TOSS: An extension of TAX with ontologies and similarity queries

Edward Hung*, Yu Deng, V. S. Subrahmanian

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

26 Scopus citations


TAX is perhaps the best known extension of the relational algebra to handle queries to XML databases. One problem with TAX (as with many existing relational DBMSs) is that the semantics of terms in a TAX DB are not taken into account when answering queries. Thus, even though TAX answers queries with 100% precision, the recall of TAX is relatively low. Our TOSS system improves the recall of TAX via the concept of a similarity enhanced ontology (SEO). Intuitively, an ontology is a set of graphs describing relationships (such as isa, partof, etc.) between terms in a DB. An SEO also evaluates how similarities between terms (e.g. "J. Ullman", "Jeff Ullman", and "Jeffrey Ullman") affect ontologies. Finally, we show how the algebra proposed in TAX can be extended to take SEOs into account. The result is a system that provides a much higher answer quality than TAX does alone (quality is defined as the square root of the product of precision and recall). We experimentally evaluate the TOSS system on the DBLP and SIGMOD bibliographic databases and show that TOSS has acceptable performance.

Original languageEnglish (US)
Pages (from-to)719-730
Number of pages12
JournalProceedings of the ACM SIGMOD International Conference on Management of Data
StatePublished - 2004
Externally publishedYes
EventProceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2004 - Paris, France
Duration: Jun 13 2004Jun 18 2004


  • Ontologies
  • Semantic Integration of Heterogeneous Data
  • Similarity Enhancement
  • XML Databases

ASJC Scopus subject areas

  • Software
  • Information Systems


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