According to the Linked Data principles, a tripleset should be interlinked with others to take advantage of existing knowledge. However, interlinking is a laborious task. Thus, users interlink their triplesets mostly with data hubs, such as DBpedia and Freebase, ignoring the more speci c yet often even more promising triplesets. To alleviate this problem, this paper describes a tripleset interlinking recommendation tool based on link prediction techniques and evaluates the tool on a real-world tripleset repository.
A considerable number of triplesets, following the Linked Data principles, have already been published in a large number of areas, ranging from geographic to bibliographic data. This growth makes it di cult to choose which triplesets should be interlinked with a given tripleset. Thus, users interlink their triplesets mostly with data hubs, such as DBpedia and Freebase, ignoring the more speci c triplesets which often contain particularly useful data. Furthermore, the metadata provided in data repositories such as the DataHub are typically not su cient to help users choose the most suitable triplesets to interlink with.
To help alleviate this situation, we describe a tool for tripleset
interlinking recommendation, based on previous work by the authors [
Recommendation Procedure. A tripleset t is a set of RDF triples. A resource, identi ed by an RDF URI reference s, is de ned in t i s occurs as the subject of a triple in t.
LRWt;u(s) = 2CjCt0 j : t;u(s) + 2CjCu0j : u;t(s)
Let t and u be two triplesets. A link from t to u is a triple of the form (s; p; o), where s is an RDF URI reference identifying a resource de ned in t and o is an RDF URI reference identifying a resource de ned in u; we also say that (s; p; o), interlinks s and o. We say that t can be interlinked with u i it is possible to de ne links from t to u. A Linked Data network is a graph G = (S; C) such that S is a set of triplesets and C contains an edge (t; u), called a connection from t to u, i there is at least one link from t to u.
Our recommendation procedure analyses the Linked Data network in much the same way as a Social Network. The inputs of the procedure are: (i) a Linked Data network G = (S; C); (ii) a target tripleset t not in S (intuitively the user wishes to de ne links from t to the triplesets in S); and (iii) a target context Ct for t consisting of one or more triplesets u in S (intuitively the user knows that t can be interlinked with u). The output is an order list L of triplesets in S, called a ranking. The triplesets in the ranking are ordered using link prediction techniques discussed in what follows.
Link prediction techniques. The procedure uses link prediction theory to
estimate the likelihood of the existence of a link between triplesets. We focus
on local and quasi-local indices to measure the structural similarity between
triplesets [
Description of the TRT Tool in Action. Brie y, suppose that the user is working on a tripleset t and wants to discover one or more triplesets u such that t can be interlinked with u. He then uses the tool to obtain recommendations.
The tool rst builds the Linked Data network G = (S; C) de ned by the metadata stored in the DataHub repository.
Then, the user de nes the rest of the input data the tool requires. He may de ne a target context Ct for t, consisting of one or more triplesets in S, in two di erent ways: (i) by providing a VoID descriptor Vt for t from which the tool extracts Ct by analysing the void:linkset declarations occuring in Vt; or (ii) by manually selecting triplesets from the categories the tool displays. Finally, the user chooses a similarity index from those shown on Table 1.
From this input data, the tool outputs a ranked list of triplesets, thereby helping reduce the e ort required to nd related triplesets for the interlinking process.
The tool can be accessed at http://web.ccead.puc-rio.br:8080/Uncover/. 3
The tool was evaluated using the DataHub repository, which contains more than 6,000 triplesets, with approximately 15 thousand links that connect only 711 of the available triplesets. The links across triplesets were used to rank and recommend triplesets for interlinking. The recommendation process was assessed using the 10-fold cross validation approach, where we randomly divided the observed links into 10 subsets used as recommendation subgraphs. Finally, the overall performance was computed in terms of the average of the performances in the testing partitions.
To evaluate the prediction indices, we used three standard metrics: Area Under the receiver operating characteristic Curve (AUC), Mean Average Precision (MAP) and Recall. Table 2 summarizes the results for di erent target context sizes (shown in the rst column of the table). The entries corresponding to the highest results among the 12 indices are emphasized in boldface underlined. The reader may observe that the PA index achieved the highest AUC (ranging from 83.74% to 95.90% depending on the target context size). The PA index also obtained the best MAP (37.83%) for target contexts with very few triplesets, while the RA index turned out to be more precise (72.42%) for larger target contexts. Table 2 also shows the coverage results. The PA index obtained the highest recall (96.4%), regardless of the size of the target context.
In this paper, we proposed the use of link prediction techniques to address the tripleset recommendation problem in the Linked Data domain and presented a tool that implements the techniques. The tool computes local and quasi-local indices to predict links between triplesets. The results showed that the tool performs better, with respect to both AUC and recall, when the PA index is adopted. In terms of MAP, the PA index should be adopted for smaller context sizes, while the RA index should be adopted for larger context sizes. Acknowledgments. This work was partly supported by CNPq, under grants 160326/2012-5, 301497/2006-0, 475717/2011-2 and 57128/2009-9, by FAPERJ, under grants E-26/170028/2008 and E-26/103.070/2011.