Gerard Ellis

Efficient Retrieval from Hierarchies of Objects using Lattice Operations

Managing large numbers of complex objects or descriptions requires sophisticated storage and retrieval mechanisms. Hierarchical classification techniques have proven to be useful for managing complex objects in practice. Previous classification methods have not used all the information recorded in hierarchies. We show how lattice operations can be used to navigate around a hierarchy. This is achieved by plunging the hierarchy into a boolean lattice of binary words. Greatest lower bound (GLB) and relative complementation (BUTNOT) lattice operations on the hierarchical structure (rather than the objects or descriptions themselves) are used to focus the search. Experiments show the number of objects compared when classifying objects using these techniques is significantly reduced.

A Conceptual Graphs Approach to Conceptual Schema Integration

The design of complex information systems usually involves the production of a number of separate conceptual design modules. These are subsequently integrated to form a global information schema. The integration of these modules is a well-known problem area in database systems design. With the likely availability of “off-the-shelf” modules in future this has become a crucial problem to solve. This paper presents an approach to the solution of this problem using conceptual graphs.

Managing complex objects in Peirce

Abstract The Peirce project (named after Charles Sanders Peirce) is an international collaborative project aiming to construct a freely available conceptual graphs workbench to support research in the conceptual graphs community in areas such as natural language processing, enterprise modelling, program specification and verification, management information systems, conceptual information retrieval, medical informatics, and construction of ontologies. Peirce advances the state of the art in conceptual graph implementations and in general complex object classification. At the core of the Peirce system is an abstract data type for partially ordered sets of objects (poset ADT). The poset ADT is used to organize a conceptual graph database. In this paper we give an overview of the innovative methods for complex object classification, and illustrate examples using complex object databases with hierarchies of chemical formulas, images and conceptual graph program specifications. We illustrate how conceptual graphs can be used for graphic programming in traditional domains and in organic chemistry and indicate how Peirces complex object database supports these activities.

The Birth of PEIRCE: A Conceptual Graphs Workbench

PEIRCE is a project aiming to build a state-of-the-art, industrial strength conceptual graph workbench. PEIRCE is integrating the conceptual graph development efforts that are taking place around the world. There are already over 40 researchers from over 8 countries involved in the project. This paper describes the PEIRCE project and the structure for managing the distributed development of PEIRCE. PEIRCE will provide a robust, portable, freely available conceptual graph workbench that will fast track new techniques into the community; facilitate comparison of competing techniques; help researchers cooperate in development; and speed application development.

Organization of Knowledge Using Order Factors

A central mechanism for the storage and retrieval of conceptual structures is the generalization hierarchy. Improvements on search in the hierarchy have been made by analyzing the order structure. We continue this by factorising the order structure into scales. We show how a membership operation can be improved by scaling of knowledge. The membership problem is a special case of the insertion problem for creating hierarchies. We give a O(k, logk n) membership algorithm for a class of orders known as term products that can be generated from existing term encoding methods.

Compiling Conceptual Graphs

This paper examines storage and retrieval of conceptual graphs using a directed acyclic graph data structure based on the partial order over conceptual graphs. We show how conceptual graphs in this hierarchy can be compiled into instructions which represent specialized cases of the canonical formation rules. Conceptual graphs are compiled as differences between adjacent graphs in the hierarchy. The differences represent the rules used in deriving the graph from the adjacent graphs. Compilation of conceptual graphs is effected in three ways: removal of redundant data, use of simple instructions which ignore redundant checks when performing matching, and by sharing common processing between graphs.