Mapping Language Analysis of Comparative Characteristics

Abstract

RDF generation processes are becoming more interoperable, reusable, and maintainable due to the increased usage of mapping languages: languages used to describe how to generate an RDF graph from (semi-)structured data. This gives rise to new mapping languages, each with di erent characteristics. However, it is not clear which mapping language is t for a given task. Thus, a comparative framework is needed. In this paper, we investigate a set of mapping languages that inhibit complementary characteristics, and present an initial set of comparative characteristics based on requirements as put forward by the reference works of those mapping languages. Initial investigation found 9 broad characteristics, classi ed in 3 categories. To further formalize and complete the set of characteristics, further investigation is needed, requiring a joint e ort of the community.

Keywords

• Mapping Language
• RDF graph generation

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RDF graph generation started as an ad-hoc process: hard-coded applications generate speci c RDF graphs, typically from speci c data sources in speci c data formats. Shortly after, mapping languages were introduced, of which the RDB to RDF Mapping Language (R2RML) is the rst W3C recommended mapping language to generate RDF graphs from relational databases (RDBs) [ 1 ]. When a mapping language is used, the rules that specify the generation are detached from the processor that executes them [ 8 ]. This improves interoperability, reusability, and maintainability of the rules, the processor, and the entire RDF graph generation process [ 13 ].

New mapping languages { or extensions to existing mapping languages { are proposed to increase functionality or user-experience, and cater to di erent ? Copyright ©2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). use cases. For example, mapping languages were proposed extending W3C recommendations, such as R2RML and SPARQL. The RDF Mapping Language (RML) is proposed as an extension of R2RML to support heterogeneous data sources [ 7, 8 ], and SPARQL-Generate is proposed as an alternative mapping language, serialized using a modi ed SPARQL syntax [ 12 ].

This multitude of mapping languages allows to support more use cases. However, this multitude also defeats the mapping language's purpose: interoperability and reusability of current mapping rules and processors decrease. Currently, mapping rules are incompatible across processors of di erent mapping languages.

The di erences between mapping languages are currently not presented in a unifying framework. Finding a suitable mapping language thus involves a laborious process where end-users need to investigate the characteristics of di erent mapping languages and interpret the di erences. There is no clear interoperable description of (non-)functional characteristics of di erent mapping languages.

In this position paper, we investigate a set of mapping languages of which the reference works of those mapping languages claim complementary characteristics. We align and present an initial set of comparative characteristics as put forward by the reference works. These characteristics can then be used to compare di erent mapping languages against each other. This work is not meant to be complete, but to trigger discussion. Further and thorough investigation is required. After presenting our reference works in Section 2, we discuss the characteristics in Section 3 and conclude in Section 4. 2

Reference works

R2RML is the W3C recommended mapping language for describing RDF graph generation from a relational database (RDB) [ 1 ]. It presents a language speci cally designed as mapping language, serialized in RDF. Other recommendations that allow to interpret a datasource as an RDF graph exist, such as JSON-LD [ 16 ] and CSVW [ 17 ]. These recommendations target a single datasource type. However, the need for supporting di erent datasource types for di erent use cases in uenced the proposal of (i) other non-standardized mapping languages, (ii) extensions of existing mapping languages, and (iii) di erent notations.

We discuss RML(+FnO), an R2RML extension that supports heterogeneous datasources and data transformations [ 5, 7, 8 ]; xR2RML, an [R2]RML extension that supports collections and nested mappings [ 14, 15 ]; FunUL, an R2RML extension that supports data transformations [ 11 ]; SPARQL-Generate, an alternative mapping language based on SPARQL [ 12 ]; and YARRRML, an alternative notation to, a.o., RML [ 10 ]. Speci cally, we discuss the references of Table 1, and the requirements they put forward.

This list is not exhaustive, however, we choose to discuss these works as their reference work(s) list a set of requirements, and these sets of requirements are (partially) complementary with those of the other mapping languages. RML The RDF Mapping Language (RML) extends the R2RML recommendation to take into account heterogeneous data sources [ 7, 8 ]. Apart from allowing to specify how to generate the subject, predicate, object, and optionally graph resources, RML allows for specifying the logical source that describes the iteration over the data records (e.g., iterating over tabular data or JSON objects), and specifying the data source that speci es the actual data connection (e.g., reading a local XML le or accessing a remote NoSQL endpoint). Later, RML was combined with the Function Ontology (FnO) [ 2, 4 ] to include arbitrary data transformations in the generation descriptions (RML(+FnO)) [ 5 ]. xR2RML An [R2]RML extension to include mapping functionalities targeted at hierarchical (NoSQL) data structures [ 14, 15 ]. These advanced functionalities include (i) nested term maps (for addressing hierarchical relationships), (ii) collections and lists, and (iii) combining multiple query languages (for addressing, e.g., a JSON record saved in an RDB).

FunUL An R2RML extension to include data transformations in the form of JavaScript snippets, and support for CSV data sources [ 11 ]. The presented work poses a set of eleven requirements for mapping languages, applied to generating RDF graphs from CSV les.

SPARQL-Generate An alternative mapping language using a SPARQL-like syntax to describe the mapping [ 12 ]. It has extensible support for di erent data sources and data transformations. The presented work poses seven functional and non-functional requirements.

