Automated Knowledge-Representation (KR) System

An Automated Knowledge-Representation (KR) System is an information processing system that implements a KR algorithm to solve a KR task.

• Context:
  • It can (typically) use a Knowledge Representation Language.
  • It can range from being an Exact Knowledge Representation System to being an Approximate Knowledge Representation System.
  • It can be used by a Knowledge Engineer.
  • …
• Example(s):
  • one based on a Structured Inheritance Network.
  • a Neural Network nowledge Representation System.
  • a Conceptual Network Knowledge Representation System (Yibo, 2004),
  • a Geographic Knowledge Representation System (e.g. Wang et al., 2019),
  • a Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (Zouari et al., 2018),
  • a KL-ONE Knowledge Representation System (Brachman & Schmolze, 1985),
  • a Medical Knowledge Representation System (Buchtela et al., 2008),
  • a PARKA Knowledge Representation System (Evett, 1994),
  • a Pawlak's Knowledge Representation System (see Vakarelov, 1995),
  • a PowerLoom Knowledge Representation and Reasoning System (PowerLoom Website, 2017),
  • a Semantic Knowledge Representation System (e.g. Hendler & Harmelen, 2008).
  • …
• Counter-Example(s):
  • a Language Representation System;
  • a Natural Language Understanding System;
  • a Semantic Network;
  • an Automated Database Modeling System.
• See: Knowledge Database, First Order Logic, Web Ontology Language, Procedural Code, Expert System, Automated Reasoning, Semantic Web, Resource Description Framework, Decision Support System.

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References

2019a

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning#Overview Retrieved:2019-10-20.
  • Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used to solve complex problems.

    The justification for knowledge representation is that conventional procedural code is not the best formalism to use to solve complex problems. Knowledge representation makes complex software easier to define and maintain than procedural code and can be used in expert systems.

    For example, talking to experts in terms of business rules rather than code lessens the semantic gap between users and developers and makes development of complex systems more practical.

    Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system. A key trade-off in the design of a knowledge representation formalism is that between expressivity and practicality. The ultimate knowledge representation formalism in terms of expressive power and compactness is First Order Logic (FOL). There is no more powerful formalism than that used by mathematicians to define general propositions about the world. However, FOL has two drawbacks as a knowledge representation formalism: ease of use and practicality of implementation. First order logic can be intimidating even for many software developers. Languages which do not have the complete formal power of FOL can still provide close to the same expressive power with a user interface that is more practical for the average developer to understand. The issue of practicality of implementation is that FOL in some ways is too expressive. With FOL it is possible to create statements (e.g. quantification over infinite sets) that would cause a system to never terminate if it attempted to verify them. Thus, a subset of FOL can be both easier to use and more practical to implement. This was a driving motivation behind rule-based expert systems. IF-THEN rules provide a subset of FOL but a very useful one that is also very intuitive. The history of most of the early AI knowledge representation formalisms; from databases to semantic nets to theorem provers and production systems can be viewed as various design decisions on whether to emphasize expressive power or computability and efficiency. In a key 1993 paper on the topic, Randall Davis of MIT outlined five distinct roles to analyze a knowledge representation framework: * A knowledge representation (KR) is most fundamentally a surrogate, a substitute for the thing itself, used to enable an entity to determine consequences by thinking rather than acting, i.e., by reasoning about the world rather than taking action in it. * It is a set of ontological commitments, i.e., an answer to the question: In what terms should I think about the world? * It is a fragmentary theory of intelligent reasoning, expressed in terms of three components: (i) the representation's fundamental conception of intelligent reasoning; (ii) the set of inferences the representation sanctions; and (iii) the set of inferences it recommends.

    • It is a medium for pragmatically efficient computation, i.e., the computational environment in which thinking is accomplished. One contribution to this pragmatic efficiency is supplied by the guidance a representation provides for organizing information so as to facilitate making the recommended inferences.
    • It is a medium of human expression, i.e., a language in which we say things about the world.
  • Knowledge representation and reasoning are a key enabling technology for the Semantic Web. Languages based on the Frame model with automatic classification provide a layer of semantics on top of the existing Internet. Rather than searching via text strings as is typical today, it will be possible to define logical queries and find pages that map to those queries. The automated reasoning component in these systems is an engine known as the classifier. Classifiers focus on the subsumption relations in a knowledge base rather than rules. A classifier can infer new classes and dynamically change the ontology as new information becomes available. This capability is ideal for the ever-changing and evolving information space of the Internet. The Semantic Web integrates concepts from knowledge representation and reasoning with markup languages based on XML. The Resource Description Framework (RDF) provides the basic capabilities to define knowledge-based objects on the Internet with basic features such as Is-A relations and object properties. The Web Ontology Language (OWL) adds additional semantics and integrates with automatic classification reasoners.

