RDF Digest: Efficient Summarization of RDF/S KBs

Abstract

The exponential growth of the web and the extended use of semantic web technologies has brought to the fore the need for quick understanding, flexible exploration and selection of complex web documents and schemas. To this direction, ontology summarization aspires to produce an abridged version of the original ontology that highlights its most representative concepts. In this paper, we present RDF Digest, a novel platform that automatically produces summaries of RDF/S Knowledge Bases (KBs). A summary is a valid RDFS document/graph that includes the most representative concepts of the schema adapted to the corresponding instances. To construct this graph, our algorithm exploits the semantics and the structure of the schema and the distribution of the corresponding data/instances. The performed preliminary evaluation demonstrates the benefits of our approach and the considerable advantages gained.

Ontology understanding without tears: The summarization approach

“…An initial version of our work has already been presented [11] and demonstrated [12]. This paper extends our previous work in several ways.…”

“…However as identified during our evaluation, blank nodes are apparent in many ontologies and KBs and we cannot keep ignoring them. In addition, in this paper we present a new algorithm for selecting the edges to be included in the constructed summary, moving out from local maximization [11] to global maximization of the importance of the selected edges. The implementation details and the complexities are presented, whereas the updated system provides more meta-data to enhance ontology understanding.…”

Graph-Based Methods for Ontology Summarization: A Survey

“…Relative Cardinality (RC) Whereas in the above approaches weights are empirically configured, we highlight relative cardinality [18,19], which is a way of automatically weighting edges for calculating weighted degree. In a class graph, the cardinality of an edge which represents a property connecting two classes is the number of the corresponding instances of the classes connected with that specific type of property.…”

Identifying Knowledge Anchors in a Data Graph

“…The technical approaches that are most relevant to the research presented in this paper refer to the adoption of basic level objects in ontology summarization [11,24,27] and in Formal Concept Analysis (FCA) [14, 25, and 26]. Ontology summarization has been seen as an important technology to help ontology engineers quickly make sense of an ontology, in order to understand, reuse and build new ontologies [28].…”

“…Measures for ranking and reranking using centrality, distance, similarity and coherence have been used to generate good explanations. The notion of relevance has been used in [27] to produce graph summaries. The closest work to the context in this paper is the summarization approach presented in [11], which highlighted the value of cognitive science (natural categories) for identifying key concepts in an ontology to aid ontology engineers to better understand the ontology and quickly judge it suitability.…”