Basic fusion of RDF data in SPARQL

A need to fuse data often arises when you combine multiple datasets. The combined datasets may contain descriptions of the same things that are given different identifiers. If possible, the descriptions of the same thing should be fused into one to simplify querying over the combined data. However, the problem with different co-referent identifiers also appears frequently in a single dataset. If a thing does not have an identifier, then it must be referred to by its description. Likewise, if the dataset's format does not support using identifiers as links, then things must also be referred to by their descriptions. For example, a company referenced from several public contracts as their supplier may have a registered legal entity number, yet its description is duplicated in each awarded contract instead of linking the company by its identifier due to the limitations of the format storing the data, such as CSV.

Fusing descriptions of things is a recurrent task both in intergration of multiple RDF datasets and in transformations of non-RDF data to RDF. Since fusion of RDF data can be complex, there are dedicated data fusion tools, such as Sieve or LD-FusionTool, that can help formulate and execute intricate fusion policies. However, in this post I will deal with basic fusion of RDF data using the humble SPARQL 1.1 Update, which is readily available in most RDF stores and many ETL tools for processing RDF data, such as LinkedPipes-ETL. Moreover, a basic data fusion is widely applicable in many scenarios, which is why I wanted to share several simple ways for approaching it.

Content-based addressing

In the absence of an external identifier, a thing can be identified with a blank node in RDF. Since blank nodes are local identifiers and no two blank nodes are the same, using them can eventually lead to proliferation of aliases for equivalent things. One practice that ameliorates this issue is content-based addressing. Instead of identifying a thing with an arbitrary name, such as a blank node, its name is derived from its description; usually by applying a hash function. This turns the “Web of Names” into the Web of Hashes. Using hash-based IRIs for naming things in RDF completely sidesteps having to fuse aliases with the same description. This is how you can rewrite blank nodes to hash-based IRIs in SPARQL Update and thus merge duplicate data:

In practice, you may want to restrict the renamed resources to those that feature some minimal description that makes them distinguishable. Instead of selecting all blank nodes, you can select those that match a specific graph pattern. This way, you can avoid merging underspecified resources. For example, the following two addresses, for which we only know that they are located in the Czech Republic, are unlikely the same:

@prefix : <http://schema.org/> .

[ a :PostalAddress ;
  :addressCountry "CZ" ] .

[ a :PostalAddress ;
  :addressCountry "CZ" ] .

More restrictive graph patterns also work to your advantage in case of larger datasets. By lowering the complexity of your SPARQL updates, they reduce the chance of you running into out of memory errors or timeouts.

Hash-based fusion

Hashes can be used as keys not only for blank nodes. Using SPARQL, we can select resources satisfying a given graph pattern and fuse them based on their hashed descriptions. Let's have a tiny sample dataset that features duplicate resources:

If you want to merge equivalent resources instantiating class :C (i.e. :r1, :r2, and :r5), you can do it using the following SPARQL update:

The downside of this method is that the order of bindings in GROUP_CONCAT cannot be set explicitly, nor it is guaranteed to be deterministic. In theory, you may get different concatenations for the same set of bindings. In practice, RDF stores typically concatenate bindings in the same order, which makes this method work.

Fusing subset descriptions

If we fuse resources by hashes of their descriptions, only those with the exact same descriptions are fused. Resources that differ in a value or are described with different properties will not get fused together, because they will have distinct hashes. Nevertheless, we may want to fuse resources with a resource that is described by a superset of their descriptions. For example, we may want to merge the following blank nodes, since the description of the first one is a subset of the second one's description:

@prefix : <http://schema.org/> .

[ a :Organization ;
  :name "ACME Inc." ] .

[ a :Organization ;
  :name "ACME Inc." ;
  :description "The best company in the world."@en ] .

Resources with subset descriptions can be fused in SPARQL Update using double negation:

The above-mentioned caveats apply in this case too, so you can use a more specific graph pattern to avoid merging underspecified resources. The update may execute several rewrites until reaching the largest superset, which makes it inefficient and slow.

