In this demo paper we show an approach to build Semantic Web ontologies from sample linked data with a tool named Contento. Contento is a data driven ontology construction kit, based on Formal Concept Analysis (FCA). We show the exploration and analysis functionalities of Contento, as well as the method to generate, annotate and prune concept hierarchies. Moreover, we describe a procedure to go from sample data - extracted from SPARQL endpoints - to a new OWL ontology.
In this demo paper we show an approach to build Semantic Web ontologies from datasets in the Linked Data with a tool named Contento3. Contento is a data driven ontology construction kit, based on Formal Concept Analysis (FCA). With Contento, users can build the input data (objects and attributes) from scratch or import them from existing datasets, for example querying Linked Data endpoints. We show how to go from sample data to a complete OWL ontology in four steps: (1) extract data from one (or more) SPARQL endpoints; (2) generate a FCA lattice; (3) annotate and prune the conceptual lattice; (4) generate the OWL ontology.
Contento4 has been developed to create, populate and curate FCA formal contexts and associated lattices, also interpreted as taxonomies of concepts.
Formal contexts can be created and populated from scratch. With this interface, the binary matrix can be populated or supervised to constitute a proper input for a FCA algorithm. In many cases, however, a ready made binary matrix can be imported from pre-existing data, for example as output of a query to a Linked Data SPARQL endpoint like http://data.open.ac.uk/sparql: SELECT distinct ?object ?attribute ("1" as ?holds)
FROM <http://data.open.ac.uk/context/xcri>
WHERE { ?object a <http://data.open.ac.uk/ontology/Qualification> ; <http://purl.org/dc/elements/1.1/subject> ?attribute } The above query will report about the topics of quali cations under presentation at The Open University. The output of this query, requested as CSV le, can be used to feed Contento using the context import procedure. Figure 1 shows the facilities o ered by the context browser. In this case the formal context is created directly from that, ready to be used to generate the concept lattice with the procedure provided.
Bottom-Up Ontology Construction with Contento
Contento implements the Chein algorithm [
Moreover, it can be visualized and explored as a concept lattice (Figure 2). The lattice can be navigated by clicking the nodes. Focusing on a single node, the respective upper and lower branches are highlighted, to facilitate the navigation to the user. Similarly, objects and attributes from the focused node can be selected, thus highlighting all nodes in the hierarchy sharing all of the selected features (in orange in Figure 2).
Contento supports the user on the curation of the concept hierarchy, supporting annotation of concepts with label and comment, and the pruning of unwanted concepts. This last operation implies an adjustment of the hierarchy, by building links between lower and upper bounds of the deleted node (only if no other path to the counterpart exists). As a result, relevant concepts can be quali ed, and concepts that are not relevant for the task at end can be removed.
The data of the FCA lattice can then be translated into OWL using prede ned or custom pro les. The user can decide how to represent the taxonomy in RDF, what terms to use to link concepts, objects and attributes, and whether items need to be represented as URIs or literals. For example, Contento o ers a default pro le, using example terms, or a SKOS pro le. Ultimately, these export con gurations can be shared and reused.
Bottom-up approaches for ontology design have been commonly applied in
knowledge engineering [