Accessing information sources to retrieve data requested by a user can be expensive, especially when dealing with distributed information sources. One way to reduce this cost is to cache the results of queries, or related classes of data. This paper presents an approach to caching and addresses the issues of which information to cache, how to describe what has been cached, and how to use the cached information to answer future queries. We consider these issues in the context of the SIMS information server, which is a system for retrieving information from multiple heterogeneous and distributed information sources. The design of this information server is ideal for representing and reusing cached information since each class of cached information is simply viewed as another information source that is available for answering future queries.

With the current explosion of data, retrieving and integrating information from various sources is a critical problem. Work in multidatabase systems has begun to address this problem, but it has primarily focused on methods for communicating between databases and requires significant effort for each new database added to the system. This paper describes a more general approach that exploits a semantic model of a problem domain to integrate the information from various information sources. The information sources handled include both databases and knowledge bases, and other information sources (e.g. programs) could potentially be incorporated into the system. This paper describes how both the domain and the information sources are modeled, shows how a query at the domain level is mapped into a set of queries to individual information sources, and presents algorithms for automatically improving the efficiency of queries using knowledge about both the domain and the information sources. This work is implemented in a system called SIMS and has been tested in a transportation planning domain using nine Oracle databases and a Loom knowledge base.

Noun phrases consisting of a sequence of nouns (sometimes referred to as nominal compounds) pose considerable difficulty for language analyzers but are common in many technical domains. The problems are compounded when some of the nouns in the sequence are ambiguously also verbs. The phrasal approach to language analysis, as implemented in PHRAN (PHRasal ANalyzer), has been extended to handle the recognition and partial analysis of such constructions. The phrasal analysis of a noun sequence is performed to an extent sufficient for continued analysis of the sentence in which it appears. PHRAN is currently being used as part of the SPAN (SPecification ANalysis) natural language interface to the USC Advanced Design AutoMation system (ADAM) (Granacki et al, 1985). PHRAN-SPAN is an interface for entering and interpreting digital system specifications, in which long noun sequences occur often. The extensions to PHRAN's knowledge base to recognize these constructs are described, along with the algorithm used to detect and resolve ambiguities which arise in the noun sequences.

We have developed an approach to natural language processing in which the natural language processor is viewed as a knowledge-based system whose knowledge is about the meanings of the utterances of its language. The approach is oriented around the phrase rather than the word as the basic unit. We believe that this paradigm for language processing not only extends the capabilities of other natural language systems, but handles those tasks that previous systems could perform in a more systematic and extensible manner.We have constructed a natural language analysis program called PHRAN (PHRasal ANalyzer) based in this approach. This model has a number of advantages over existing systems, including the ability to understand a wider variety of language utterances, increased processing speed in some cases, a clear separation of control structure from data structure, a knowledge base that could be shared by a language production mechanism, greater ease of extensibility, and the ability to store some useful forms of knowledge that cannot readily be added to other systems.