Key-based fusion

If you want to fuse resources with unequal descriptions that are not all subsets of one resource's description, a key to identify the resources to fuse must be defined. Keys can be simple, represented by a single inverse functional property, or compound, represented by a combination of properties. For instance, it may be reasonable to fuse the following resources on the basis of shared values for the properties rdf:type and :name:

@prefix :    <http://schema.org/> .

[ a :Organization ;
  :name "ACME Inc." ;
  :foundingDate "1960-01-01"^^xsd:date ;
  :email "contact@acme.com" ;
  :description "The worst company in the world."@en ] .

[ a :Organization ;
  :name "ACME Inc." ;
  :foundingDate "1963-01-01"^^xsd:date ;

To fuse resources by key, we group them by the key properties, select one of them, and rewrite the others to the selected one:

If we fuse the resources in the example above, we can get the following:

@prefix :    <http://schema.org/> .

[ a :Organization ;
  :name "ACME Inc." ;
  :foundingDate "1960-01-01"^^xsd:date, "1963-01-01"^^xsd:date ;
  :email "contact@acme.com" ;
  :description "The best company in the world."@en, "The worst company in the world."@en ] .

This example illustrates how fusion highlights problems in data, including conflicting values of functional properties, such as the :foundingDate, or contradicting values of other properties, such as the :description. However, resolving these conflicts is a complex task that is beyond the scope of this post.


While I found the presented methods for data fusion to be applicable for a variety of datasets, they may fare worse for complex or large datasets. Besides the concern of correctness, one has to weigh the concern of performance. Based on my experience so far, the hash-based and key-based methods are usually remarkably performant, while the methods featuring double negation are not. Nonetheless, the SPARQL updates from this post can be oftentimes simply copied and pasted and work out of the box (having tweaked the graph patterns selecting the fused resources).


On generating SPARQL

The question how to generate SPARQL comes so often in my work that I figured I attempt a summary of the different approaches that answer this question, none of which is established enough to be considered a best practice. The lack of a well-established method for generating SPARQL may have arisen from the fact that SPARQL is serialized to strings. While its string-based format may be convenient to write by hand, it is less convenient to generate through code. To make SPARQL more amenable to programmatic manipulation, several approaches have been devised, some of which I will cover in this post.

Opting for strings or structured data to represent a data manipulation language is a fundamental design decision. Dumbing the decision down, there is a usability trade-off to be made: either adopt a string representation to ease manual authoring or go for a representation using data to ease programmatic manipulation. Can we nevertheless have the best of both options?

SPARQL continues in the string-based tradition of SQL; possibly leveraging a superficial familiarity between the syntaxes. In fact, SPARQL was recently assessed as “a string-based query language, as opposed to a composable data API.” This assessment implicitly reveals that there is a demand for languages represented as structured data, such as the Elasticsearch query DSL serialized in JSON or the Datomic query syntax, which is serialized in EDN.

To illustrate the approaches for generating SPARQL I decided to show how they fare on an example task. The chosen example should be simple enough, yet realistic, and such that it demonstrates the common problems in encountered when generating SPARQL.


Let's say you want to know all the people (i.e. instances of foaf:Person) known to DBpedia. There are 1.8 million such persons, which is way too many to fetch in a single SPARQL query. In order to avoid overload, DBpedia's SPARQL endpoint is configured to provide at most 10 thousand results. Since we want to get all the results, we need to use paging via LIMIT and OFFSET to partition the complete results into smaller parts, such that one part can be retrieved within a single query.

Paging requires a stable sort order over the complete collection of results. However, sorting a large collection of RDF resources is an expensive operation. If a collection's size exceeds a pre-configured limit, Virtuoso requires the queries paging over this collection to use scrollable cursors (see the section “Example: Prevent Limits of Sorted LIMIT/OFFSET query”), which basically wrap an ordered query into a subquery in order to better leverage the temporary storage of the sorted collection. Because of the number of persons in DBpedia we need to apply this technique to our query.

Let's say that for each person we want to get values of several required properties and some optional properties. For example, we may want to get names (foaf:name) and birth dates (dbo:birthDate) and, optionally, dates of death (dbo:deathDate). Since persons can have multiple names and we want only one name per person, we need to use SAMPLE with the names to retrieve a single random name associated with each person. We could assume that a person has no more than one birth date and death date, but in fact in DBpedia there are 31 thousand persons with multiple birth dates and 12 thousand persons with multiple death dates, so we also need to use SAMPLE for these properties. Considering all these requirements, our SPARQL query may look like the following.

