**6. References**

14 Semantics – Advances in Theories and Mathematical Models

information extraction from the input data, followed by a process of input physical information association with physical information stored at the lowest level of a semantic hierarchy. In this way, input physical information becomes named with an appropriate linguistic label and framed into a suitable linguistic phrase (and further – in a story, a tale, a

In this chapter I have proposed a new definition of information (as a description, a linguistic text, a piece of a story or a tale) and a clear segregation between two different types of information – physical and semantic information. I hope, I have clearly explained the (usually obscured and mysterious) interrelations between data and physical information as well as the relations between physical information and semantic information. Consequently, usually indefinable notions of "knowledge", "memory" and "learning" have also received

Traditionally, semantics is seen as a feature of human language communication praxis. However, the explosive growth of communication technologies (different from the original language-based communication) has led to an enormous diversification of matters which are being communicated today – audio and visual content, scientific and commercial, military and medical health care information. All of them certainly bear their own non-linguistic semantics (Pratikakis et al, 2011). Therefore, attempts to explain and to clarify these new forms of semantics are permanently undertaken, aimed to develop tools and services which would enable to handle this communication traffic in a reasonable and meaningful manner. In the reference list I provide some examples of such undertakings: "The Semantics of Semantics" (Petrie, 2009), "Semantics of the Semantic Web" (Sheth et al, 2005), "Geospatial Semantics" (Di

What is common to all those attempts is that notions of data, information, knowledge and semantics are interchanged and swapped generously, without any second thought about what implications might follow from that. In this regard, even a special notion of Data Semantics was introduced (Sheth, 1995) and European Commission and DARPA are pushing research programs aimed on extracting meaning and purpose from bursts of sensor data (Examples of such research roadmaps could be found in my last presentation at The 3-rd Israeli Conference on Robotics, November 2010, available at my website http://www.vidia-mant.info). However, as my present definition claims – data and information are not interchangeable, physical information is not a substitute for semantic information, and data is semantics devoid

Contrary to the widespread praxis (Zins, 2007), I have defined semantics as a special kind of information. Revitalizing the ideas of Bar-Hillel and Carnap (Bar-Hillel & Carnap, 1952) I have recreated and re-established (on totally new grounds) the notion of semantics as the

Considering the crucial role that information usage, search for, exchange and exploration have gained in our society, I dare to think that clarifying the notion of semantic information will illuminate many shady paths which Semantic Web designers and promoters are forced to take today, deprived from a proper understanding of semantic information peculiarities. As a result, information processing principles are substituted by data processing tenets

Donato, 2010), "Semantics in the Semantic Web" (Almeida et al, 2011).

(semantics is a property of a human observer, not a property of the data).

narrative), which provides the desired meaning for the input physical information.

**5. Conclusions** 

their suitable illumination and explanation.

notion of Semantic Information.

Adams, F. (2003). The Informational Turn in Philosophy, Minds and Machines 13: 471–501, 2003. Available from

 http://www.arquitetura.eesc.usp.br/laboratorios/lei/sap5865/2007/leituras/Min d\_and\_machines.pdf


http://www.survivor99.com/lcg/information/CARNAP-HILLEL.pdf

Beaver, D. & Frazee, J. (2011). Semantics, In: *The Handbook of Computational Linguistics*, Ruslan Mitkov (ed.), Available from

http://semanticsarchive.net/Archive/DAzYmYzO/semantics\_oup.pdf


**1. Introduction**

knowledge logic.

The study of knowledge and belief has a long tradition in philosophy. An early treatment of a formal logical analysis of reasoning about knowledge and belief came from Hintikka's work [15]. More recently, researchers in such diverse fields as economics, linguistics, artificial intelligence and theoretical computer science have become increasingly interested in reasoning about knowledge and belief [1–5, 10–13, 18, 20, 24]. In wide areas of application of reasoning about knowledge and belief, it is necessary to reason about uncertain information. Therefore the representation and reasoning of probabilistic information in belief is important. There has been a lot of works in the literatures related to the representation and reasoning of probabilistic information, such as evidence theory [25], probabilistic logic [4], probabilistic dynamic logic [7], probabilistic nonmonotonic logic [21], probabilistic knowledge logic [3] and etc. A distinguished work is done by Fagin and Halpern [3], in which a probabilistic knowledge logic is proposed. It expanded the language of knowledge logic by adding formulas like "*wi*(*ϕ*) ≥ 2*wi*(*ψ*)" and "*wi*(*ϕ*) < 1/3", where *ϕ* and *ψ* are arbitrary formulas. These formulas mean "*ϕ* is at least twice probable as *ψ*" and "*ϕ* has probability less than 1/3". The typical formulas of their logic are "*a*1*wi*(*ϕ*1) + ... <sup>+</sup> *akwi*(*ϕk*) <sup>≥</sup> *<sup>b</sup>*", "*Ki*(*ϕ*)" and "*K<sup>b</sup>*

**Probabilistic Belief Logics for Uncertain Agents** 

*1Department of Computer Science and Technology, Nanjing Universityof Aero. & Astro.,* 

*2Provincial Key Laboratory for Computer Information Processing Technology,* 

the latter formula is an abbreviation of "*Ki*(*wi*(*ϕ*) ≥ *b*)". Here formulas may contain nested occurrences of the modal operators *wi* and *Ki*, and the formulas in [4] do not contain nested occurrences of the modal operators *wi*. On the basis of knowledge logic, they added axioms of reasoning about linear inequalities and probabilities. To provide semantics for such logic, Fagin and Halpern introduced a probability space on Kripke models of knowledge logic, and gave some conditions about probability space, such as *OBJ*, *SDP* and *UNIF*. At last, Fagin and Halpern concluded by proving the soundness and completeness of their probabilistic

Fagin and Halpern's work on probabilistic epistemic logic is well-known and original. However, there are several aspects worth further investigation: First, the completeness proof of Fagin and Halpern can only deal with the finite set of formulas for that their method reduces the completeness to the existence of a solution of a set of finitely many linear inequalities. In the case of an infinite set of formulas, their method reduces the problem to the existence of a solution of infinitely many linear inequalities with infinitely many variables, which does not seem to be captured by the axioms in [3] for their language only contains finite-length

*<sup>i</sup>* (*ϕ*)",

Zining Cao1,2

*Soochow University, Suzhou* 

*Nanjing* 

**2**

*P. R. China* 

http://www.illc.uva.nl/~seop/entries/information-semantic/

Fodor, J. (2007). Semantics: an interview with Jerry Fodor. *ReVEL.* Vol. 5, No. 8, 2007. Available from

 http://www.revel.inf.br/site2007/\_pdf/8/entrevistas/revel\_8\_interview\_jerry\_fo dor.pdf


http://www-cdr.stanford.edu/~petrie/online/peer2peer/semantics.pdf