2019b

• (PARKA Project, 2019) http://www.cs.umd.edu/projects/plus/Parka/parka-cm.html Retrieved:2019-10-20.
  • QUOTE: PARKA is a frame-based knowledge representation system which uses the massive parallelism of the Connection Machine (CM) to perform certain inferences extremely rapidly. PARKA performs property inheritance, set-theoretic inferences, and inferences on partonomic relations. To date, the system has been implemented both on the CM-2 and CM-5 massively parallel supercomputers made by Thinking Machines Corporation. The Connection Machine supports a SIMD (Single Instruction stream, Multiple Data stream) programming model in which each processor (the CM-2 has up to 65536 of them) either performs the same instruction or sits idle.

2019c

• (Wang et al., 2019) ⇒ Shu Wang, Xueying Zhang, Peng Ye, Mi Du, Yanxu Lu, and Haonan Xue. (2019). “Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation.” In: SPRS International Journal of Geo-Information, 8(4). doi:10.3390/ijgi8040184

2017

• (PowerLoom Website, 2017) ⇒ https://www.isi.edu/isd/LOOM/PowerLoom/ Last modified: May 01, 2017.
  • QUOTE: PowerLoom® is the successor to the Loom® knowledge representation system. It provides a language and environment for constructing intelligent, knowledge-based applications. PowerLoom uses a fully expressive, logic based representation language (a variant of KIF). It uses a natural deduction inference engine that combines forward and backward chaining to derive what logically follows from the facts and rules asserted in the knowledge base. While PowerLoom is not a description logic, it does have a description classifier which uses technology derived from the Loom classifier to classify descriptions expressed in full first order predicate calculus (...). PowerLoom uses modules as a structuring device for knowledge bases, and ultra-lightweight worlds to support hypothetical reasoning.

    To implement PowerLoom we developed a new programming language called STELLA, which is a Strongly Typed, Lisp-like Language that can be translated into Lisp, C++ and Java. PowerLoom is written in STELLA and therefore available in Common-Lisp, C++ and Java versions.

     PowerLoom is being developed at the Intelligent Systems Division of USC's Information Sciences Institute (ISI).

2018

• (Zouari et al., 2018) ⇒ Mariam Zouari, Nesrine Baklouti, Javier Sanchez Medina, Mounir Ben Ayed, and Adel M. Alimi. (2018). “An Evolutionary Hierarchical Interval Type-2 Fuzzy Knowledge Representation System (EHIT2FKRS) for Travel Route Assignment.”
  • QUOTE: ... an innovative advanced traffic management system based on Hierarchical Interval Type-2 Fuzzy Logic model optimized by the Particle Swarm Optimization (PSO) method. The aim of designing this system is to perform dynamic route assignment to relieve traffic congestion and limit the unexpected fluctuation effects on traffic flow. The suggested system is executed and simulated using SUMO, a well-known microscopic traffic simulator. For the present study, we have tested four large and heterogeneous metropolitan areas located in the cities of Sfax, Luxembourg, Bologna and Cologne. The experimental results proved the effectiveness of learning the Hierarchical Interval type-2 Fuzzy logic using real time particle swarm optimization technique PSO to accomplish multiobjective optimality regarding two criteria: number of vehicles that reach their destination and average travel time. The obtained results are encouraging, confirming the efficiency of the proposed system.

2008a

• (Hendler & Harmelen, 2008) ⇒ Jim Hendler, and Frankvan Harmelen. (2008). “The Semantic Web: Webizing Knowledge Representation.” In: "Handbook of Knowledge Representation" (Edited by F. van Harmelen, V. Lifschitz and B. Porter). Foundations of Artificial Intelligence - Elsevier Journal, 3(2008). doi:10.1016/S1574-6526(07)03021-0
  • QUOTE: In a traditional KR system we can generally assert a new class or predicate or formula, and within the KB it resides the name is unique. However, if we want to refer to it from outside that KB, there is generally not a way to do so. So if we want to say “the concept Student which is used by Jim Hendler’s Web Page” or “the concept person as defined in CYC” there needs to be an identifier for the concept, and traditional KR has not provided an externally addressable referent (...)