We need the LIMIT and OFFSET in this query to be generated dynamically, as well as the listing of the required and the optional properties. In the following, I will cover and compare several approaches for generating SPARQL queries using these parameters. All the approaches are illustrated by examples in Clojure to make them better comparable.

String concatenation

The approach that is readily at hand of most developers is string concatenation. While it is simple to start, it intertwines SPARQL with code, which makes it brittle and error prone. Convoluted manual escaping may be needed and the result is particularly messy in programming languages that lack support of multi-line strings, such as JavaScript. Here is an example implementation using string concatenation to generate the above-mentioned query.

Parameterized queries

If queries to be generated differ only in few variables, they can be generated from a parameterized query, which represents a generic template that can be filled with specific parameter values at runtime. Parameterized queries enable to split static and dynamic parts of the generated queries. While the static query template is represented in SPARQL and can be stored in a separate file, the dynamic parts can be represented using a programming language's data types and passed to the template at runtime. The separation provides both the readability of SPARQL and the expressiveness of programming languages. For example, template parameters can be statically typed in order to improve error reporting during development or avoid some SPARQL injection attacks in production.

Although parameterized queries improve on string concatenation and are highlighted among the linked data patterns, they are limited. In particular, I will discuss the limitations of parameterized queries as implemented in Apache Jena by ParameterizedSparqlString. While the main drawbacks of parameterized queries are true for other implementations as well, the details may differ. In Jena's parameterized queries only variables can be dynamic. Moreover, each variable can be bound only to a single value. For example, for the pattern ?s a ?class . we cannot bind ?class to schema:Organization and schema:Place to produce ?s a schema:Organization, schema:Place .. If we provide multiple bindings for a variable, only the last one is used. Queries that cannot restrict their dynamic parts to variables can escape to using string buffers to append arbitrary strings to the queries, but doing so gives you the same problems string concatenation has. Due to these restrictions we cannot generate the example query using this approach. Here is a partial implementation that generates only the limit and offset.

Jena also provides a similar approach closer to prepared statements in SQL. When executing a query (via QueryExecutionFactory) you can provide it with pre-bound variables (via QuerySolutionMap). Similar restrictions to those discussed above apply. Moreover, your template must be a syntactically valid SPARQL query or update. In turn, this prohibits generating LIMIT numbers, because LIMIT ?limit is not a valid SPARQL syntax. The following implementation thus does not work.


If we need higher expressivity for generating SPARQL, we can use a templating language. Templating is not restricted to the syntax of SPARQL, because it treats SPARQL as arbitrary text, so that any manipulation is allowed. As is the case with parameterized queries, templating enables to separate the static template from the dynamic parts of SPARQL. Unlike parameterized queries, a template is not represented in pure SPARQL, but in a mix of SPARQL and a templating language. This recalls some of the shortcomings of interposing SPARQL with code in string concatenation. Moreover, templates generally do not constrain their input, for example by declaring types, so that any data can be passed into the templates without it being checked in advance. Notable exceptions allowing to declare types of template data are available in Scala; for example Scalate or Twirl.

In order to show how to generate SPARQL via templating I adopted Mustache, which is a minimalistic and widely known templating language implemented in many programming languages. The following is a Mustache template that can generate the example query.

Rendering this template requires little code that provides the template with input data.

Domain-specific languages

As the string concatenation approach shows, SPARQL can be built using programming language construct. Whereas string concatenation operates on the low level of text manipulation, programming languages can be used to create constructs operating on a higher level closer to SPARQL. In this way, programming languages can be built up to domain-specific languages (DSLs) that compile to SPARQL. DSLs retain the expressivity of the programming languages they are defined in, while providing a syntax closer to SPARQL, thus reducing the cognitive overhead when translating SPARQL into code. However, when using or designing DSLs, we need to be careful about the potential clashes between names in the modelled language and the programming language the DSL is implemented in. For example, concat is used both in SPARQL and Clojure. Conversely, if a DSL lacks a part of the modelled language, escape hatches may be needed, regressing back to string concatenation.