YARRRML A notation using YAML1 to provide a user-friendly way to describe mapping rules [ 10 ] which can be translated into RML (or other) mapping rules. 3

Characteristics

We discuss the following characteristics based on the aforementioned reference works, and their posed requirements. We further classify as non-functional, data source support, or functional characteristics (summarized in Table 2). For each characteristic, we state which reference work posed the original requirement. 1 https://yaml.org/

Non-functional characteristics

Non-functional characteristics are in relation to the user of the mapping language. This user is either the end-user (creating the mapping), or the developer (integrating the mapping language processor into his/her application). NF1: Easy to use by Semantic Web experts (from SPARQL-Generate/YARRRML) Whether the mapping language is \easy to use by Semantic Web experts" [ 12 ] to describe the generation process or not. This characteristic is addressed by SPARQL-Generate and YARRRML, which both provide a syntax that is either \familiar" (SPARQ-like) [ 12 ] or \human-readable" (YAML) [ 10 ] to write rules. The other mapping languages are described in RDF, without a speci c notation. NF2: Based on Semantic Web standards (from SPARQL-Generate) Whether the mapping language \[integrates] with a typical semantic web engineering workow" [ 12 ] or not, i.e., if it is related to existing standards. This characteristic is ful lled by all mapping languages, namely, they are related to R2RML, SPARQL, or YAML.

NF3: Fully covering the generation process (from FunUL) Whether the mapping language \allows the serialization of the [generation] process for further reuse" [ 11 ] or not: whether the generation description is fully covered by the mapping language, or certain parts are hard-coded. xR2RML requires retrieval of the data records as part of the hard-coded process. The other mapping languages allow to specify the connection to the physical data source. 3.2

Data source support characteristics

DS1: Supporting heterogeneous sources (from RML/xR2RML/SPARQL-Generate) Whether the mapping language is focused on a single type of data source, or can support multiple. FunUL targets tabular data sources. The other mapping languages have support for { and are extensible to { other data sources.

Although this is not tied to the mapping language itself, we further detail which data sources are currently supported by the reference mapping language processors. 3.3

Functional characteristics

F1: Supporting general mapping functionalities (from FunUL) Whether general mapping functionalities are provided or not, namely, specifying \M:N relationships", \literal to IRI", \vocabulary reuse", \data types", \named graphs", and \blank nodes" [ 11 ]. These functionalities include (i) generating subject, predicate, object, and (optionally) graph resources, (ii) joining data, and (iii) specifying the used ontology and datatypes. This is typically supported by most mapping languages.

F2: Extensible (from RML/FunUL/SPARQL-Generate) Whether additional functionalities can be added or not, to \allow data to be manipulated and transformed" [ 11 ]. RML supports this and proves it by combining data descriptions and FnO, FunUL allows including JavaScript snippets, and SPARQL-Generate relies on SPARQL 1.1's extension functions.

F3: Supporting nested hierarchies (from xR2RML) Whether nested data records can be joined or not, to \map data elements from rows as well as structured values (nested collections [...])" [ 15 ]. This is di erent from being able to query a hierarchical dataset: you can query a hierarchical dataset to return (and join) only non-nested data records. Instead, this characteristic relates to whether the mapping language itself can handle hierarchical data structures or not. Except for xR2RML, all R2RML extensions use the tabular datamodel to join data. xR2RML also supports combining query languages when a nested data record has a di erent serialization than the parent data record. SPARQL-Generate is assumed to support this, as it extends SPARQL, a graph-based query language. F4: Supporting collections and lists (from xR2RML) Whether the mapping language allows \to generate hierarchical values in the form of RDF collections or containers" [ 15 ] or not. xR2RML has built-in support, SPARQL-Generate allows to generate all triples required to generate compliant RDF lists. 3.4

Discussion

The di erence in support of certain functional characteristics can help nding a mapping language that is suitable for a certain use case. However, other characteristics also in uence the choice of a mapping language. For example, depending on the use case, a di erent mapping language notation might be preferred. On the one hand, a description in RDF can be less ambiguous, allowing more accurate analysis of the generation description. On the other hand, a mapping description serialized in RDF is arguably less user-friendly to edit and maintain by users.

Further investigation is needed into the di erence between serialization and notation of mapping languages, and the underlying model and semantics they employ. For example, comparing SPARQL-Generate with YARRRML: the latter is designed to be human-friendly, and used to employ the semantics (and functionalities) as proposed by RML(+FnO). The former is based on the SPARQL language, but exhibits similar functionalities compared to RML(+FnO). RML, xR2RML and FunUL extend the model of R2RML, which is based on a tabular data model. SPARQL-Generate, following SPARQL, is based on a graph model.

Finally, it needs to be investigated on how to divide which characteristics apply to the mapping language and which apply to the processors of that mapping language. For example, a mapping language can be extended to a speci c datasource, however, support of that datasource might not be easily achieved in the processor. Another example is the automatic generation of metadata of the generation process. This is easier when the mapping language is described in a machine-understandable format, i.e., RDF [ 3, 6 ].

Conclusion

In this position paper, we provide an initial investigation towards a comparative framework for mapping languages. A more systematic review is further required. It is apparent that this e ort cannot continue without support from the larger community. This work needs to be extended to consider a more complete range of existing mapping languages. Choices made in this work { speci cally, the classi cation of characteristics into non-functional, data source support, functional, and the level of detail of di erent characteristics { need to be veri ed.

The resulting framework will allow for a clear division of which mapping languages support which characteristics, and allows for end-users to more easily nd the right language for their use-case. More, development e ort can be consolidated on missing features and lling the gaps, instead of spending time on re-developing existing functionalities.

Further formalization and comparison between mapping languages can be tested using a uniform set of test cases. Recent works are looking into test cases sets for speci c mapping languages [ 9 ]. This work can be extended to provide a set of test cases across mapping languages. We expect this work to start a larger discussion, and to provide a basis for a more complete and accurate comparative framework. The recently started Knowledge Graph Construction Community Group can be a crucial driver for further investigation.

Acknowledgements The described research activities were funded by Ghent University, imec, Flanders Innovation & Entrepreneurship (VLAIO), and the European Union. Ruben Verborgh is a postdoctoral fellow of the Research Foundation { Flanders (FWO).

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