Information retrieval in schema-based P2P systems using one-dimensional semantic space

2007 ◽  
Vol 51 (16) ◽  
pp. 4543-4560 ◽  
Author(s):  
Tao Gu ◽  
Hung Keng Pung ◽  
Daqing Zhang
Keyword(s):  
Information Retrieval ◽  
Semantic Space ◽  
One Dimensional ◽  
P2p Systems

A quantum-based semiotic model for textual semantics

Kybernetes ◽  
2018 ◽  
Vol 47 (2) ◽  
pp. 307-320 ◽  
Author(s):  
Francesco Galofaro ◽  
Zeno Toffano ◽  
Bich-Liên Doan
Keyword(s):  
Information Retrieval ◽  
Quantum Theory ◽  
Philosophy Of Language ◽  
Formal Model ◽  
Semantic Space ◽  
Turing Test ◽  
Point Of View ◽  
Content Type ◽  
Semantic Organization ◽  
Semiotic Model

Purpose The paper aims to provide a semiotic interpretation of the role played by entanglement in quantum-based models aimed to information retrieval and suggests possible improvements. Actual models are capable of retrieving documents relevant to a query composed of a keyword and its acceptation expressed by a given context. The paper also considers some analogies between this technique and quantum-based approaches in other disciplines to discuss the consequence of this quantum turn, as epistemology and philosophy of language are concerned. Design/methodology/approach We use quantum geometry to design a formal model for textual semiotics. In particular, the authors refer to Greimas’s work on semantics and information theory, to Eco’s writings on semantic memory and to Lotman’s work on a cybernetic notion of culture. Findings Quantum approaches imply a particular point of view on meaning. Meaning is not a real, positive quality of a given word. It is a net of relations constructed in the text, whose value is progressively determined during the reading process. Furthermore, reading is not a neutral operation: to read is to determine meaning. If it is said that, from a general semiotic point of view, meaning is stored in quantum semantic memories and is read/written by semantic machines, then the operation of “reading/writing” is analogous to the operation of measuring in quantum theory: in other terms, meaning is a value, and this implies an instance (not necessarily human) according to which values are valuable. Research limitations/implications The authors are not proposing a complete quantum semantics. At the present, quantum information retrieval can detect the presence of semantic relations. The authors suggest a way to characterize them, leaving open the problem on how to formalize the document as a vector in four-state semantic space. Practical implications A quantum turn shows deep semiotic implications on the approach to language, which shows an immanent semantic organization not reducible to syntax and morphology. This organization is probabilistic and indeterministic and explains to what extent text fixes the meaning of its lexical units. Social implications In the authors’ perspective, signification is not the exclusivity of a human subject. Criticizing Turing test, the great semiotic and cybernetic scholar Jurij Lotman wrote that if we identify “intelligent” and “human”, we raise the failings of an actual form of intelligence to the rank of an essential characteristic. On this line, meaning is considered as a feature of social, artificial and biological systems. Originality/value The adoption of quantum formalism seems in line with cybernetic framework, involving a probabilistic, non-cartesian point of view on meaning aimed to critically discuss the human–machine relation. Furthermore, Quantum theory (QT) implies a phenomenological point of view on the conditions of possibility of meaning.

Image-Word Mapping

2011 ◽  
pp. 78-87
Author(s):  
Yang Cai ◽  
David Kaufer
Keyword(s):  
Information Retrieval ◽  
Visual Information ◽  
Ambient Intelligence ◽  
Semantic Differential ◽  
Human Computer Interactions ◽  
One Dimensional ◽  
Information Classification ◽  
Facial Information ◽  
Mapping Process ◽  
Facial Identification

No Ambient Intelligence can survive without human-computer interactions. Over ninety percent of information in our communication is verbal and visual. The mapping between one-dimensional words and two-dimensional images is a challenge for visual information classification and reconstruction. In this Chapter, we present a model for the image-word two-way mapping process. The model applies specifically to facial identification and facial reconstruction. It accommodates through semantic differential descriptions, analogical and graph-based visual abstraction that allows humans and computers to categorize objects and to provide verbal annotations to the shapes that comprise faces. An image-word mapping interface is designed for efficient facial recognition in massive visual datasets. We demonstrate how a two-way mapping of words and facial shapes is feasible in facial information retrieval and reconstruction.

scholarly journals Quantum Cognitive Triad: Semantic Geometry of Context Representation

2020 ◽  
Author(s):  
Ilya A. Surov
Keyword(s):  
Semantic Representation ◽  
Cognitive Structure ◽  
Semantic Space ◽  
Situational Factor ◽  
Mathematical Apparatus ◽  
Bloch Sphere ◽  
One Dimensional ◽  
Cognitive Triad ◽  
Decision Alternative ◽  
Qubit States