Lisps, Clojure included, are uniquely positioned to serve as languages for defining DSLs. Since Lisp code is represented using data structures, it is easier to manipulate than languages represented as strings, such as SPARQL.

Matsu is a Clojure library that provides a DSL for constructing SPARQL via macros. Macros are expanded at compile time, so when they generate SPARQL they generally cannot access data that becomes available only at runtime. To a limited degree it is possible to work around this limitation by invoking the Clojure reader at runtime. Moreover, since Matsu is built using macros, we need to use macros to extend it. An example of such approach is its in-built the defquery macro that allows to pass parameters into a query template. Nevertheless, mixing macros with runtime data quickly becomes convoluted, especially if larger parts of SPARQL need to be generated dynamically.

If we consider using Matsu for generating the example query, we discover several problems that prevent us from accomplishing the desired outcome, apart from the already mentioned generic issues of macros. For instance, Matsu does not support subqueries. Defining subqueries separately and composing them as subqueries via raw input is also not possible, because Matsu queries contain prefix declarations, which are syntactically invalid in subqueries. Ultimately, the farthest I was able to get with Matsu for the example query was merely to the inner-most subquery.

Query DSLs in object-oriented langauges are often called query builders. For example, Jena provides a query builder that allows to build SPARQL by manipulating Java objects. The query builder is deeply vested in the Jena object model, which provides some type checking at the expense of a more verbose syntax. Since Clojure allows to call Java directly, implementing the example query using the query builder is straightforward.

While Matsu represents queries via macros and Jena's query builder does so via code, there is another option: representing queries via data. Using a programming language's native data structures for representing SPARQL provides arguably the best facility for programmatic manipulation. Data is transparent at runtime and as such it can be easily composed and inspected. In fact, a widespread Clojure design rule is to prefer functions over macros and data over functions. An example of using data to represent a SPARQL-like query language in Clojure is the Fabric DSL. While this DSL is not exactly SPARQL, it is “highly inspired by the W3C SPARQL language, albeit expressed in a more Clojuresque way and not limited to RDF semantics” (source).


An approach that uses data in RDF for representing SPARQL is SPIN RDF. It offers an RDF syntax for SPARQL and an API for manipulating it. While the translation of SPARQL to RDF is for the most part straightforward, one of its more intricate parts is using RDF collections for maintaining order in triple patterns or projected bindings, because the collections are difficult to manipulate in SPARQL.

Nonetheless, SPIN RDF seems to have a fundamental problem with passing dynamic parameters from code. For what I can tell, the membrane between SPIN RDF and code is impermeable. It would seem natural to manipulate SPIN RDF via SPARQL Update. However, how can you pass data to the SPARQL Update from your code? If you adopt SPIN RDF wholesale, your SPARQL Update operation is represented in RDF, so you have the same problem. Passing data from code to SPIN RDF thus results in a recursive paradox. Although I tried hard, I have not found a solution to this conundrum in the SPIN RDF documentation, nor in the source code of SPIN API.

This is how the example query can be represented using SPIN RDF; albeit using fixed values in place of the dynamic parts due to the limitations discussed above.

Rendering SPIN RDF to SPARQL can be implemented using the following code.

I have found a way to generate dynamic SPARQL queries in SPIN RDF using JSON-LD. JSON-LD can be represented by data structures, such as hash maps or arrays, that are available in most programming languages. This representation can be serialized to JSON that can be interpreted as RDF using the JSON-LD syntax. SPIN RDF can be in turn translated as SPARQL, obtaining our desired outcome. As may be apparent from this workflow, crossing that many syntaxes (Clojure, JSON-LD, RDF, SPIN, and SPARQL) requires large cognitive effort due to the mappings between the syntaxes one has to be aware of when authoring SPARQL in this way. Here is an implementation of this approach for the example query.

