Semantic Computing and AI

Researches about semantic computing are usually evaluated by experiments, lacking support from the theory of computation. But traditional finite automata are defined on a fixed finite set of states. They will be invalid when a different application needs to change the states. However, semantic computing always asks for more robust automata that can modify itself according to the changes of states. Therefore, in this paper, we redefine traditional automata in more robust forms by semantic computing. The intuitive idea is when the state which an automaton is supposed to enter has changed, we drive automata into a state that is similar to the original one. We can find another similar state in the original state set to replace the changed state by semantic similarity. Based on the theories of semantic similarity, there always exists a similar state. Therefore, once a state has changed, we can always find a similar state to replace it, which means that we can empower automata to adapt to the changes of states. Our new automata bridge the gap between semantic computing and the theory of computation. Furthermore, we also redefine fuzzy finite automata and pushdown automata in more robust forms. Finally, we provide an application about the weather forecast, which indicates how to overcome the limitations of traditional automata.

There is increasing awareness within enterprises of the many opportunities becoming available through adoption of emerging technologies and tools that utilize machine intelligence. A transition from reliance on conventional technologies and legacy data stores to more knowledge oriented semantic computing capabilities that make use of natural language processing and text analytics, video analytics, ontologies, reasoners, machine learning, Big Data and graph data stores is beginning to occur. Does this signal the beginning of a major shift in enterprise computing? What are some of these evolving trends and business opportunities? What are some of the major enterprise pain points that semantic computing can help to mitigate? How can enterprises best position themselves for successful deployment of these technologies to maximize business value? How is the financial industry embracing semantic computing?

Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.

This research proposes the multispectral image retrieval method by using spectral feature and semantic computing which is not many studies have focused. The main contributions are to enhance the effectiveness and advantageous of global environmental analysis system and realize semantic associative search and analysis. In this work, we study multispectral image retrieval using spectral feature computed in multispectral semantic-image space. The multispectral semantic-image space is supposing to realize the interpretation of substance (materials) on earth surface which can be provided the analyzed results as human-level interpretation. Our essential approach is utilizing the semantic computing to measure the similarity between multispectral image and the meaningful keywords which according to the user's contexts. Our research results found that this method possible to acquire the spectral feature from the multispectral image and could be used in multispectral image retrieval. In this study, a multispectral image is used as the image query according to user's query contexts. Moreover, the method performance of UAV-based multispectral aerial image retrieval using spectral feature and semantic computing is measured based on the queries with three contexts of multispectral image which is indicated by previous study on agricultural monitoring system and semantic interpretation model.

Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new semantic computing method with multi-spectrum images for analyzing and interpreting environmental phenomena and changes occurring in the physical world.

As the proposition of the next-generation Web – semantic Web, semantic computing has been drawing more and more attention within the circle and the industries. A lot of research has been conducted on the theory and methodology of the subject, and potential applications have also been investigated and proposed in many fields. The progress of semantic computing made so far cannot be detached from its supporting pivot – language resources, for instance, language knowledge bases. This paper proposes three perspectives of semantic computing from a macro view and describes the current status of affairs about the construction of language knowledge bases and the related research and applications that have been carried out on the basis of these resources via a case study in the Institute of Computational Linguistics at Peking University.

In this article, we survey different types of big data techniques such as Hadoop, NoSQL, and R and how semantic computing can be utilized to improve these methods. We explain the terms big data and semantic analysis. We discuss how big data analysis and semantic analysis are utilized in different domains such as healthcare and business.

Pebble games are a powerful tool in the study of finite model theory, constraint satisfaction and database theory. Monads and comonads are basic notions of category theory which are widely used in semantics of computation and in modern functional programming. We show that existential k-pebble games have a natural comonadic formulation. Winning strategies for Duplicator in the k-pebble game for structures A and B are equivalent to morphisms from A to B in the coKleisli category for this comonad. This leads on to comonadic characterisations of a number of central concepts in Finite Model Theory: - Isomorphism in the co-Kleisli category characterises elementary equivalence in the k-variable logic with counting quantifiers. - Symmetric games corresponding to equivalence in full k-variable logic are also characterized. - The treewidth of a structure A is characterised in terms of its coalgebra number: the least k for which there is a coalgebra structure on A for the k-pebbling comonad. - Co-Kleisli morphisms are used to characterize strong consistency, and to give an account of a Cai-F\"urer-Immerman construction. - The k-pebbling comonad is also used to give semantics to a novel modal operator. These results lay the basis for some new and promising connections between two areas within logic in computer science which have largely been disjoint: (1) finite and algorithmic model theory, and (2) semantics and categorical structures of computation.