    Most KR systems have been designed in the past to assume that inconsistency is a problem, and to define mechanisms to rule it out (either by limiting expressivity or defining inconsistency as an error condition) or which provide some mechanism (like a belief revision mechanism) that triggers from knowing the sources of the inconsistency. Semantic Web KR appears to mandate either some form of local consistency or the development of paraconsistent or other, some argue higher order, logics that disallow the general proof of all concepts from an inconsistency.

2008b

• (Buchtela et al., 2008) ⇒ David Buchtela, Jan Peleska, Miroslav Zvolsky, Jana Zvarova (2008). "Medical knowledge representation system". Studies in health technology and informatics, 136, 377. DOI:10.3233/978-1-58603-864-9-377
  • QUOTE: The aim of this article is to present a design of a Medical Knowledge Representation System (MEKRES). The system automatically offers relevant formalized knowledge by extended GLIF (Guidelines Interchange Format) models to participants (patient, physician, operator, ..) on the basis of acquired data. This selection algorithm is based on key attributes and cooperation with knowledge ontologies.

2004

• (Yibo, 2004) ⇒ Gao Yibo (2004). "A Concept-based Knowledge Representation System". Microelectronics and Computer, vol. 21, no. 9, pp. 71-74.
  • QUOTE: This paper discusses the process and content of cognition and thinking, and gives an introduction to the Conceptual Network Knowledge Representation System (ConNet). Based on symbol structure and symbol analysis, ConNet takes the concept as the core of its theory, gets a network by connecting the concepts, and gives a solution to the using of background knowledge which is necessary to the study of AI. ConNet is suitable to all domains of knowledge, and has extensive applicability and infinite extendible space.

1995

• (Vakarelov, 1995) ⇒ Dimiter Vakarelov (1995). "A duality between Pawlak's knowledge representation systems and BI-consequence systems". Studia Logica, 55(1), 205-228.
  • QUOTE: The information in Pawlak's knowledge representation systems is represented in terms of objects, attributes (like "color") and values of attributes (like "green") and consists of listing for each object [math]\displaystyle{ x }[/math] and attribute [math]\displaystyle{ a }[/math] the set [math]\displaystyle{ f(x, a) }[/math] of all values of a possessed by [math]\displaystyle{ x }[/math]. I will call these systems attribute systems (A-systems for short). Several variations of A-systems under the name of information systems have been introduced by Pawlak in , .

1994

• (Evett, 1994) ⇒ Matthew Evett (1994). "Parka: A system for massively parallel knowledge representation" (Doctoral dissertation, University of Maryland).
  • QUOTE: PARKA is a frame-based knowledge representation system implemented on massively parallel hardware -- the Connection Machine (CM-2). PARKA provides a representation language consisting of concept descriptions (frames) and binary relations on those descriptions (slots). The system is designed explicitly to provide extremely fast property inheritance inference capabilities. In particular, PARKA can perform fast recognition queries of the form find all frames satisfying p property constraints in O(d+p) time -- proportional only to the depth, d of the knowledge base (KB), and independent of its size. This performance compares very favorably with serial systems. For conjunctive queries of this type, PARKA's performance is measured in tenths of a second, even for KBs with 100,000+ frames. A PARKA implementation of the Cyc commonsense KB yields similar timing results. In addition, for queries in a case-based planning domain of over 30,000 frames, PARKA has demonstrated speed-ups of more than 10,000 over a highly optimized serial representation system. Because PARKA's run-time performance is independent of KB size, it promises to scale up to arbitrarily larger domains. Thus, PARKA is computationally effective even for realistically sized KBs. Such run-time performance makes PARKA one the fastest knowledge representation systems in the world.

1985

• (Brachman & Schmolze, 1985) ⇒ Ronald J. Brachman, James G. Schmolze (1985). "An Overview of the KL-ONE Knowledge Representation System". In:Cognitive Science 1985. DOI:10.1207/s15516709cog0902_1.
  • QUOTE: 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 o number of places in the AI community (...)

    Literally speaking, KL-ONE (aka KLONE) is an implementation of some ideas about the structure of descriptions and their use in reasoning, a computational incarnation of what have been called structured inheritance networks (or S-Nets, ...).

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