SPARQL algebra

As previously mentioned, a problematic part of SPIN RDF is its use of RDF collections for representing order. The documentation of Apache Jena recognizes this, saying that “RDF itself is often the most appropriate way to do this, but sometimes it isn't so convenient. An algebra expression is a tree, and order matters.” (source). The documentation talks about SPARQL algebra, which formalizes the low-level algebraic operators into which SPARQL is compiled. Instead of using RDF, Jena represents SPARQL algebra in s-expressions (SSE), which are commonly used in programming languages based on Lisp, such as Scheme. In fact, the “SSE syntax is almost valid Scheme” (source), but the SSE's documention acknowledges that Lisps “lacks convenient syntax for the RDF terms themselves” (source).

In order to see how our example query looks in SSE we can use Jena's command-line tools and invoke qparse --print=op --file query.rq to convert the query into SSE. The following is the result we get.

If SSEs were valid Clojure data structures, we could manipulate them as data and then serialize them to SPARQL. Nevertheless, there are minor differences between SSE and the syntax of Clojure. For example, while ?name and _:a are valid symbols in Clojure, absolute IRIs enclosed in angle brackets, such as <http://dbpedia.org/ontology/>, are not. Possibly, these differences can be remedied by using tagged literals for RDF terms.


I hope this post gave you a flavour of the various approaches for generating SPARQL. There is an apparent impedance mismatch between the current programming languages and SPARQL. While the programming languages operate with data structures and objects, SPARQL must be eventually produced as a string. This mismatch motivates the development of approaches for generating SPARQL, which is presented with many challenges, some of which I described in the post.

I assessed these approaches on the basis of how they fare on generating an example query using the data from DBpedia. The complete implementations of these approaches are available in this repository. Out of the approaches I reviewed, I found four in which it is feasible to generate the example SPARQL query without undue effort, which include:

  • String concatenation
  • Templating
  • Jena's query builder DSL
  • SPIN RDF using JSON-LD

My personal favourite that I use for generating SPARQL is templating with Mustache, which appears to mesh best with my brain and the tasks I do with SPARQL. Nonetheless, I am aware of the limitations of this approach and I am on a constant lookout for better solutions, possibly involving rendering SPARQL from data.

While I invested a fair amount of effort into this post, it is entirely possible I might have overlooked something or implemented any of the reviewed approaches in a sub-optimal way, so I would be glad to hear any suggestions on how to improve. In the meantime, while we search for the ideal solution for generating SPARQL, I think the membrane between code and SPARQL will remain only semipermeable.


Academia-driven development

In this post I present an opinionated (and mostly-wrong) account on programming in academia. It's based in part on my experience as a developer working in academia and in part on conversations I had with fellow developers working in the private sector.

In academia, you rarely program even if you are in computer science. Instead, you read papers, write papers, write deliverables for ongoing projects, or write project proposals. You aren't paid to program, you are paid to publish. You do only as much programming as is needed to have something to write a paper about. “Scientists use programming the way software engineers use public transport – just as a means to get to what they have to do,” observes Bozhidar Bozhanov. While programming purists may be dissatisfied with that, Jason Baldridge is content with this state of affairs and writes: “For academics, there is basically little to no incentive to produce high quality software, and that is how it should be.”

Albert Einstein allegedly said this: “If we knew what it was we were doing, it would not be called research, would it?” While the attribution of this quote is dubious at best, there's a grain of truth in what the quote says. It's natural in research that you often don't know what you work on. I think this is the reason why test-driven development (TDD) is not truly applicable in research. Programming in research is used to explore new ideas. TDD, on the contrary, requires upfront specification of what you are building. German has the verb ‘basteln’ that stands for DIY fiddling. The word was adopted into the Czech spoken language with a negative connotation of not knowing what you're doing, which I think captures nicely what often happens in academic programming.

The low quality of academic software hinders its maintenance and extensibility in long-term development. For one-off experiments these concerns aren't an issue, but most experiments needs to be reproducible. Academic software must allow to reproduce and verify the results that are reported in the publication associated with it. Anyone must be able to re-run the software. It must be open-source, allowing others to scrutinize its inner workings. Unfortunately, it's often the case that academic software isn't released or, when it's made available, it's nigh impossible to run it without asking its creators for assistance.

