Ontology Verbalization using Semantic-Refinement

Tracking #: 1788-3001

This paper is currently under review
Vinu E. V
P Sreenivasa Kumar

Responsible editor: 
Philipp Cimiano

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In this paper, we propose an inference-based technique to generate redundancy-free natural language (NL) descriptions of Web Ontology Language (OWL) entities. The existing approaches for verbalizing OWL ontologies generate NL text segments which are close to their counterpart statements in the ontology. Some of these approaches also perform grouping and aggregation of these NL text segments to generate a more fluent and comprehensive form of the content. Restricting our attention to description of individuals and atomic concepts, we find that the approach currently used in the available tools is that of determining the set of all logical conditions that are satisfied by the given individual/concept name and translate these conditions verbatim into corresponding NL descriptions. Human-understandability of such descriptions is affected by the presence of repetitions and redundancies, as they have high fidelity to the OWL representation of the entities. In the literature, no major efforts have been taken to remove redundancies and repetitions at the logical level before generating the NL descriptions of entities and we find this to be the main reason for lack of readability of the generated text. In this paper, we propose a technique called semantic-refinement to generate meaningful and easily-understandable (what we call redundancy-free) text descriptions of individuals and concepts of a given OWL ontology. We identify the combinations of OWL/DL constructs that lead to repetitive/redundant descriptions and propose a series of refinement rules to rewrite the conditions that are satisfied by an individual/concept in a meaning-preserving manner. The reduced set of conditions are then employed for generating textual descriptions. Our experiments show that, semantic-refinement technique could significantly improve the readability of the descriptions of ontology entities, especially for domain experts. We have also tested the effectiveness and usefulness of the the generated descriptions in validating the ontologies and found that the proposed technique is indeed helpful in the context. Details of the empirical study and the results of statistical tests to support our claims are provided in the paper.
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