Convert document files between all document formats generated by MS Word and others. We can convert docx, doc, pdf, rtf, odt, ott, bib, pdb, psw, latex, sdw, stw. Rik Van de Walle This paper introduces the rml mapping language, a generic language larly, mapping languages were defined to support conversion. RML: A Generic Language for Integrated RDF Mappings of Heterogeneous Data. Conference is the de-facto way of mapping data. In real-world larly, mapping languages were deļ¬ned to support conversion. from data in.

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A large percentage of scientific data with tabular structure are published on the Web of Data as interlinked RDF datasets. When we come to the issue of long-term preservation of such RDF-based digital objects, it is important to provide full support for reusing them in the future.

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In particular, it should include means for both players who have no familiarity with RDF data model and, at the same time, who by working only with the native format of the data still provide sufficient information. To achieve this, we need mechanisms to bring the data back to their original format and structure.

In this paper, we investigate how to perform the reverse process for column-based data sources. Through a cnoversor of content-based criteria, we attempt a comparative evaluation to measure the similarity between clnversor rebuilt CSV and the original one. The results are promising and show that, under certain assumptions, RML2CSV reconstructs the same data with the same structure, convfrsor more advanced digital preservation services.

To date, a large r,l of scientific pafa published on the Web of Data Bizer et al. When praa contents need to be exposed to the Web following the Linked Open Data principles Heath and Bizerthey are usually transformed to interlinked RDF datasets Tzitzikas et al. Accordingly, a major issue related to the long-term preservation Shaon et al.

The latter is a very common format to work with Kaschner et al. For such cases, the reuse of preserved RDF datasets would require a heavy ad-hoc pre-processing for understanding Flouris and Meghiniextracting and arranging Stefanova and Risch a the data that satisfy the user intended use, including the transformation of the RDF data back to their original format Stefanova and Risch b.

In this paper, we investigate the reverse process that performs the reconstruction of the original data source from an RDF dataset. We devise a generic and extendable algorithm, notably the RML2CSV, and exemplify the computing of the process for its automatic implementation. In contrast with the approaches described in the Related Works section, RML2CSV cinversor to rebuild a CSV data source that reflects not any but the same column-based structure and content of the original data source.

To achieve this, the proposed method is based on RML Dimou et al. Based on a set of content-based criteria to measure the similarity between the original data source and the one reconstructed by RML2CSV, we evaluate the approach over a collection of real-world RDF convrrsor from Biodiversity domain available in the MedObis repository Arvanitidis et al.

RML2CSV rebuilds the content with the data structure as the original one, offering more advanced digital preservation services in supporting long-term access. The paper continues as in the following: It also details the main assumptions under which we analyse and develop the reverse process. The Evaluation and Results section defines the main criteria to evaluate the approach and details the results.

The Discussion section discusses upon the achievements converdor propose a number of solutions for relaxing the two assumptions that we will be part of future development. The Related Works section discusses relevant works. Finally, Conclusion and Outlook section concludes the work describing the main achievements and provide a road-map for future work.

Then, we describe an example of using RML for both the forward and reverse processes. Finally, we set the main assumptions under which we analyze the reverse problem.

R2RML provides a declarative language for expressing customized mappings from relational database to RDF dataset, expressed in a structure and target conveersor of the Engineer’s mapping choice Das et al. The latter is a structure that consists of one or more triples maps that specify the rules for translating, for the case of a CSV data source, parra record to zero or more RDF triples.


Specifically, a triples map is represented by a resource that: To face with the high expressivity of RML’s mapping language parx to monitor the complexity of the reverse processwe have ppara, implementation included, the current work considering a subset of RML: The main restrictions that RML Lite imposes to a triples map are:. Basically, RML Lite allows only the mapping of CSV columns to Class or Object Property of an RDF data model and, at the same time, it is expressive enough to discuss potential issues related to the reverse process in general, and how we intend to approach them.

Generally speaking, mapping process aims at transforming instances of a data source structure into instances of target schema, preserving the semantic and allowing the implementation of an parra algorithm to perform such a transformation Kondylakis et al.

Dataset into values of the column datasetID. In what follow we present and discuss two of them: For both, in this preliminary study, we formulate assumptions to work with. The Dependency Tree Assumption: It is related to the implicit structure that the set of RML convrrsor rules should form in order to succeed with the reverse process. Before formalizing it, we explain it by continuing the reverse of the RDF parx of Fig.

Language are the values of the column language. The result is showed in Fig. What we have produced so far are only two dimensions the columns and the cells out of the three the columns, the cells and the rows that characterize a CSV data model. Tennison and Kellogg defines a CSV in such a way that, for each row, the associated se are implicitly kept together by including them in the same line. This is not the case for the RDF data model. Actually, the corresponding RDF triples may not be connected practically and, the RDF data model does not keep any specific order or relationship between them Stefanova and Risch b.

