A mathematical model of meaning and its application to multidatabase systems

Kitagawa, Takashi; Kiyoki, Yasushi

doi:10.1109/ride.1993.281933

Cited by 49 publications

(38 citation statements)

References 7 publications

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“…We define the eigenvector space I of candidate flame video clip of the size F rate as (Kitagawa & Kiyoki, 1993),…”

Section: Defining Semantic Projections Smentioning

confidence: 99%

“…. ; 2 v Þ, then the semantic space S can be represented as (Kitagawa & Kiyoki, 1993), S ¼ f0; P k1 ; . .…”

Section: Defining Semantic Projections Smentioning

confidence: 99%

“…Then the label of the feature vector with neatest distance will be assigned to the testing vector F t . It can be written as follows (Kitagawa & Kiyoki, 1993),…”

Section: Classifying Candidate Flame Videomentioning

confidence: 99%

See 2 more Smart Citations

Hierarchical detection of wildfire flame video from pixel level to semantic level

Zhao

Tang

2015

Expert Systems with Applications

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“…We define the eigenvector space I of candidate flame video clip of the size F rate as (Kitagawa & Kiyoki, 1993),…”

Section: Defining Semantic Projections Smentioning

confidence: 99%

“…. ; 2 v Þ, then the semantic space S can be represented as (Kitagawa & Kiyoki, 1993), S ¼ f0; P k1 ; . .…”

Section: Defining Semantic Projections Smentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical detection of wildfire flame video from pixel level to semantic level

Zhao

Tang

2015

Expert Systems with Applications

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“…Dimensionality reduction techniques of vector space model have been used for developing traditional vector space models such as latent semantic indexing [2] and the mathematical model of meaning [3,4]. These techniques are applied to information resources, characterized by elements in a flat domain.…”

Section: Related Workmentioning

confidence: 99%

A Discovery Method of Anteroposterior Correlation for Big Data Era

Nakanishi

2015

Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

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Abstract. In this paper, we present a new knowledge extraction method on Big data era. We introduce new concepts, anteroposterior correlation, and propose an extraction method of anteroposterior correlation. The anteroposterior correlation means the correlation based on the time anteroposterior relation. We consider that Heterogeneity, continuity, and visualization are the most critical features of Big data analytics, which provides a scale and connection merits based on them. No current data analysis methods are based on opened assumptions. Big data analytics provides a new data analysis method based on opening assumptions. In this paper, we especially focus on an aspect of heterogeneity. We discover a correlation in consideration of the continuity of time. By our method, we effectively discover relationships between heterogeneous things, events and phenomena. The anteroposterior correlations are represented in relative comparison with each conditional probability distribution. The one of the features of our method is a measurement correlation by using conditional probability. That is, we calculate the correlation relative by representing all in conditional probability, no absolutely. Our method is determined higher correlation by comparison to each heterogeneous thing, event and phenomenon. This is the most important points on the Big data era. When you apply current association rule extraction techniques, you obtain too big rule base to organize them. By our method, we realize the one of the methods for decision mining.

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“…For example, a well-known method of integrating different information sources transforms the proprietary interface of each source into a common information model using wrappers [9]. A mathematical model of semantics and its applications, such as to semantic association search methods, have been proposed to ensure semantic interoperability between DBs [10], [11].…”

Section: Related Workmentioning

confidence: 99%

Towards Integration Services for Heterogeneous Resources: An Integrated Search Engine forWeb Content and TV Programs

Miyamori

Stejic

Araki

et al. 2006

2006 Semantics, Knowledge and Grid, Second International Conference On

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A search engine that can handle TV programs and Web content in an integrated way is proposed. Conventional search engines can target Web content and/or data stored in desktop PCs. However, in the future, the information to be searched is expected to be stored in various places such as in hard-disk recorders, digital cameras, mobile devices, and even in real space, and a search engine that can search across such heterogeneous resources will become essential. As a first step towards developing such a next-generation search engine, a prototype search system is proposed that can handle Web content as an information source with hyperlinks and TV programs as another without them, that performs integrated searches of those content, and that can subsequently search for content related to each search result. An integrated search is achieved by generating integrated indices based on keywords obtained from TV programs and Web content and by ranking them, and a chain search for related content is done by calculating the similarities of and ranking the content in the integrated indices. A zoom-based display of the results enables information to be acquired efficiently. Testing a prototype of the system validated the approach of the proposed method.

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A mathematical model of meaning and its application to multidatabase systems

Cited by 49 publications

References 7 publications

Hierarchical detection of wildfire flame video from pixel level to semantic level

Hierarchical detection of wildfire flame video from pixel level to semantic level

A Discovery Method of Anteroposterior Correlation for Big Data Era

Towards Integration Services for Heterogeneous Resources: An Integrated Search Engine forWeb Content and TV Programs

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