Some Thoughts to Consider 8 How difficult is it to get a group of people, or a group of companies, or a group of nations to agree on a particular ontology? How many real live systems are in production use today that make use of a full-scale inference engine and a knowledge base, like the design pattern we are studying? Why is the engineering of knowledge possessed by a true domain expert sometimes likened to psychoanalysis? What is a Truth Maintenance System? Check out Chapter 10 of the textbook. What does it mean to retract something from a knowledge base?

What’s With Chapters 9 & 10? Why are we having to struggle our way through these difficult sections? It is important to know how a reasoning system is implemented. Various design patterns and design decisions are possible, all influencing capability and performance. The very knowledge in the knowledge base can be represented, indexed, tabled, hashed, and associated in a variety of ways (sort of like looking at the insides of a DBMS). These design patterns and representation techniques are reusable as modules in other kinds of software.

Knowledge Representation Principles One cannot understand knowledge representation without doing it, or at least seeing it. The process of representing knowledge of a domain goes through 5 primary stages. The first, informal stage involves deciding what kinds of objects and relations need to be represented (the ontology). Then a vocabulary is selected, and used to encode general knowledge of the domain. After encoding specific problem instances, automated inference procedures can be used to solve them. Good representations eliminate irrelevant detail, capture relevant distinctions, and express knowledge at the most general level possible. Constructing knowledge based systems has advantages over programming: the knowledge engineer has to concentrate only on what’s true about the domain, rather than on solving the problems and encoding the solution process; the same knowledge can often be used in several ways; debugging knowledge is often simpler than debugging systems. Special-purpose ontologies, such as the one constructed for the circuits domain, can be effective within the domain but often need to be generalized to broaden their coverage. A general-purpose ontology needs to cover a wide variety of knowledge, and should be capable in principle of handling any domain. It is often necessary to develop ontologies based on categories and the event calculus; which includes the representation of structured objects, time and space, change, processes, substances, and beliefs. The nature of an appropriate representation depends on the world being represented and the intended range of uses of the representation.

A Guide to Chapter 10 Large-scale knowledge representation requires general-purpose ontology to organize and tie together the various specific domains of knowledge. The chapter covered an upper ontology based on categories and the event calculus. It covered structured objects, time and space, change, processes, substances, and beliefs. Actions, events, and time can be represented either in situation calculus or in more expressive representations such as event calculus and fluent calculus. Such representations enable an agent to construct plans by logical inference. The mental states of agents can be represented by strings that denote beliefs. Special purpose representation systems, such as semantic networks and description logics, have been devised to help in organizing a hierarchy of categories. Inheritance is an important form of inference, allowing properties of objects to be deduced from their membership in categories. The closed-world assumption, as implemented in logic programs, provides a simple way to avoid having to specify lots of negative information. It is best interpreted as a default that can be overridden by additional information. Nonmonotonic logics, such as circumscription and default logic, are intended to capture default reasoning in general. Answer set programming speeds up nonmonotonic inference, much as WalkSat speeds up propositional inference. Truth maintenance systems handle knowledge updates and revisions efficiently.

So, What’s in a Semantic Network? Mostly Nodes and Arcs. Emphasis on categories of entities (ontology). Relations among entities. Every semantic network or frame system can be defined in sentences of First Order Logic. Inheritance in a semantic network often replaces a chain of inference in First Order Logic. Inference in semantic networks can be much faster than in First Order Logic. Semantic networks have difficulty representing negation, omission, and disjunction. Semantic networks are easier to understand than First Order Logic and often can be rendered in meaningful visualizations. Description Logics are more expressive extensions of semantic networks that focus on categories, their definitions, and the classification of entities into categories.