate the RDF resource, as the triple generating code can contain lexical errors in predicate definition such as typos, inconsistent usage of upper and lower case, or misspelled words, thereby populating a resource containing information on proteins with information on “porteins”, which describes proteins associated to transmembrane transport. These errors lead to descriptions of the intended content of the resource rather than of its actual content.

Shape Expressions (ShEx) is a standard to describe, validate and transform RDF data. One of the goals of this standard is to create an easy to read language for the validation of instance data, however, it is still being developed and no final recommendation is yet available [17–19].

Computational tools able to reconstruct the structure of RDF resources are thus required to i) facilitate query writing and to ii) enable data providers to verify the structural integrity of their resource. To our knowledge, no such tool, able to automatically recover the structure of the resource and the associated multiplicity of the predicates, exist. Semscape [20] is an already existing Cytoscape [21] app that is able to retrieve to some extent the structure of the resource. However, it has limited recovery and simplification capabilities, leading to unreadable hairballs for larger structures. Furthermore, additional statistical information about the classes and links is not retained. Here we present RDF2Graph, a tool to automatically recover the structure of an RDF resource and to generate a visualization, ShEx file and/or an OWL ontology thereof. These can be used to write SPARQL queries or to verify (generated) RDF resources.

RDF2Graph performs two distinct processes to retrieve the structure of a resource. Initially, there is a recovery of all classes, predicates and unique type links together with their associated statistics. In the second stage there is a simplification step to arrive to a neat structural overview. A simplified overview of the complete process is given in Fig. 1.

A type link is defined as a link joining a subject class type to an object class or value data type, via a predicate. A unique type link is defined as a unique tuple: type of subject, predicate, (data)type of object. For the triple <:BRCA1, :locatedOn, :chromosome17> the type link is <:Gene, :locatedOn, :Chromosome>. When considering the full resource, all type links <:Gene, :locatedOn, :Chromosome> correspond to the same unique type link. In the triple <:Adam :hasSon :Bob> the type link is <:Person, :hasSon, :Person>.

The multiplicity of a unique type link describes the number of instances connected to each other. The forward multiplicity can be: i) One-to-one (also denoted: 1..1) each source instance has exactly one reference to the target; ii) One-or-many (1..N) each source instance has one or more references to the target; iii) Zero-or-one (0..1) some source instances have at most one reference to the target; iv) Zero-or-many (0..N) some source instances have one or even more than one reference to the target. Similarly, for the reverse multiplicity the roles of target and source are inverted. In the previous examples, the forward multiplicity of the unique type link <:Gene, :locatedOn, :Chromosome> is (1:1) since each human gene is associated to one and only one chromosome, whereas the reverse multiplicity is (1..N) since a chromosome contains many genes. In the second case <:Person, :hasSon, :Person>, the forward multiplicity is (0..N) since there is no limitation to the number of sons a person may have; in this case the reverse multiplicity is (N = 2..1) given that each son has two parents.

The initial recovery process is performed through a series of SPARQL queries on the selected endpoint. Detailed information about the SPARQL queries and the queries themselves are provided in RDF2Graph’s documentation. These queries can be adapted to change the introduced limitations and to customise the tool for specific end points. The queries can be limited to reduce the running time since this process can take between a few minutes for a resource with a million triples, to several days for a resource with 16 billion triples, such as the RDF version of UniProt, as described in the ‘Results’ section. However the limitation in the number of retrieved triples may lead to incompleteness of the recovered structure, since some type links could be missed. This may cause that for some unique type links not all type links are retrieved, which can cause errors in the calculation of the multiplicities (forward and/or reverse). It may also lead to some unique type links not being identified if no type links associated to them are found. Therefore, we advise caution when using these limitations.

After the initial recovery of type links and unique type links, a simplification process follows, in which type links with a common parent class for either the subject or object types are merged. These process proceeds in a pairwise manner, so that at each iteration only two unique type links sharing either the subject type or the object (data)type are considered. If more than two unique type links are present, the first two are merged, and their result is combined with the next one and so on until all have been considered. Therefore, only two unique type links at a time are merged. Figure 2 represents the cases that need to be considered when analysing two unique type links. In principle, other cases involving the “sameAs” relationship could appear, but in our approach, the “subClassOf” relationship also includes the “sameAs” relationship, which reduces all possible cases to the ones represented in Fig. 2.

This process also allows the identification of concept classes. A concept class is defined as a class that has no instances and no subclasses with some instances. A typical set of examples of concept classes are all the GO classes in the GO database [22]. This concept is needed to support the exclusion of them in the network view as they have little value for the structural overview and will overcrowd the network visualization.

All classes identified in the recovery process and associated subclassOf links are loaded into a memory based directed graph. This class tree is then used in the merging process. During the merging process five steps are executed per retrieved predicate. Step 1 is the initialization; step 2 performs the merging in case A and steps 3 and 4 are used for case B, whereas case C is the combination of cases A and B from Fig. 2; step 5 is the fictionalization step.

Results are stored in a local triple store that contains the unique type links and their count (number of type links associated to them) together with their forward and reverse multiplicities.

