A KNOWLEDGE REPRESENTATION LANGUAGE FOR NATURAL LANGUAGE PROCESSING, SIMULATION AND REASONING

2012 ◽  
Vol 06 (01) ◽  
pp. 3-23 ◽  
Author(s):  
MARJORIE McSHANE ◽  
SERGEI NIRENBURG
Keyword(s):  
Knowledge Representation ◽  
Language Processing ◽  
Intelligent Agents ◽  
Cognitive Modeling ◽  
Special Focus ◽  
Human Beings ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
The Many ◽  
Knowledge Memory

OntoAgent is an environment that supports the cognitive modeling of societies of intelligent agents that emulate human beings. Like traditional intelligent agents, OntoAgent agents execute the core functionalities of perception, reasoning and action. Unlike most traditional agents, they engage in extensive "translation" functions in order to render perceived inputs into the unambiguous, ontologically-grounded knowledge representation language (KRL) that is used to model their knowledge, memory and reasoning. This paper describes the KRL of OntoAgent with a special focus on the many runtime functions used to translate between perceived inputs and the KRL, as well as to manipulate KRL structures for reasoning and simulation.

scholarly journals NKRL, a knowledge representation tool for encoding the ‘meaning’ of complex narrative texts

1997 ◽  
Vol 3 (2) ◽  
pp. 231-253 ◽  
Author(s):  
GIAN PIERO ZARRI
Keyword(s):  
Knowledge Representation ◽  
Data Structures ◽  
Semantic Content ◽  
Narrative Texts ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Original Narrative

In this paper, we describe NKRL (Narrative Knowledge Representation Language), a language designed for representing, in a standardized way, the semantic content (the ‘meaning’) of complex narrative texts. After having introduced informally the four ‘components’ (specialized sub-languages) of NKRL, we will describe (some of) the data structures proper to each of them, trying to show that the NKRL coding retains the main informational elements of the original narrative expressions. We will then focus on an important subset of NKRL, the so-called AECS sub-language, showing in particular that the operators of this sub-language can be used to represent some sorts of ‘plural’ expressions.

scholarly journals Vicious Circle Principle and Logic Programs with Aggregates

2014 ◽  
Vol 14 (4-5) ◽  
pp. 587-601 ◽  
Author(s):  
MICHAEL GELFOND ◽  
YUANLIN ZHANG
Keyword(s):  
Knowledge Representation ◽  
Vicious Circle ◽  
Logic Programs ◽  
Vicious Circle Principle ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Answer Sets ◽  
Mathematical Semantics

AbstractThe paper presents a knowledge representation language $\mathcal{A}log$ which extends ASP with aggregates. The goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of $\mathcal{A}log$, an algorithm for computing its answer sets, and comparison with other approaches.

Network Morphology

2020 ◽  
Author(s):  
Andrew Hippisley
Keyword(s):  
Special Focus ◽  
Lexical Knowledge ◽  
The West ◽  
Multiple Inheritance ◽  
Case System ◽  
Slavonic Language ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Global And Local ◽  
Network Morphology

The morphological machinery of a language is at the service of syntax, but the service can be poor. A request may result in the wrong item (deponency), or in an item the syntax already has (syncretism), or in an abundance of choices (inflectional classes or morphological allomorphy). Network Morphology regulates the service by recreating the morphosyntactic space as a network of information sharing nodes, where sharing is through inheritance, and inheritance can be overridden to allow for the regular, irregular, and, crucially, the semiregular. The network expresses the system; the way the network can be accessed expresses possible deviations from the systematic. And so Network Morphology captures the semi-systematic nature of morphology. The key data used to illustrate Network Morphology are noun inflections in the West Slavonic language Lower Sorbian, which has three genders, a rich case system and three numbers. These data allow us to observe how Network Morphology handles inflectional allomorphy, syncretism, feature neutralization, and irregularity. Latin deponent verbs are used to illustrate a Network Morphology account of morphological mismatch, where morphosyntactic features used in the syntax are expressed by morphology regularly used for different features. The analysis points to a separation of syntax and morphology in the architecture of the grammar. An account is given of Russian nominal derivation which assumes such a separation, and is based on viewing derivational morphology as lexical relatedness. Areas of the framework receiving special focus include default inheritance, global and local inheritance, default inference, and orthogonal multiple inheritance. The various accounts presented are expressed in the lexical knowledge representation language DATR, due to Roger Evans and Gerald Gazdar.

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