RDF Digest: Ontology Exploration Using Summaries

Abstract. 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 and visualizes 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. A novel feature of our platform is that it allows summary exploration through extensible summaries. The aim of this demonstration is to dive in the exploration of the sources using summaries and to enhance the understanding of the various algorithms used. Introduction Given the explosive growth in both data size and schema complexity, data sources are becoming increasingly difficult to understand and use. Ontologies often have extremely complex schemas which are difficult to comprehend, limiting the exploration and the exploitation potential of the information they contain. Besides schema, the large amount of data in those sources increase the effort required for exploring them. Over the latest years, various techniques have been provided on constructing overviews on ontologies [1-4], maintaining however the more important ontology elements. These overviews are provided by means of an ontology summary. Ontology summarization [4] is defined as the process of distilling knowledge from an ontology in order to produce an abridged version. While summaries are useful, creating a "good" summary is a non-trivial task. A summary should be concise, yet it needs to convey enough information in order to enable a decent understanding of the original schema. Moreover, the summarization should be coherent and should provide an extensive coverage of the entire ontology. So far, although a reasonable number of research works tried to address the problem of summarization from different angles, a solution that simultaneously exploits the semantics of the schemas and the data instances is still missing. In this demonstration, we focus on RDF/S KBs and demonstrate for the first time the implementation of the algorithms introduced i

Evaluations of User-Driven Ontology Summarization

Ontology Summarization has been found useful to facilitate ontology engineering tasks in a number of different ways. Recently, it has been recognised as a means to facilitate ontology understanding and then support tasks like ontology reuse in ontology construction. Among the works in literature, not only distinctive methods are used to summarize ontology, also different measures are deployed to evaluate the summarization results. Without a set of common evaluation measures in place, it is not possible to compare the performance and therefore judge the effectiveness of those summarization methods. In this paper, we investigate the applicability of the evaluation measures from ontology evaluation and summary evaluation domain for ontology summary evaluation. Based on those measures, we evaluate the performances of the existing user-driven ontology summarization approaches

Ontology summarization: an analysis and an evaluation

Ontology summarization has been recognized as a very useful technique to facilitate ontology understanding and then support ontology reuse as a new or supplementing technique. A number of efforts have emerged lately that apply different criteria, addressing different features of ontology, to extract ontology summaries. However, those efforts are ad-hoc in that there lacks consensus on a number of issues fundamental to the development of the field, such as a definition for ontology summarization, use case scenarios etc. Also, there lack sufficient evaluations and analysis, e.g. comparison among them and with other similar techniques, to provide meaning guidelines for users of this technique. With the aim to provide solutions to those fundamental issues, in this work, we present an analysis of this technique and its approaches. With the help of an objective evaluation method, we investigate what features of ontology are important in ontology summarization

Evaluating Knowledge Anchors in Data Graphs against Basic Level Objects

The growing number of available data graphs in the form of RDF Linked Da-ta enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they in-teract with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by us-ers who are not domain experts. We propose a new navigation support ap-proach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cog-nitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two ap-plication domains - semantic browsing (Music) and semantic search (Ca-reers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts