The Experts below are selected from a list of 238281 Experts worldwide ranked by ideXlab platform
Luciano Floridi - One of the best experts on this subject based on the ideXlab platform.
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Semantic Information and the network theory of account
Synthese, 2012Co-Authors: Luciano FloridiAbstract:The article addresses the problem of how Semantic Information can be upgraded to knowledge. The introductory section explains the technical terminology and the relevant background. Section 2 argues that, for Semantic Information to be upgraded to knowledge, it is necessary and sufficient to be embedded in a network of questions and answers that correctly accounts for it. Section 3 shows that an Information flow network of type A fulfils such a requirement, by warranting that the erotetic deficit, characterising the target Semantic Information t by default, is correctly satisfied by the Information flow of correct answers provided by an Informational source s. Section 4 illustrates some of the major advantages of such a Network Theory of Account (NTA) and clears the ground of a few potential difficulties. Section 5 clarifies why NTA and an Informational analysis of knowledge, according to which knowledge is accounted Semantic Information, is not subject to Gettier-type counterexamples. A concluding section briefly summarises the results obtained.
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Semantic Information and the Correctness Theory of Truth
Erkenntnis, 2011Co-Authors: Luciano FloridiAbstract:Semantic Information is usually supposed to satisfy the veridicality thesis: p qualifies as Semantic Information only if p is true. However, what it means for Semantic Information to be true is often left implicit, with correspondentist interpretations representing the most popular, default option. The article develops an alternative approach, namely a correctness theory of truth (CTT) for Semantic Information. This is meant as a contribution not only to the philosophy of Information but also to the philosophical debate on the nature of truth. After the introduction, in Sect. 2 , Semantic Information is shown to be translatable into propositional Semantic Information ( i ). In Sect. 3 , i is polarised into a query ( Q ) and a result ( R ), qualified by a specific context, a level of abstraction and a purpose. This polarization is normalised in Sect. 4 , where [ Q + R ] is transformed into a Boolean question and its relative yes/no answer [ Q + A ]. This completes the reduction of the truth of i to the correctness of A . In Sects. 5 and 6 , it is argued that (1) A is the correct answer to Q if and only if (2) A correctly saturates Q by verifying and validating it (in the computer science’s sense of “verification” and “validation”); that (2) is the case if and only if (3) [ Q + A ] generates an adequate model ( m ) of the relevant system ( s ) identified by Q ; that (3) is the case if and only if (4) m is a proxy of s (in the computer science’s sense of “proxy”) and (5) proximal access to m commutes with the distal access to s (in the category theory’s sense of “commutation”); and that (5) is the case if and only if (6) reading/writing ( accessing , in the computer science’s technical sense of the term) m enables one to read/write (access) s . Sect. 7 provides some further clarifications about CTT, in the light of Semantic paradoxes. Section 8 draws a general conclusion about the nature of CTT as a theory for systems designers not just systems users. In the course of the article all technical expressions from computer science are explained.
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Is Semantic Information Meaningful Data
Philosophy and Phenomenological Research, 2005Co-Authors: Luciano FloridiAbstract:There is no consensus yet on the definition of Semantic Information. This paper contributes to the current debate by criticising and revising the Standard Definition of Semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute Semantic Information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of Semantic Information. SDI is incorrect because truth-values do not supervene on Semantic Information, and misInformation (that is, false Semantic Information) is not a type of Semantic Information, but pseudo-Information, that is not Semantic Information at all. This is shown by arguing that none of the reasons for interpreting misInformation as a type of Semantic Information is convincing, whilst there are compelling reasons to treat it as pseudo-Information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates some interesting areas of application of the revised definition.
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Outline of a Theory of Strongly Semantic Information
Minds and Machines, 2004Co-Authors: Luciano FloridiAbstract:This paper outlines a quantitative theory of strongly Semantic Information (TSSI) based on truth-values rather than probability distributions. The main hypothesis supported in the paper is that the classic quantitative theory of weakly Semantic Information (TWSI), based on probability distributions, assumes that truth-values supervene on factual Semantic Information, yet this principle is too weak and generates a well-known Semantic paradox, whereas TSSI, according to which factual Semantic Information encapsulates truth, can avoid the paradox and is more in line with the standard conception of what generally counts as Semantic Information. After a brief introduction, section two outlines the Semantic paradox implied by TWSI, analysing it in terms of an initial conflict between two requisites of a quantitative theory of Semantic Information. In section three, three criteria of Semantic Information equivalence are used to provide a taxonomy of quantitative approaches to Semantic Information and introduce TSSI. In section four, some further desiderata that should be fulfilled by a quantitative TSSI are explained. From section five to section seven, TSSI is developed on the basis of a calculus of truth-values and Semantic discrepancy with respect to a given situation. In section eight, it is shown how TSSI succeeds in solving the paradox. Section nine summarises the main results of the paper and indicates some future developments.
