The project described by the present paper aims at building a bridge between Intelligent Query Interfaces and Natural Language Generation technologies. The idea is to have a query interface enabling the users to access heterogeneous data sources by means of an integrated ontology. This paper shows how we are redesigning our intelligent query interface by rendering the logic-based queries in natural language, leveraging the results achieved to-date by applied Systemic-Functional Linguistics.
There is currently a growing interest in techniques for hiding parts of the signature of an ontology Kh that is being reused by another ontology Kv. Towards this goal, in this paper we propose the import-by-query framework, which makes the content of Kh accessible through a limited query interface. If Kv reuses the symbols from Kh in a certain restricted way, one can reason over Kv U Kh by accessing only Kv and the query interface. We map out the landscape of the import-by-query problem. In particular, we outline the limitations of our framework and prove that certain restrictions on the expressivity of Kh and the way in which Kv reuses symbols from Kh are strictly necessary to enable reasoning in our setting. We also identify cases in which reasoning is possible and we present suitable import-by-query reasoning algorithms.
The Intelligent Database Interface (IDI) is a cache-based interface that is designed to provide Artificial Intelligence systems with efficient access to one or more databases on one or more remote database management systems (DBMSs). It can be used to interface with a wide variety of different DBMSs with little or no modification since SQL is used to communicate with remote DBMSs and the implementation of the ID1 provides a high degree of portability. The query language of the ID1 is a restricted subset of function-free Horn clauses which is translated into SQL. Results from the ID1 are returned one tuple at a time and the ID1 manages a cache of result relations to improve efficiency. The ID1 is one of the key components of the Intelligent System Server (ISS) knowledge representation and reasoning system and is also being used to provide database services for the Unisys spoken language systems program.
An increasing number of biological databases are providing publicly accessible query interfaces. For example, archival databases such as the Mouse Genome Database (MGD) at the Jackson Laboratory, Genome Database (GDB) (Fasman et al. 1996) at Johns Hopkins School of Medicine, the Genome Sequence Database (GSDB) at National Center for Genome Resources(NCG 1997), and Genbank at the National Center for Biotechnology Information (Shuler el al. 1997), can all be queried via Web based interfaces. Exploring data in biological databases involves examining the structure (metadata) of the databases, browsing and querying the databases, interpreting the resuits of queries, and processing and viewing applicationspecific data types, such as protein and DNA sequences, using special data type-specific operations, such as sequence comparison, structure comparison, and protein structure visualization.