An Approach to Extracting Topic-guided Views from the Sources of a Data Lake


In the last years, data lakes are emerging as an effective and an efficient support for information and knowledge extraction from a huge amount of highly heterogeneous and quickly changing data sources. Data lake management requires the definition of new techniques, very different from the ones adopted for data warehouses in the past. In this scenario, one of the most challenging issues to address consists in the extraction of topic-guided (i.e., thematic) views from the (very heterogeneous and often unstructured) sources of a data lake. In this paper, we propose a new network-based model to uniformly represent structured, semi-structured and unstructured sources of a data lake. Then, we present a new approach to, at least partially, “structuring” unstructured data. Finally, we define a technique to extract topic-guided views from the sources of a data lake, based on similarity and other semantic relationships among source metadata.

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  1. 1.

  2. 2.

  3. 3.

    Recall that, in database context, a view is the result of a query or a more complex extraction process that can be exploited by users for further computations.

  4. 4.

  5. 5.

    Here and in the following, to make the presentation smoother, we use the term “source” (resp., “keyword”) to denote both the source (resp., a keyword) and the corresponding node associated with it.

  6. 6.

    In this paper, we use the term “lemma” according to the meaning it has in BabelNet (Navigli and Ponzetto 2012). Here, given a term, its lemmas are other objects (terms, emoticons, etc.) that contribute to specify its meaning.

  7. 7.

    Note that Phases 2 and 4 could be merged into a unique one, avoiding to define arcs with label “lemmaOf”. Here, we maintain these arcs and both phases to keep the information about similarity between nodes for future uses.

  8. 8.

    Whenever this does not happen, the mapping can be automatically provided by the DBpedia Lookup Service (

  9. 9.

    Here, two nodes are assumed to be equal if the corresponding names coincide.

  10. 10.

    In Figs. 3 and 4, we do not show the arc labels for the sources C, W and E because all of them are “contains” and their presence would have complicated the layout unnecessarily.

  11. 11.

    Hereafter, we use the notation S.o to indicate the object o of the source S.

  12. 12.

    In this figure, for layout reasons, we do not show the arc labels because they are the same as the ones of the corresponding arcs of Figs. 34 and 5.

  13. 13.

    Prefixes dbo and dbr stand for and

  14. 14.

    Consider that, since we have 20 real sources in the data lakes adopted in our experimental campaign, the value of Hj can range in the real interval [0.05, 20].

  15. 15.

    As a matter of fact, a topic set with 8 keywords would encompass a great number of different concepts and, as such, it would not be generally able to capture a clear and specific desire of a user.


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Diamantini, C., Lo Giudice, P., Potena, D. et al. An Approach to Extracting Topic-guided Views from the Sources of a Data Lake. Inf Syst Front 23, 243–262 (2021).

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  • Data lakes
  • Unstructuted data sources
  • Metadata management
  • Thematic views
  • Semantic similarities
  • DBpedia