On Graph Features of Semantic Web Schemas

• Published in: IEEE transactions on knowledge and data engineering Vol. 20; no. 5; pp. 692 - 702
• Main Authors: Theoharis, Y., Tzitzikas, Y., Kotzinos, D., Christophides, V.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2008; IEEE Computer Society; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Approximation; Artificial intelligence; Benchmarking; Computer science; Computer science; control theory; systems; Computer systems and distributed systems. User interface; conceptual schemas morphology; Data processing. List processing. Character string processing; Database languages; Density measurement; Exact sciences and technology; Frequency measurement; Graphs; Hierarchies; Internet; Memory organisation. Data processing; Morphology; OWL; Power law; power-laws; Recursive; Repositories; Resource description framework; Semantic Web; Semantics; Software; Speech and sound recognition and synthesis. Linguistics; Studies; Topology; Semantic Web; power-laws; conceptual schemas morphology; Performance evaluation; Branching; Semantic web; power laws; Graph decomposition; Query language; Morphology; conceptual schema morphology; Conceptual analysis; Power law
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2007.190735

In this paper, we measure and analyze the graph features of semantic Web (SW) schemas with focus on power-law degree distributions. Our main finding is that the majority of SW schemas with a significant number of properties (respectively, classes) approximate a power law for total-degree (respectively, the number of subsumed classes) distribution. Moreover, our analysis revealed some emerging conceptual modeling practices of SW schema developers: (1) each schema has a few focal classes that have been analyzed in detail (that is, they have numerous properties and subclasses), which are further connected with focal classes defined in other schemas, (2) class subsumption hierarchies are mostly unbalanced (that is, some branches are deep and heavy, while others are shallow and light), (3) most properties have as domain/range classes that are located high at the class subsumption hierarchies, and (4) the number of recursive/multiple properties is significant. The knowledge of these features is essential for guiding synthetic SW schema generation, which is an important step toward benchmarking SW repositories and query language implementations.