Author: Peter Karp

We have developed a Web server tool, written in Common Lisp, that allows existing graphical user interface applications written using the Common Lisp Interface Manager (CLIM) to hook easily into the WWW.

This article provides an overview of the DB-interoperation problem, and offers several solutions. It discusses how links are used in molecular biology DBs, and describes the potential stumbling blocks when DB links are created and used.

We describe the design and implementation of visual presentations that closely mimic those found in the biology literature, and employ a frame knowledge representation system (FRS) called HyperTHEO to manage the EcoCyc knowledge base.

We describe the design and implementation of a storage subsystem that submerges a database management system (DBMS) within a knowledge representation system.

We have developed a Web server tool, written in Common Lisp, that allows any existing graphical user interface application written using the Common Lisp Interface Manager (CLIM) to hook easily into the WWW.

The Generic Frame Protocol (GFP) is an application program interface for accessing knowledge bases stored in frame knowledge representation systems (FRSs).

Our research investigates the hypothesis that one can employ an existing database management system (DBMS) as a storage subsystem for an FRS, to provide high-speed access to large, shared KBs.

The chief contributions of the paper are a minimized representation for biochemical pathways called the predecessor list, and inference procedures for converting the predecessor list into a pathway-graph representation that can serve as input to a pathway-drawing algorithm.

This paper presents algorithms for drawing metabolic pathways by dynamically querying the underlying knowledge base.

This article describes challenges and opportunities for addressing computational problems in the metabolism with techniques from knowledge representation, planning, integration of heterogeneous databases, qualitative reasoning, knowledge acquisition, and machine learning.