What's more is that usability of software is hardly ever a concern in academia, in spite of the fact that usable software may attract more citations, thereby increasing the academic prestige of its author. An often-mentioned example of this effect in practice is Word2vec, the paper of which boasts with 1305 citations according to Google Scholar. Indeed, it would be a felicitous turn if we reconsidered the usability of academic software as a valuable proxy that increases citation numbers.

A great benefit that comes with reproducible and usable software is extensibility. Ted Pedersen argues that there's “a very happy side-effect that comes from creating releasable code—you will be more efficient in producing new work of your own since you can easily reproduce and extend your own results.” Nonetheless, even though software may be both reproducible and usable, extending a code base without tests may be like building on quicksand. This is usually an opportunity for refactoring. For example, the feature to be extended can be first covered with tests that document its expected behaviour, as Nell Shamrell-Harrington suggests in surgical refactoring. The subsequent feature extension must not break these tests, unless the expected behaviour should change. I think adopting this approach can do a great good to the continuity of academic development.

Finally, there's also an economic argument to make for ‘poor-quality’ academic software. If software developed in the academia achieved production quality, it would constitute a competition to software produced in the private sector. Since academia is a part of the public sector, academic endeavours are financed mostly from the public funds. Hence such competition with commercial software can be considered unfair. Dennis Polhill argues that “unfair competition exists when a government or quasi-government entity takes advantage of its tax exemption and other privileges to supply private goods to the market in competition with private suppliers.” Following this line of thought, the public sector should not subsidize the development of software that is commercially viable and can be built by private companies. Instead of developing working solutions, academia can try and test new prototypes. If released openly, this proof-of-concept work can be then adopted in the private sector and grown into commercial products.

Eventually, when exploring my thoughts on academia-driven development, I realized that I'm torn between settling for the current status quo and pushing for emancipating software with publications. While I'm stuck figuring this out, there are laudable initiatives, such as Semantic Web Developers, which organizes regular conference workshops that showcase semantic web software and incite conversations about the status of software in academia. Let's see how these conversations pan out.


In science, form follows funding

Recently, brief Twitter exchanges I had with @csarven on the subject of #LinkedResearch made me want to articulate a longer-form opinion on scientific publishing that no longer fitted a tweet. Be wary though, although this opinion is longer, it's still oversimplifying a rather complex matter for the sake of conveying the few key points I have.

There's a pervasive belief that “if you can't measure it, you can't manage it”. Not only is this quote often misattributed to Peter Drucker, its author William Edwards Deming actually wrote that “it is wrong to suppose that if you can't measure it, you can't manage it — a costly myth” (The New Economics, 2000, p. 35). Contradicting the misquoted statement, Deming instead suggested that it's possible to manage without measuring. With that being said, he acknowledges that metrics remain an essential input to management.

Since funding is a key instrument of management, metrics influence funding decisions too. Viewed from this perspective, science is difficult to fund because its quality is hard to measure. The difficulty of measuring science is widely recognized, so that scientometrics was devised with the purpose of studying how to measure science. Since measuring science directly is difficult, scientometrics found ways to measure scientific publishing, such as citation indices. Though using publishing as a proxy to science comes with an implicit assumption that the quality of scientific publications correlates positively with the quality of science, many are willing to take on this assumption simply because of the lack of a better way for evaluating science. The key issue of this approach is that the emphasis on measurability constrains the preferred form of scientific publishing to make measuring it simpler. A large share of scientific publishing is centralized in the hands of few large publishers who establish a constrained environment that can be measured with less effort. The form of publishing imposes a systemic influence on science. As Marshall McLuhan wrote, the medium is the message. While in architecture form follows function, in science, form follows funding.

Measuring distributed publishing on the Web is a harder task, though not an insurmountable one. For instance, Google's PageRank algorithm provides a fair approximation of the influence the documents distributed on the Web have. Linked research, which proposes to use the linked data principles for scientific publishing, may enable to measure science without the cost incurred by centralization of publishing. In fact, I think its proverbial “killer application” may be a measurable index like the Science Citation Index. Indeed, SCI was a great success, and it “did not stem from its primary function as a search engine, but from its use as an instrument for measuring scientific productivity” (Eugene Garfield: The evolution of the Science Citation Index, 2007). A question that naturally follows is: how off am I in thinking this?