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Aggregation in ontologies: Practical implementations in OWL

Profile image of Csaba VeresCsaba Veres

2005, Web Engineering

https://doi.org/10.1007/11531371_39
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11 pages

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Abstract

Data modeling for Web Applications needs to be guided not only by the specific requirements of a particular application, but also by the goal of maximizing interoperability between systems. This necessitates the adoption of widely accepted design methods and a set of rich, theoretically motivated principles for organizing data in ontologies. This paper presents one set of such principles. It is based on the observation that current ontologies emphasize the abstraction mechanism of generalization but ignore the various forms of aggregation. We explore possible techniques for modeling aggregation with OWL, investigate the semantics of aggregation, and consider the merits of aggregation over generalization for modeling knowledge in particular situations.

Key takeaways
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AI

  1. Aggregation offers a compelling alternative to generalization in ontology modeling.
  2. The paper proposes three approaches to implement aggregation in OWL: SOS, UOS, and IS.
  3. UOS is the most useful approach, balancing compatibility with OWL-DL and semantic representation.
  4. Unique properties can be inherited through appropriately defined ontology hierarchies.
  5. Semantic modeling should embrace both aggregation and generalization for richer data representation.
Figures (2)
Unfortunately this solution as implemented with SOS introduces a severe problem. In the definition of VEGETABLE we stipulate that its only instances are the classes CARROT and TOMATO (“asserted conditions” in figure 2). But this will prevent the creation of an ordinary instance VEGETABLE: carrot1, unless it was inferred that carrot! was the same as either one of the defined members CARROT or TOMATO.
Unfortunately this solution as implemented with SOS introduces a severe problem. In the definition of VEGETABLE we stipulate that its only instances are the classes CARROT and TOMATO (“asserted conditions” in figure 2). But this will prevent the creation of an ordinary instance VEGETABLE: carrot1, unless it was inferred that carrot! was the same as either one of the defined members CARROT or TOMATO.
allowing us to take advantage of available description logic reasoners. Classi- fying the taxonomy with Racer then reveals the result shown in figure 3. The inferred hierarchy is identical to an ontology that might have been built with more “traditional” approaches in which the subclass relationships were asserted. Retaining the originally asserted hierarchy as the foundational ontology main- tains the benefit of explicit semantics while computing subsumption relations is beneficial for downward compatibility with ontologies in which the subclass relations are asserted. TO summarize thus far, it seems that SOS will not work, but UOS will work with one possible exception. But an important novel feature of the solutions is that the semantics of the categories is explicitly represented because the nature of the aggregation can be “read off” the ontology. In this example the reason for the category can be defined in that it represents a collection of entities that serve the same purpose or function (to provide a certain kind of nutrition) and have the same origin (growing in the ground). The specific function and origin are therefore facts that can be represented in the ontology. Perhaps the usefulness of this is less obvious for VEGETABLE than a concept like WEAPON. Here, division into subclasses would appear odd if the subclasses of WEAPON included GUN, KNIFE, ICE-PICK, FEATHER PILLOW, and DECK CHAIR. Collecting them in an aggregation whose intent was defined would clear up the mystery.
allowing us to take advantage of available description logic reasoners. Classi- fying the taxonomy with Racer then reveals the result shown in figure 3. The inferred hierarchy is identical to an ontology that might have been built with more “traditional” approaches in which the subclass relationships were asserted. Retaining the originally asserted hierarchy as the foundational ontology main- tains the benefit of explicit semantics while computing subsumption relations is beneficial for downward compatibility with ontologies in which the subclass relations are asserted. TO summarize thus far, it seems that SOS will not work, but UOS will work with one possible exception. But an important novel feature of the solutions is that the semantics of the categories is explicitly represented because the nature of the aggregation can be “read off” the ontology. In this example the reason for the category can be defined in that it represents a collection of entities that serve the same purpose or function (to provide a certain kind of nutrition) and have the same origin (growing in the ground). The specific function and origin are therefore facts that can be represented in the ontology. Perhaps the usefulness of this is less obvious for VEGETABLE than a concept like WEAPON. Here, division into subclasses would appear odd if the subclasses of WEAPON included GUN, KNIFE, ICE-PICK, FEATHER PILLOW, and DECK CHAIR. Collecting them in an aggregation whose intent was defined would clear up the mystery.

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