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«Abstract. Reifying literals clearly increases expressivity. But reified literals appear to waste memory, slow queries, and complicate graph-based ...»

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We established that common reified names have comparable in-memory cost to nonreified names. We similarly established that query speed for exact matching of reified names is equal or better than non reified names. And the additional speed cost of inexact matching is negligible in systems where inexact matching speed dominates that of a join. We argue that the overall structure of reified names and their metadata is simpler. We showed that reified names allow a sort of tightly linked expressivity that un-reified names do not.

We similarly analyzed dates, heights, and weights and found them to be slower by one join in interval queries. We found date expressivity to be significant and height and weight expressivity similar yet less likely to be justified by our data sets.

We distilled the following rules from the above analysis to help determine when

the literal reification design pattern should be used:

1. Rare reified literals are individually costly, but the net cost is only a concern if there are very many rare types.

2. Range queries such as with reified scalars and dates are slower by an equijoin.

3. Inexact match queries over reified literals are slower by an equijoin. That equijoin is inconsequential on systems where inexact match dominates the query time.

4. Otherwise the speed and memory cost is comparable.

As we value expressivity, we found most of our literals to be reasonably strong candidates for reification. Our desire for expressivity also makes us less concerned as to how well amortized our shared structure is. We found all of our scalars to be weaker/marginal candidates because we have no present or near future need for scalar-related expressivity. Their inclusion would be based more on a desire to apply all design patters consistently.

In all cases, the value of the expressivity gain must be subjectively weighed against the possible cost in memory and speed. We have used literal reification for over fifteen years in the IC and in two different data integration projects at scale.

We expect that as we continue to observe the results of our choices to reify and not to reify literals, we will more finely characterize how to make such choices in the future. We expect to have opportunity to garner shared structure amortization statistics on our various reified literals.

5 TimePoint is encoded as an interval as per common convention. TimePoint duration varies with the number of significant digits in the input.

7 Conclusions Commonly referenced reified literals come at little or no significant cost in memory, speed, or complexity. Queries over such literals are never slower than the cost of one join with respect to unreified literals and are usually comparable. Where literal-related expressivity is specifically needed or expected, reified literals should be considered.


1. Cycorp Inc.: OpenCyc. http://opencyc.org

2. Hightfleet (formerly OntologyWorks): IODE, http://www.highfleet.com

3. Pease, A., Niles, I., and Li, J.: The suggested upper merged ontology: A large ontology for the semantic Web and its applications. In Proceedings of the AAAI-2002 Workshop on Ontologies and the Semantic Web, Edmonton, Alta., Canada (2002)

4. Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A., and Schneider, L.: DOLCE:

A Descriptive Ontology for Linguistic and Cognitive Engineering. WonderWeb Project, Deliverable D17 v2.1 (2003)

5. Hobbs, J., Pan, F.: Time Ontology in OWL. Working draft, http://www.w3.org/TR/owltime (2006)

6. Charlet, J., Vandenbussche, P.: Concept Terms. Ontology Design Patterns.


7. Gangemi, A.: Ontology Design Patters for Semantic Web Content. ISWC 2005. LNCS, vol. 1729, pp. 262-276 (2005)

8. Zipf, G., Selected Studies of the Principle of Relative Frequency in Language. Harvard University Press, Cambridge, MA (1932)

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