Building ontologies is difficult for three reasons. First, articulating knowledge in sufficient detail that it can be expressed in computationally effective formalisms is hard. Second, the scope of shared background knowledge underlying interactions of two agents can be enormous. For example, two doctors collaborating to reach a diagnosis might combine commonsense conclusions based on a patient’s lifestyle with their specialized knowledge. Third, there are unsolved problems in using large bodies of knowledge effectively, including selecting relevant subsets of knowledge, handling incomplete information, and resolving inconsistencies.
3.7.1 Relevance to the NII
The creation of repositories of ontologies that can be used by intelligent software robots is crucial to the NII because ontologies provide the shared conceptualizations that are required for communication and collaboration. For example, an engineer’s software agent needs to understand the design rationale and function of a subsystem to detect the possible impact of other design decisions on the subsystem. Data-mining intelligent agents need to understand the contents of databases to integrate information from disparate sources. Robust representations of commonsense knowledge will be essential for agents that communicate with people; the inability to draw on the background knowledge we share with other people is one reason computers today are so difficult to use.
3.7.2 State of the Art
Despite its fundamental importance, the accumulation of ontologies has only just begun. Techniques for organizing ontologies, combining smaller ontologies to form larger systems, and using this knowledge effectively are all in their infancy. There are few collections of ontologies in existence; almost all are still under development, and currently none of them are widely used.
Efforts are under way to create ontologies for a variety of central commonsense phenomena, including time, space, motion, process, and quantity. Research in qualitative reasoning has led to the creation of techniques for organizing large bodies of knowledge for engineering domains and automatic model-formulation algorithms that can select what subset of this knowledge is relevant for certain tasks. Although these efforts are promising, they are only in the preliminary stages of development. The natural language community has invested in a different form of ontological development. WordNet is a simple but comprehensive taxonomy of about 70,000 interrelated concepts that is being used in machine translation systems, health care applications, and World Wide Web interfaces.
Another important development has been the creation of easy-to-use tools for creating, evaluating, accessing, using, and maintaining reusable ontologies by both individuals and groups. The motivation is that ontology construction is difficult and time consuming and is a major barrier to the building of large-scale intelligent systems and software agents. Because many conceptualizations are intended to be useful for a wide variety of tasks, an important means of removing this barrier is to encode ontologies in a reusable form so that large portions of an ontology for a given application can be assembled from smaller ontologies, that are drawn from repositories. This work is also only in the preliminary stages of development.
3.7.3 Research Opportunities
Several research directions offer exceptional payback for NII infrastructure and applications: developing reusable ontologies for commonsense concepts, such as physical concepts (for example, time, space, material properties), NII concepts (such as, computers, networks, documents, bandwidth), social concepts (such as privacy and harm), and mental concepts (such as forgetting and attention); defining semiformal representation languages that support descriptions both informally in natural language and formally in a computer-interpretable knowledge representation language; implementing the next generation of ontology construction tools (these tools should include capabilities for browsing and visualizing ontologies, detecting inconsistencies, and semiautonomously synthesizing ontologies based on the use of terms in natural language documents; and devising strategies that agents can use to detect communications problems stemming from inconsistent ontologies and developing translation algorithms so that intelligent agents can agree on a common communication substrate.
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