The paper describes an algorithm for semantic representation of behavioral contexts relative to a dichotomic decision alternative. The contexts are represented as quantum qubit states in two-dimensional Hilbert space visualized as points on the Bloch sphere. The azimuthal coordinate of this sphere functions as a one-dimensional semantic space in which the contexts are accommodated according to their subjective relevance to the considered uncertainty. The contexts are processed in triples defined by knowledge of a subject about a binary situational factor. The obtained triads of context representations function as stable cognitive structure at the same time allowing a subject to model probabilistically-variative behavior. The developed algorithm illustrates an approach for quantitative subjectively-semantic modeling of behavior based on conceptual and mathematical apparatus of quantum theory.

scholarly journals Query Sense Discovery Approach to Realize the User's Search Intent

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Performance Measures ◽  
Evolutionary Process ◽  
Semantic Space ◽  
Precise Meaning ◽  
User Query ◽  
Determination Process ◽  
The One ◽  
System Operating

The main goal of information retrieval is getting the most relevant documents to a user’s query. So, a search engine must not only understand the meaning of each keyword in the query but also their relative senses in the context of the query. Discovering the query meaning is a comprehensive and evolutionary process; the precise meaning of the query is established as developing the association between concepts. The meaning determination process is modeled by a dynamic system operating in the semantic space of WordNet. To capture the meaning of a user query, the original query is reformulating into candidate queries by combining the concepts and their synonyms. A semantic score characterizing the overall meaning of such queries is calculated, the one with the highest score was used to perform the search. The results confirm that the proposed "Query Sense Discovery" approach provides a significant improvement in several performance measures.

Efficient distributed semantic based data and service unified discovery with one-dimensional semantic space

2015 ◽  
Vol 49 ◽  
pp. 78-87 ◽  
Author(s):  
Ying Zhang ◽  
Houkuan Huang ◽  
Hui He ◽  
Jing Teng ◽  
Zhuxiao Wang
Keyword(s):  
Semantic Space ◽  
One Dimensional

scholarly journals A Heterogeneous System Based on Latent Semantic Analysis Using GPU and Multi-CPU

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Gabriel A. León-Paredes ◽  
Liliana I. Barbosa-Santillán ◽  
Juan J. Sánchez-Escobar
Keyword(s):  
Information Retrieval ◽  
Latent Semantic Analysis ◽  
Graphics Processing Units ◽  
Semantic Analysis ◽  
Computational Cost ◽  
Heterogeneous Systems ◽  
Semantic Space ◽  
General Purpose ◽  
Space Construction ◽  
Value Decomposition

Latent Semantic Analysis (LSA) is a method that allows us to automatically index and retrieve information from a set of objects by reducing the term-by-document matrix using the Singular Value Decomposition (SVD) technique. However, LSA has a high computational cost for analyzing large amounts of information. The goals of this work are (i) to improve the execution time of semantic space construction, dimensionality reduction, and information retrieval stages of LSA based on heterogeneous systems and (ii) to evaluate the accuracy and recall of the information retrieval stage. We present a heterogeneous Latent Semantic Analysis (hLSA) system, which has been developed using General-Purpose computing on Graphics Processing Units (GPGPUs) architecture, which can solve large numeric problems faster through the thousands of concurrent threads on multiple CUDA cores of GPUs and multi-CPU architecture, which can solve large text problems faster through a multiprocessing environment. We execute the hLSA system with documents from the PubMed Central (PMC) database. The results of the experiments show that the acceleration reached by the hLSA system for large matrices with one hundred and fifty thousand million values is around eight times faster than the standard LSA version with an accuracy of 88% and a recall of 100%.

A one-dimensional self-gravitating stellar gas

1966 ◽  
Vol 25 ◽  
pp. 46-48 ◽  
Author(s):  
M. Lecar
Keyword(s):  
Gravitational Field ◽  
Phase Space ◽  
Motion Picture ◽  
Space Distribution ◽  
Two Dimensional ◽  
One Dimensional ◽  
Phase Space Distribution ◽  
Dimensional Phase Space ◽  
The Mean

“Dynamical mixing”, i.e. relaxation of a stellar phase space distribution through interaction with the mean gravitational field, is numerically investigated for a one-dimensional self-gravitating stellar gas. Qualitative results are presented in the form of a motion picture of the flow of phase points (representing homogeneous slabs of stars) in two-dimensional phase space.

Demonstration of the Use of Tape-Recorded Information from a High Vacuum Electron Microscope

1969 ◽  
Vol 27 ◽  
pp. 218-219
Author(s):  
Richard E. Hartman ◽  
Roberta S. Hartman ◽  
Peter L. Ramos
Keyword(s):  
Information Retrieval ◽  
Electron Microscope ◽  
Magnetic Tape ◽  
High Vacuum ◽  
Data Retrieval ◽  
Electronic Information ◽  
Electronic Data ◽  
Subsequent Processing ◽  
Continuous Record

We have long felt that some form of electronic information retrieval would be more desirable than conventional photographic methods in a high vacuum electron microscope for various reasons. The most obvious of these is the fact that with electronic data retrieval the major source of gas load is removed from the instrument. An equally important reason is that if any subsequent analysis of the data is to be made, a continuous record on magnetic tape gives a much larger quantity of data and gives it in a form far more satisfactory for subsequent processing.

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