Interactive Knowledge Discovery over Web of Data

Mehwish Alam 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Recently, the ``Web of Documents'' has become the ``Web of Data'', i.e., the documents are annotated in the form of RDF making this human processable data directly processable by machines. This data can further be explored by the user using SPARQL queries. As web clustering engines provide classification of the results obtained by querying web of documents, a framework for providing classification over SPARQL query answers is also needed to make sense of what is contained in the data. Exploratory Data Mining focuses on providing an insight into the data. It also allows filtering of non-interesting parts of data by directly involving the domain expert in the process. This thesis contributes in aiding the user in exploring Linked Data with the help of exploratory data mining. We study three research directions, i.e., 1) Creating views over RDF graphs and allow user interaction over these views, 2) assessing the quality and completing RDF data and finally 3) simultaneous navigation/exploration over heterogeneous and multiple resources present on Linked Data. Firstly, we introduce a \emph{solution modifier} i.e., {\tt View By} to create views over RDF graphs by classifying SPARQL query answers with the help of Formal Concept Analysis. In order to navigate the obtained concept lattice and extract knowledge units, we develop a new tool called RV-Explorer (Rdf View eXplorer) which implements several navigational modes. However, this navigation/exploration reveal several incompletions in the data sets. In order to complete the data, we use association rule mining for completing RDF data. Furthermore, for providing navigation and exploration directly over RDF graphs along with background knowledge, RDF triples are clustered w.r.t. background knowledge and these clusters can then be navigated and interactively explored. Finally, it can be concluded that instead of providing direct exploration we use FCA as an aid for clustering RDF data and allow user to explore these clusters of data and enable the user to reduce his exploration space by interaction.
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Mehwish Alam. Interactive Knowledge Discovery over Web of Data. Information Retrieval [cs.IR]. Loria & Inria Grand Est, 2015. English. ⟨NNT : 2015LORR0158⟩. ⟨tel-01754477v3⟩

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