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From Data to Semantic Information
Entropy, 2003Co-Authors: Luciano FloridiAbstract:There is no consensus yet on the definition of Semantic Information. This paper contributes to the current debate by criticising and revising the Standard Definition of Semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute Semantic Information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of Semantic Information. SDI is incorrect because truth-values do not supervene on Semantic Information, and misInformation (that is, false Semantic Information) is not a type of Semantic Information, but pseudo-Information, that is not Semantic Information at all. This is shown by arguing that none of the reasons for interpreting misInformation as a type of Semantic Information is convincing, whilst there are compelling reasons to treat it as pseudo-Information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates the important implications of the revised definition for the analysis of the deflationary theories of truth, the standard definition of knowledge and the classic, quantitative theory of Semantic Information.
Josef Kittler - One of the best experts on this subject based on the ideXlab platform.
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Visual Semantic Information Pursuit: A Survey
IEEE transactions on pattern analysis and machine intelligence, 2019Co-Authors: Daqi Liu, Miroslaw Bober, Josef KittlerAbstract:Visual Semantic Information comprises two important parts: the meaning of each visual Semantic unit and the coherent visual Semantic relation conveyed by these visual Semantic units. Essentially, the former one is a visual perception task while the latter one corresponds to visual context reasoning. Remarkable advances in visual perception have been achieved due to the success of deep learning. In contrast, visual Semantic Information pursuit, a visual scene Semantic interpretation task combining visual perception and visual context reasoning, is still in its early stage. It is the core task of many different computer vision applications, such as object detection, visual Semantic segmentation, visual relationship detection or scene graph generation. Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual Semantic Information pursuit methods. Surprisingly, a comprehensive review for this exciting area is still lacking. In this survey, we present a unified theoretical paradigm for all these methods, followed by an overview of the major developments and the future trends in each potential direction. The common benchmark datasets, the evaluation metrics and the comparisons of the corresponding methods are also introduced.
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Visual Semantic Information Pursuit: A Survey.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Daqi Liu, Miroslaw Bober, Josef KittlerAbstract:Visual Semantic Information comprises two important parts: the meaning of each visual Semantic unit and the coherent visual Semantic relation conveyed by these visual Semantic units. Essentially, the former one is a visual perception task while the latter one corresponds to visual context reasoning. Remarkable advances in visual perception have been achieved due to the success of deep learning. In contrast, visual Semantic Information pursuit, a visual scene Semantic interpretation task combining visual perception and visual context reasoning, is still in its early stage. It is the core task of many different computer vision applications, such as object detection, visual Semantic segmentation, visual relationship detection or scene graph generation. Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual Semantic Information pursuit methods. However, a comprehensive review for this exciting area is still lacking. In this survey, we present a unified theoretical paradigm for all these methods, followed by an overview of the major developments and the future trends in each potential direction. The common benchmark datasets, the evaluation metrics and the comparisons of the corresponding methods are also introduced.
Pinar Senkul - One of the best experts on this subject based on the ideXlab platform.
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Improving pattern quality in web usage mining by using Semantic Information
Knowledge and Information Systems, 2012Co-Authors: Pinar Senkul, Suleyman SalinAbstract:Frequent Web navigation patterns generated by using Web usage mining techniques provide valuable Information for several applications such as Web site restructuring and recommendation. In conventional Web usage mining, Semantic Information of the Web page content does not take part in the pattern generation process. In this work, we investigate the effect of Semantic Information on the patterns generated for Web usage mining in the form of frequent sequences. To this aim, we developed a technique and a framework for integrating Semantic Information into Web navigation pattern generation process, where frequent navigational patterns are composed of ontology instances instead of Web page addresses. The quality of the generated patterns is measured through an evaluation mechanism involving Web page recommendation. Experimental results show that more accurate recommendations can be obtained by including Semantic Information in navigation pattern generation, which indicates the increase in pattern quality.