To store information for the new concept of unique type links we developed a new ontology. Figure 3 depicts the elements within this ontology that are related to storage of the unique type links. Each unique type link links to an object type which is either: i) a class; ii) a data type, such as xsd:integer; iii) external, a subject in another resource; or iv) invalid, a subject with no defined type. In each class the class property groups the associated unique type links per predicate and links them to the rdfs:Property. Additionally, the number of occurrences are stored for each class and predicate.

The RDF version of the UniProt database contains more than 16 billion triples which is more than one thousand times the number of triples present in the RDF version of Reactome. Therefore there are huge differences in the time required for the recovery process. More than 99 % of the CPU time spent in the recovery process is consumed by the SPARQL endpoint.

Given the size of the UniProt resource, we had to impose a limitation on the number of considered type links per predicate (100.000). With this limitation the recovery process required more than 740 hours (4 days on 8 cores on a 2.3 GHz computer). On the other hand, the time required to retrieve the structure of Reactome was of only half an hour. This shows that the relationship between the recovery time and the number of triples is non linear. This nonlinearity can be attributed to the higher memory requirements associated with a larger resource but also to the intrinsic differences in the structure of the resource, given by the different values of the total number of unique type links and the average number of type links per class property (see Table 1).

Even though the UniProt RDF resource has around 40 times more triples than ChEBI RDF, they have a similar number of triples in the local resource. This is due to the high number of concept classes and subClassOf relationships that can be found in ChEBI, for example the subClassOf relationship associated with galactose is an aldohexose.

The number of triples in the local triple store does not necessarily grow with the number of triples in the resource, since the number of triples in the local triple store is associated with the complexity and the number of classes in the resource, but not with the number of occurrences of each unique type link. Table 1 shows that the number of triples in the local triple store is roughly equal to twice the number of classes in the resource plus eight times the number of unique type links. The number of classes and relationships that can be recovered is limited by the amount of triples that the local triple store can handle. In our case (Jena TDB) that would correspond to an upper limit of roughly 10⁷ unique type links and classes. So, in practice, the only limitations are given by the restrictions on the SPARQL endpoint imposed by data providers and not by the storage capacity in the local triple store.

Figures 4, 5, 6 and 7 provide graphical representations of the retrieved structures for these resources (SAPP, ChEBI Uniprot and Reactome) respectively. The nodes in these representations correspond to classes with instances, whereas the edges represent the unique type links with determined multiplicity. See Additional file 1 for additional output of RDF2Graph regarding these resources (OWL, XGMML, ShEx and the RDF of the local store).

The simplification process reduces the number of unique type links from 40 % to 80 % in the selected resources. Thereby providing a neat structural overview that can be browsed for query building.

There is a small subset of unique type links for which the multiplicity could not be determined. These correspond to unique type links referencing to an invalid subject involved in a set of triples but without a defined type. These links were identified using the XGMML exporter.

We used RDF2Graph to incrementally develop and improve SAPP, our semantic annotation tool. For each incremental improvement we recovered the structure and used the graphical overview to assess the integrity of the resource and compare its intended and the actual content. We manually verified every class, associated properties and type links to identify and solve a number of issues, such as capitalization errors, predicate naming errors, faulty URI’s, broken links, missing attributes and type definitions, unwanted interconnections and faulty multiplicities. For example, a broken link will appear as a reference to an external resource where a reference to another class would be expected. A predicate naming error in one of the RDF exporter functions will cause some subjects to be in triples with the “wrong” predicate and will change the multiplicity from 1..1 to 0..1. Finally, the OWL exporter was used to generate an OWL file requiring little manual curation to complete it.

Similarly we verified the structure of the UniProt RDF resource. The XGMML exported reported 24 issues, most of them associated to subjects with the same missing class definition (see Additional file 1). Additionally we manually compared the provided OWL file with the one created by the RDF2Graph OWL exporter. We detected a set of practical issues such as missing type definitions, references linking the wrong type of objects, incorrect multiplicities and mismatches between the descriptive OWL file and the actual content. These have been reported and will result in an improvement of the quality of this important resource.

The recovered structures and their associated statistics about classes, predicates and type links were successfully used to create multiple complex queries. For instance, the retrieved structure of Reactome is depicted in Fig. 7, panel A. Using this structural information we created the query in Fig. 7, panel B. This query extracts from Reactome the names of all pathways associated to a specific gene identifier. Through the use of the structural overview we were able to find and follow multiple links from the gene or protein of interest to the associated pathways.

RDF2Graph is distributed under MIT license and it is freely available from https://github.com/jessevdam/RDF2Graph. RDF2Graph is written in Java, SPARQL and bash. RDF2GRaph runs under Linux and Mac OS X (tested under version 10.11) however, a virtual machine is also distributed with the version described in this manuscript. Furthermore a Galaxy interface is available at http://semantics.systemsbiology.nl/RDF2Graph/. The RDF resource size, in this case, is limited to 20.000.000 lines.

Maven 2 [32] is required for installation and the resulting jar can be executed with Java using bash or alike. In addition it requires Jena to host the local temporary RDF store and Cytoscape (version 3.x) [21] to generate the network based overview. See the RDF2Graph manual enclosed in the Git repository for more details.