Quillian, M.R.: Word concepts: A theory and simulation of some basic semantic capabilities. Behavioural Science 12 (1967)  Home/Search   Document Not in Database   Summary   Related Articles   Check

....and the model s performance in comparison with the existing psychological experimental data is discussed. 2 Preliminaries. In this section we briefly discuss the static and dynamic features upon which the model is based. The model s static structure is a semantic network, originated by Quillian [15, 16, 17] as a mode of knowledge representation. Its primary motivation was the Economy principle stating that items are stored without redundancy, as is described further below. Information is represented by a set of nodes, representing concepts, connected to each other by a set of edges (pointers) which ....

Quillian M.R., Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Sci. 12, 410-430, 1967.

....relationship connections as defined by the ontology. The system is called ONTOCOPI (ONTOlogy based Community Of Practice Identifier) and is currently implemented as a Protege ( 20] plug in as well as a standalone Web accessible program. Spreading activation was first introduced by Quillian [38] to simulate human semantic processing in a machine subsequently it has formed the basis for many information retrieval methods such as semantic similarity measures, Web analysis algorithms, community identification, case based reasoning, etc. ONTOCOPI s algorithm combines and improves ideas from ....

M.R Quillian, `Word concepts: A theory and simulation of some basic semantic capabilities', Behavioral Science, 12, 410--430, (1967).

....queries of this sort Hydra has special commands to return the functions and instances that make up the associations. This has knock on effects throughout the language. For instance dealing with the problem of functions as data elements) Associations in graphs are also used in semantic networks [15]. In this section we show that associative queries can be written in Spider without the need for any extensions to the language. r r p a a a a a Figure 3.3 Figure 3.4 6 1 2 3 4 5 1 4 5 6 7 3 1 13 result Y X ShortestPath arg2 tagged arg1 ShortestPath FAILED GetThePath result Y X keep ....

Quillian M.R. (1967) Word Concepts: A Theory and Simulation of Some Basic Semantic Capabilities. Behavioural Science 12, 1967. pp. 410-430.

....in the theorem. 5 Related Work ALL draws from several diverse fields. We attempt only to sketch in general terms the fields from which it draws and discuss a few particularly relevant past approaches. ALL draws from semantic networks [Brachman et al. 83, Bobrow Winograd, 77, Findler, 79, Quillian, 67, Shapiro, 89, Vilain, 85] the intuition that retrieval and reasoning can be guided and limited by the structure of the network. This has long been a key intuition behind semantic networks: the knowledge required to perform an intellectual task generally lies in the semantic vicinity of the ....

Quillian, M. Ross. (1967). Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science 12, 410-430. (Reprinted in [Brachman & Levesque, 85], pp. 98-118.)

....is given, our algorithm should reason about the system and about the problem instance, and should find a solution to the problem. In order to handle such problems, researchers often use general schemes of knowledge representation such as First Order Logic [6] logic programs [4] semantic networks [10], etc. Moreover, the reasoning is then often carried out using general schemes of reasoning such as resolution for theorem proving, Prolog for logic programs, etc. Consider the following question: Can our agent improve its on line performance in a case where the system it uses (e.g. its knowledge ....

M. Quillian. Word concepts: a theory and simulation of some basic semantic capabilities. Behav. Sci., 12:410--430, 1967.

....in types, and each symbol is created as a token, an instance of some type. The types form semantic hierarchies, the instances inherit the properties of parent types and also accumulate specific properties of their own during processing. Whether implemented in a symbolic semantic network [40] or in a semantic network subsymbolic neural network hybrid [53; 54; 55] in effect, there are two separate systems with a very complex interaction. One system performs reasoning based on semantic relations and the other one propagates the bindings. In the ID content approach, on the other hand, ....

M. R. Quillian. Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science, 12:410--430, 1967.

....of useful techniques and tools which provides no formal guarantees. 8.1.2 Related Formalisms ALL draws from several diverse fields. We attempt only to sketch in general terms the fields from which it draws and discuss a few particularly relevant past approaches. ALL draws from semantic networks [Quillian, 67, Bobrow Winograd, 77, Findler, 79, Brachman et al. 83, Vilain, 85, Shapiro, 89] the intuition that retrieval and reasoning can be guided and limited by the structure of the network. This has long been a key intuition behind semantic networks: the knowledge required to perform an ....

Quillian, M. Ross. (1967). Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science 12, 410-430. (Reprinted in [Brachman & Levesque, 85], pp. 98-118.)

....for a simple path is quadratic; that for an extended path between families of closely related concepts is cubic. 1. Introduction An essential reasoning task in network like knowledge representation formalisms is finding paths between two or more concepts (Shapiro 1991, Selman Levesque 1991) Quillian (1967) proposed manipulations on networks to make inference about pairs of concepts by finding connections between nodes that represent them. Finding a path can be reduced to the problem of link traversal (Shastri 1991) The existing path finding methods take a start concept S and follow one of its ....

....be applied. To limit the complexity of search, constraints on the path length can be imposed (Szpakowicz Koperczak 1990) but then the solution 1043 may not be found even if it exists. Parallel search methods have been proposed, such as the spreading activation method, first mentioned by Quillian (1967). When people perform this kind of search, they do not find it difficult. For example, when one is shown a drawing of a network and asked to find a path between two given concepts, a solution will be found with little blind search or backtracking that the algorithmic methods require. This ....

Quillian, M. R. (1967) "Word Concepts: A Theory and Simulation of Some Basic Semantic Capabilities". Behavioral Science 12, 410-430.

....and vice versa. 1 For this reason, BNs more closely mimic human reasoning, which is able to modify the belief of a hypothesis from both causal and evidential support. It is not surprising, since associative networks were introduced in Artificial Intelligence as an attempt to model human memory [65]. The main shortcoming of BNs is that they must be represented by acyclic directed graphs. This constitutes a limitation as to the models which can be represented in this formalism. Accordingly, they are not suited for domains where feedback plays an important role. A related drawback is that, ....

M. R. Quillian. Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Science, 12:410--430, 1967.

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Quillian, M.R.: Word concepts: A theory and simulation of some basic semantic capabilities. Behavioural Science 12 (1967)

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M. R. Quillian. Word concepts: A theory and simulation of some basic semantic capabilities. Behavioral Sciences, 12:410--430, 1967.

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M.R. Quillian, "Word Concepts: A Theory and Simulation of Some Basic Semantic Capabilities," Behavioral Science 12, 1967, pp. 410-430.

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Quillian M.R., 1967 "Word Concepts: a Theory and Simulation of some Basic Semantic Capabilities", in Behavioral Science, 12 (410-430).

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Quillian MR, 1967, Word concepts: a theory and simulation of some basic semantic capabilities, Behav. Science, 12, 410-430.

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