An overview of the KL-ONE Knowledge Representation System
Cognitive SciencePublished 1 June 1985
R. J. Brachman, James G. Schmolze
Citations1,620
SJR quartileQ1
SJR score1.13
SNIP1.32
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The kernel ideas of KL-ONE are presented, emphasizing its ability to form complex structured descriptions, and notions of taxonomy and classification that are central to it are highlighted.
Abstract
KL-ONE is a system for representing knowledge in Artificial Intelligence programs. It has been developed and refined over a long period and has been used in both basic research and implemented knowledge-based systems in a number of places in the AI community. Here we present the kernel ideas of KL-ONE, emphasizing its ability to form complex structured descriptions. In addition to detailing all of KL-ONE's description-forming structures, we discuss a bit of the philosophy underlying the system, highlight notions of taxonomy and classification that are central to it, and include an extended example of the use of KL-ONE and its classifier in a recognition task.
Keywords
Computer Science
Naming and Necessity
5,279 Citations1972Saul A. Kripke
Artificial IntelligenceA logic for default reasoning
3,920 Citations1980Raymond Reiter
This paper proposes a logic for default reasoning, develops a complete proof theory and shows how to interface it with a top down resolution theorem prover, and provides criteria under which the revision of derived beliefs must be effected.
Principles of Artificial Intelligence
3,336 Citations1982Nils J. Nilsson
This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Elsevier eBooksWHAT'S IN A LINK
790 Citations1975William A. Woods
The chapter presents the logical inadequacies of almost all current network notations for representing quantified information and also discusses some of the disadvantages of a few logically adequate techniques.
Elsevier eBooksON THE EPISTEMOLOGICAL STATUS OF SEMANTIC NETWORKS**Prepared in part at Bolt Beranek and Newman Inc. under contracts sponsored by the Defense Advance Research Projects Agency and the Office of Naval Research. The views and conclusions stated are those of the author and should not be interpreted as necessarily representing the official policies, either express or implied, of the Defense Advanced Research Projects Agency or the U.S. Government.
623 Citations1979Ronald J. Brachman
This chapter examines in detail the history of a set of network-structured formalisms for knowledge representation—the so-called “semantic networks,” which were introduced around 1966 as a representation for the concepts underlying English words and were designed expressly to treat concepts as formal representational objects.
ComputerKrypton: A Functional Approach to Knowledge Representation
363 Citations1983Brachman, Fikes +1 more
The authors have developed a design strategy for avoiding these types of problems and have implemented a representation system based on it, called Krypton, which clearly distinguishes between definitional and factual information.
ACM SIGART BulletinArtificial intelligence meets natural stupidity
320 Citations1976Drew McDermott
As a field, artificial intelligence has always been on theborder of respectability, and therefore on the border of crackpottery, and is justifiably proud of its willingness to explore weird ideas, because pursuing them is the only way to make progress.
DSpace@MIT (Massachusetts Institute of Technology)Towards a Computational Theory of Definite Anaphora Comprehension in English Discourse
292 Citations1979Candace L. Sidner
The focussing mechanism is designed to take advantage of syntactic and semantic information encoded as constraints on the choice of anaphora interpretation, and provides a principled means for choosing when to apply the constraints in the comprehension process.
Elsevier eBooksOn Representations of Problems of Reasoning about Actions
268 Citations1981Saul Amarel
The chapter discusses a specific problem of transportation scheduling to evaluate the effects of alternative formulations of this problem on the expected efficiency of mechanical procedures for solving it and also to examine the processes that come into play when a transition takes place from a given problem formulation into a better one.
International Journal of Man-Machine StudiesWhat's in a concept: structural foundations for semantic networks
236 Citations1977Ronald J. Brachman
This paper examines the fundamentals of network notation, in order to understand why the “formalism” has not been the panacea it was once hoped to be and emphasizes the importance of considering an “epistemological foundation” on which to consistently build representations for complex concepts.
Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)A Structural Paradigm for Representing Knowledge.
214 Citations1978Ronald J. Brachman
A detailed look is taken at the history of semantic nets and begins to understand their inadequacies by examining closely what their representational pieces have been intended to model, and a new type of network is presented - the Structured Inheritance Network (SI-NET) - designed to circumvent common expressive shortcomings.
Elsevier eBooksENCODING KNOWLEDGE IN PARTITIONED NETWORKS
167 Citations1979Gary G. Hendrix
This chapter discusses network notations for encoding a number of different kinds of knowledge, including taxonomic information; general statements involving quantification; information about processes and procedures; the delineation of local contexts, beliefs, and wishes; and the relationships between syntactic units and their interpretations.
Classification in the KL-ONE knowledge representation system
148 Citations1983James G. Schmolze, Thomas Lipkis
This work discusses issues and demonstrate some uses of the classification algorithm, which takes a new Concept and determines other Concepts that either subsume it or that it subsumes, thereby determining the location for the new Concept within a given taxonomy.
ComputerWhat's Important About Knowledge Representation?
75 Citations1983Woods
The author discusses a number of issues that serve as research goals for discovering the principles of knowledge representation, using techniques and concepts evolved while developing the knowledge-representation system KL-one as illustrations.
Elsevier eBooksTHE STRUCTURE AND ORGANIZATION OF A SEMANTIC NET FOR COMPREHENSION AND INFERENCE
72 Citations1979Lenhart K. Schubert, Randolph G. Goebel +1 more
A network representation for propositional knowledge that is capable of encoding any proposition expressible in natural language, and the feasibility of building such hierarchies, inserting information into them automatically, and accessing the inserted information with a second experimental implementation is demonstrated.
Topics in information systemsSome Remarks on the Semantics of Representation Languages
30 Citations1984David Israël, Ronald J. Brachman
It has been said many times that semantic nets are mere notational variants of predicate calculus, but before laying down nets, it ought at least to be clear about what predicate calculus is.
An information presentation system
30 Citations1981Frank Zdybel, Norton R. Greenfeld +2 more
The concept of an Information Presentation System (IPS), the intimate relationship between IPS goals and knowledge representation issues, and some of the architecture of AIPS are described.
Computers & Mathematics with ApplicationsInterpreting network formalisms
28 Citations1983David Israël
This work attempts to sketch adequate semantic accounts for at least two (kinds of) semantic network formalisms; one, based on the notion of inheritance, one, not.
Research in Knowledge Representation for Natural Language Understanding
24 Citations1980William A. Woods