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An improved version of IRACQ (for Interpretation Rule ACQuisition) is presented. I Our approach to semantic knowledge acquisition: 1 ) is in the context of a general purpose NL interface rather than one that accesses only databases, 2) employs a knowledge representation formalism with limited inferencing capabilities, 3) assumes a trained person but not an AI expert, and 4) provides a complete environment for not only acquiring semantic knowledge, but also maintaining and editing it in a consistent knowledge base. IRACQ is currently in use at the Naval Ocean Systems Center. . | AN ENVIRONMENT FOR ACQUIRING SEMANTIC INFORMATION Damaris M. Ayuso Varda Shaked and Ralph M. Weischedel BBN Laboratories Inc. 10 Moulton St. Cambridge MA 02238 Abstract An improved version of IRACQ for Interpretation Rule ACQuisition is presented.1 Our approach to semantic knowledge acquisition 1 is in the context of a general purpose NL interface rather than one that accesses only databases 2 employs a knowledge representation formalism with limited Inferencing capabilities 3 assumes a trained person but not an Al expert and 4 provides a complete environment for not only acquiring semantic knowledge but also maintaining and editing it in a consistent knowledge base. IRACQ Is currently In use at the Naval Ocean Systems Center. 1 Introduction The existence of commercial natural language interfaces NLI s such as INTELLECT from Artificial Intelligence Corporation and Q A from Symantec shows that NLI technology provides utility as an interface to computer systems. The success of all NLI technology Is predicated upon the availability of substantial knowledge bases containing Information about the syntax and semantics of words phrases and idioms as well as knowledge of the domain and of discourse context. A number of systems demonstrate a high degree of transportability in the sense that software modules do not have to be changed when moving the technology to a new domain area only the declarative domain specific knowledge need be changed. However creating the knowledge bases requires substantial effort and therefore substantial cost. It Is this assessment of the state of the art that causes US to conclude that knowledge acquisition is one of the most fundamental problems to widespread applicability of NLI technology. This paper describes our contribution to the acquisition of semantic knowledge as evidenced in IRACQ for Interpretation Rule ACQuisition within the context of our overall approach to representation of domain knowledge and Its use in the IRUS natural .