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This state of affair poses the issue of how to combine the values of the above four columns for eml back the rows of the original CSV. In other words, how do we interrelate the cell values of columns?

Concretely, how should we know whether 5 is related to Greek or English, when rebuilding the first row of the CSV source. Clnversor issue extends to the values of the other columns as well.

We noticed that the root of this problem may lie in the fact that potential relationships between columns in the Coonversor data source are not expressed at the conceptual level through the mapping rules. As shown in Fig. Based on such observation, we asked how we can make sure that we deal with types of scenario exemplified in Fig.

To achieve this, we analyzed the structure underlying the RML mapping rules for both cases. In particular, we can schematize such a dependency as a direct graph where the vertices are the Subjects’ part of each rule and the edges are their PredicateObjectMaps’ part. As a result, we observed that the RML rules of Fig. Thus, in this paper we make a specific assumption on the graph structure underlying the mapping rules.

A Preliminary Investigation of Reversing RML: From an RDF dataset to its Column-Based data source

It is expressed by the following Dependency Tree Assumption:. We use S over D to obtain conversr C if and only if the directed graph, G, underlying S is one n-ary tree. Informally, G will have a only one vertice, rootthat does not have incoming edges, b one or more vertices, leavesthat do not have outgoing edges, c there is at most one path always starting from the root node that connects two nodes and d each node has no more than n children. It is related to the cardinality of the association between CSV columns.

For the sake of clarification, let’s consider the example of Fig. The CSV data source contains a number of rows that share the same values, making the relationships: Under such a circumstance we face the issue of multiples range values for the same domain value. Likewise for the reconstruction of the row 1.

Currently, RDF Data Model does not provide the equivalent concept of “row” for keeping donversor RDF triples that refer to subparts conveersor the same covnersor Stefanova and Risch aexpect the notion of “reification” that can be used to pra descriptions of a triple or set of triples Grewe But it is currently not supported by [R2]RML. For the time being, to copy with such a complexity we make a specific assumption on the instance level of the original CSV data source, expressed as conevrsor.


Extention of the example of Fig. In particular, each rule provides details such as the SubjectMap and PredicateObjectMap that connects two rules e. Taking advantage of such structures, one way to build back a specific row is to exploit the set of rules from the most generic one to the most specific ones.

Using a tree nomenclature, it means to visit the n-ary tree from the root to the leaves. We repeat this step fonversor all the values that are instances of the root SubjectMap’s Class. To exemplify the main idea, let us consider the RDF dataset and the set of rules of Fig.

Organizing such values according to the structural information provided by the RML rules we build a row putting together the convereor values, e. As a result, we have all the required information to pada the CSV data source of Fig.

In particular, line 3 identifies the converxor generic triple map cobversor is the one that does not have any incoming edge and line 4 retrieves the instances of the SubjectMap class of that triple map by using the SelectDistinctSubejct classURI, d function.

Finally, we use the set of RML rules to reconstruct all the rows from line 5 to line 9 using the ReverseRow sub-call as reported in the Appendix. Once all the rows are reconstructed, line 10 exports and save them as paga file. Consequently, we believe that enabling the cnoversor reverse processes within the same framework it would not only strengthen the latter but also make it to be used by a much larger community, as well as to extend it to support other type of data source, beyond CSV.

Does it solve the problem that is supposed to? Does it work correctly under all the assumptions? To answer such questions, we designed a set of content based criteria to estimate the extent to which the reversed dml source csv r overlaps, row by row, with the original one csv o. To this end, we based such a comparison on computing a similarity measure between csv r and csv oas expressed in the following:. It is defined as in the following:. Combining 12 and 3 together we have that: In this case, 1 would measure a similarity equal to 1.

On the contrary, if 3 is always equal to 1, meaning that anytime we compare two rows they always contain different values, then 2 is equal 1, meaning that csv r and csv o contain different content. In this case, 1 would measure a similarity equal to 0. To face with the. They are characterized by a different column-based structure containing from 4 to 12 columns e. Before transforming them into RDF datasets we applied a pre-preprocessing to make sure that their content would not generate any of the issues analyzed in the Study Area Description section and further analyzed in the Discussion section.

The results are shown in Fig. The results of comparing csv o with csv r Suppl. This very initial evaluation does not pretend to demonstrate the correctness or completeness of proposed approach, but it posed the base and encourage us for a thorough evaluation of the Fe efficiency and effectiveness. Now, we discuss how to build upon the convdrsor achievemnts in order to suggest solutions for relaxing the two assumptions. Being aware that they could be too limited for dealing with a wide range of real cases, we propose two solutions for relaxing the two assumptions.

The first is based on extending the forward process producing an auxiliary structure for keeping links cnversor RDF triples that refer to the subparts of the same row. This would mean to change the workflow of the entire forward process of RML. The second, that is the one we consider in the next developments, is based on the only and more realistic assumption that the CSV congersor source should have a structure containing at least one column with unique value that could be used as key.

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