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ISCIS - Using Semantic Information for web usage mining based recommendation
2009 24th International Symposium on Computer and Information Sciences, 2009Co-Authors: Suleyman Salin, Pinar SenkulAbstract:Web usage mining has become popular in various business areas related with Web site development. In Web usage mining, commonly visited navigational paths are extracted in terms of Web page addresses from the Web server visit logs, and the patterns are used in various applications including recommendation. The Semantic Information of the Web page contents is generally not included in Web usage mining. In this work, a framework for integrating Semantic Information with Web usage mining is presented. The frequent navigational patterns are extracted in the form of ontology instances instead of Web page addresses and the result is used for generating Web page recommendations to the visitor. In addition, an evaluation mechanism is implemented in order to test the success of the recommendation. Test results show that more accurate recommendations can be obtained by including Semantic Information in the Web usage mining.
Massimo Girelli - One of the best experts on this subject based on the ideXlab platform.
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Interhemispheric transfer of spatial and Semantic Information: electrophysiological evidence.
Psychophysiology, 2013Co-Authors: Anna Dal Molin, Carlo Alberto Marzi, Marie T. Banich, Massimo GirelliAbstract:The goal of this study was to cast light on the existence of functional callosal channels for the interhemispheric transfer (IHT) of spatial and Semantic Information. To do so, we recorded event-related potentials in healthy humans while performing a primed odd-even discrimination task. Targets were visually presented numbers preceded by single-letter primes signaling the probable presentation of an odd or an even number. Primes and targets could appear either in the same or in different visual fields, thus requiring an IHT in the latter case. The P1 and N2 components were influenced by IHT of spatial Information only, whereas the later N400 was influenced by IHT of both spatial and Semantic Information. This was not the case for the P3b, which was modulated by Semantic validity only. These results provide novel evidence of the existence of a temporally separated interhemispheric exchange of spatial and Semantic Information.
Daqi Liu - One of the best experts on this subject based on the ideXlab platform.
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Visual Semantic Information Pursuit: A Survey
IEEE transactions on pattern analysis and machine intelligence, 2019Co-Authors: Daqi Liu, Miroslaw Bober, Josef KittlerAbstract:Visual Semantic Information comprises two important parts: the meaning of each visual Semantic unit and the coherent visual Semantic relation conveyed by these visual Semantic units. Essentially, the former one is a visual perception task while the latter one corresponds to visual context reasoning. Remarkable advances in visual perception have been achieved due to the success of deep learning. In contrast, visual Semantic Information pursuit, a visual scene Semantic interpretation task combining visual perception and visual context reasoning, is still in its early stage. It is the core task of many different computer vision applications, such as object detection, visual Semantic segmentation, visual relationship detection or scene graph generation. Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual Semantic Information pursuit methods. Surprisingly, a comprehensive review for this exciting area is still lacking. In this survey, we present a unified theoretical paradigm for all these methods, followed by an overview of the major developments and the future trends in each potential direction. The common benchmark datasets, the evaluation metrics and the comparisons of the corresponding methods are also introduced.
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Visual Semantic Information Pursuit: A Survey.
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Daqi Liu, Miroslaw Bober, Josef KittlerAbstract:Visual Semantic Information comprises two important parts: the meaning of each visual Semantic unit and the coherent visual Semantic relation conveyed by these visual Semantic units. Essentially, the former one is a visual perception task while the latter one corresponds to visual context reasoning. Remarkable advances in visual perception have been achieved due to the success of deep learning. In contrast, visual Semantic Information pursuit, a visual scene Semantic interpretation task combining visual perception and visual context reasoning, is still in its early stage. It is the core task of many different computer vision applications, such as object detection, visual Semantic segmentation, visual relationship detection or scene graph generation. Since it helps to enhance the accuracy and the consistency of the resulting interpretation, visual context reasoning is often incorporated with visual perception in current deep end-to-end visual Semantic Information pursuit methods. However, a comprehensive review for this exciting area is still lacking. In this survey, we present a unified theoretical paradigm for all these methods, followed by an overview of the major developments and the future trends in each potential direction. The common benchmark datasets, the evaluation metrics and the comparisons of the corresponding methods are also introduced.