Domain words clustering have important theoretical and practical significance in text categorization, the ontology research, machine learning and many other research areas. The domain words clustering method in this article is a method based on word2vec and semantic similarity computation. First of all, we get the candidate word set with word2vec tools to preliminary clustering of words. Then we tectonic domain category semantic core word set and screening candidate word set by means of semantic similarity computation. Finally we get new word set belongs to the target domain and get the word set in the field of clustering. Experiments show that this method has higher recall ratio and accuracy.

Deforestation is still a major nature phenomenon in our society. For assessing deforestation effect, satellites remote sensing provides a fundamental data for observation. While new remote-sensing technologies are able to represent high-resolution forest mapping, the application is still limited only for detecting and mapping the deforestation area. In this paper, we proposed a new method for automatically extract features of Satellite Multispectral images for interpreting deforestation effect in the context of soil degradation. We proposed an idea to interpret reflected “substances (material)” of bare soil in deforested area in spectrum domain into human language. The objectives of this paper are to (1) recognize the deforestation activity automatically. (2) Identify deforestation causes and examines the deforestation effect based on deforestation causes. (3) Scrutinize deforestation effects on soil degradation. (4) Representing nature knowledge of deforestation effect in human language using semantic computing, to bring the clear, comprehensible knowledge even for people who are not familiar with forestry. As for the experimental study, Riau Tropical Forest has been selected as the study area, where the multispectral data was acquired by using Landsat 8 Satellite between 2013 and 2014; Where forest fire and logging activities are reported and detected.

The key note of the following remarks will be the distinction between two kinds of attitudes towards the relations between syntax and semantics, which I shall call without any claim to historical accuracy the Fregean and the Russellian attitudes, respectively. My main point will be that as far as the formal analysis of natural languages is concerned, the trend in recent years has been contrary to what Hintikka would have us believe towards more Fregean approaches. By naming them thus I by no means want to imply that these two important precursors of contemporary research in the philosophy of language and linguistic semantics would agree with most of what I will attribute to them. The distinction I want to draw does, however, cut across many of the issues dealt with in Hintikka's paper; to some of these I will turn shortly. But I would first like to motivate the distinction and to argue briefly for one of the two. The Fregean and Russellian attitudes are essentially alternatives to ways of considering the relation between syntax and semantics within one's theory of language. The Fregean attitude consists in regarding the relation between entities from the two domains as roughly one-to-one. What it basically entails is that the syntactic decomposition of the expressions of the language must enter directly into the semantic computation. A Fregean analysis is typically surface oriented and forsees in general for each syntactic category of the language (or rather of the syntax) a corresponding semantic category. On the other hand, what I am calling the Russellian attitude towards the relation between syntax and semantics is based on the view that the direct surface structure of a language (here I am of course referring to natural languages) is not a faithful guide to its semantic structure, or if you want, to its logical form. Typically, a Russellian attitude will lead to a kind of "syntactic transformationalism", best known, for example, from the kind of work done within the various trends of generative trans formational grammar (where the motivation for transformational analyses was of course not explicitly semantic). One could cite among

The novel networking paradigm of the Internet of Things (IoT) represents a promising solution to address the challenges facing modern societies. All-pervading smart technologies in the IoT paradigm have facilitated collection of gigantic amounts of personal and organisational data. Effective management of smart city data is critical in providing smart applications to end users in a reliable and secure manner. Semantic computing provides a range of technologies that can aptly address big data concerns of smart city. In this paper we propose a smart city architecture that harnesses the power of semantic computing to realise the smart city vision.

Semantics is grounded on intellectual activities in various communities in the human society. Conversations and stories are primary media for substantiating the semantic processes in a community. In this paper, I model a semantic process as a coevolutionary spiral of conversations and stories in a community, and present a simple but practical computational model featuring knowledge cards that can serve as a semantic component, lifecycle support of knowledge cards, and strategic control of information streams. Then, I generalize the approach as a technique called conversation quantization, which is based on the idea of approximating a continuous flow of conversation by a series of conversation quanta that represent points of the discourse. I show some implemented systems to show how these ideas are implemented.

The focus of this introduction to this special issue is to draw a picture as comprehensive as possible about various dimensions of inconsistency. In particular, we consider: (1) levels of knowledge at which inconsistency occurs; (2) categories and morphologies of inconsistency; (3) causes of inconsistency; (4) circumstances of inconsistency; (5) persistency of inconsistency; (6) consequences of inconsistency; (7) metrics for inconsistency; (8) theories for handling inconsistency; (9) dependencies among occurrences of inconsistency; and (10) problem domains where inconsistency has been studied. The take-home message is that inconsistency is ubiquitous and handling inconsistency is consequential in our endeavors. How to manage and reason in the presence of inconsistency presents a very important issue in semantic computing, cloud computing, social computing, and many other data-rich or knowledge-rich computing systems.

Constructing a bidirectional transformation between BPMN and BPEL with a functional logic programming language