Knowledge Representation Technologies Research Articles

Differential Analysis of Stylistic Features in Chinese-English Interpretation Based on Natural Language Processing

Abstract In this paper, after researching the natural language processing model, we use the knowledge representation technology to construct a knowledge base and use the sequence comparison algorithm to compare the sequences of Chinese and English translations, analyze the similarities and differences between the two sequences, and analyze the entropy of information between English and Chinese translations through the probabilistic theory and mathematical statistics method. On this basis, we analyze the differences between Chinese and English translations in terms of lexical dimension, syntactic dimension and articulation dimension and explore the differences in lexical richness and thematic concentration between Chinese and English based on “entropy”. The results show that Chinese is significantly higher than English translations in terms of language output length, sentence complexity, subordinate structure and phrase structure (p<0.001), and Chinese is significantly higher than English translations in terms of articulation (p<0.05). This paper provides an important reference value for studying the differences in stylistic features in Chinese-English translation.

A comprehensive survey of personal knowledge graphs

AbstractInformation that can encapsulate a person's daily life and its different aspects provides insightful knowledge. This knowledge can prove to be more useful than general knowledge for improving personalized tasks. When it comes to storing such knowledge, personal knowledge graphs (PKGs) come in as handy saviors. PKGs are knowledge graphs which store details that are pertinent to a user but not, in general, useful to the rest of humanity. Conversational agents can access these PKGs to answer queries related to the user's day‐to‐day life, whereas recommender systems can harness the knowledge stored in PKGs to make personalized suggestions. Despite the immense applicability of PKGs, there has not been significant research in this area. We present an extensive review of PKGs. We categorize them according to the domains in which they are most relevant; in particular, we highlight the use of PKGs in medicine, finance, and education and research. We also categorize the different ways of constructing a PKG based on the source of data required for such constructions. Furthermore, we discuss the limitations of PKGs and suggest directions for future work.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Artificial Intelligence

Towards a Computational Ontology for the Philosophy of Wittgenstein: Representing Aspects of the Tractarian Philosophy of Mathematics

The present paper concerns the Wittgenstein ontology project: an attempt to create a Semantic Web representation of Ludwig Wittgenstein’s philosophy. The project has been in development since 2006, and its current state enables users to search for information about Wittgenstein-related documents and the documents themselves. However, the developers have much more ambitious goals: they attempt to provide a philosophical subject matter knowledge base that would comprise the claims and concepts formulated by the philosopher. The current knowledge representation technology is not well-suited for this task, and a non-standard approach is required. The creators of the Wittgenstein ontology project are aware of this fact; recently, they have been discussing conceptual devices adjusting the technology to their needs. The main goal of this paper is to present examples of a representation of philosophical content that make use of both the devices already proposed and some new inventions. The represented content comes from the Tractatus Logico-Philosophicus; more specifically, its theses concerning the problems in philosophy of mathematics.

Generative Design Methodology and Framework Exploiting Designer-Algorithm Synergies

Designing is a problem-solving activity. The process is usually iterative: a solution is proposed, then analysed and tested until it satisfies all constraints and best fulfils the criteria. Usually, a designer proposes a solution based on intuition, experience, and knowledge. However, this does not work for problems they are facing for the first time. An alternative approach is generative design, where the designer focuses on iteratively defining a problem with its constraints and criteria in the form of a parametric computational model, and then leaves the search for the solution to the algorithms and their ability to rapidly generate and test several alternatives. The result of this approach is not only a set of solutions embedding implicitly the knowledge but also a model where problem-defining knowledge is quite explicit. The idea of the proposed approach is the exploitation of synergies between the designer and the algorithms. The designer focuses on problem definition and the algorithm focuses on finding a solution, showing that the capacity of the generative approach to replace the designer is limited. In the paper, we first present the framework of generative design, then apply the process to a case study of designing an efficient shading solution, and in the end, we present the results and compare them with the traditional approach. The approach is general and can be applied in other areas of engineering. It is relevant both to designers as well as software developers who are expected to take this approach further. More theoretical work is needed to study problem definitions as a form of knowledge representation in engineering.

Fact-condition statements and super relation extraction for geothermic knowledge graphs construction

Researchers utilize information from the geoscience literature to deduce the regional or global geological evolution. Traditionally this process has relied on the labor of researchers. As the number of papers continues to increase, acquiring domain-specific knowledge becomes a heavy burden. Knowledge Graph (KG) is proposed as a new knowledge representation technology to change this situation. However, the super relation is not considered in the previous KG, which bridges the geological phenomenon (fact) and its precondition (condition). For instance, in the statement (“the late Archean was a crucial transition period in the history of global geodynamics”), the condition statement (“crucial transition for global geodynamics”) works as the complementary fact statement (“the late Archean was a crucial transition period”), which defines the scale of crucial transition accurately in the late Archean. In this study, fact-condition statement extraction is introduced to construct a geological knowledge graph. A rule-based multi-input multi-output model (R-MIMO) is proposed for information extraction. In the R-MIMO, fact-condition statements and their super relation are considered and extracted for the first time. To verify its performances, a GeothCF dataset with 1455 fact tuples and 789 condition tuples is constructed. In experiments, the R-MIMO model achieves the best performance by using BERT as encoder and LSTM-d as decoder, achieving F1 80.24% in tuple extraction and F1 70.03% in tag prediction task. Furthermore, the geothermic KG with super relation is automatically constructed for the first time by trained R-MIMO, which can provide structured data for further geothermic research.

A comprehensive review on updating concept lattices and its application in updating association rules

AbstractFormal concept analysis (FCA) visualizes formal concepts in terms of a concept lattice. Usually, it is an NP‐problem and consumes plenty of time and storage space to update the changes of the lattice. Thus, introducing an efficient way to update and maintain such lattices is a significant area of interest within the field of FCA and its applications. One of those vital FCA applications is the association rule mining (ARM), which aims at generating a loss‐less nonredundant compact Association Rule‐basis (AR‐basis). Currently, the real‐world data rapidly overgrow that asks the need for updating the existing concept lattice and AR‐basis upon data change continually. Intuitively, updating and maintaining an existing concept‐lattice or AR‐basis is much more efficient and consistent than reconstructing them from scratch, particularly in the case of massive data. So far, the area of updating both concept lattice and AR‐basis has not received much attention. Besides, few noncomprehensive studies have focused only on updating the concept lattice. From this point, this article comprehensively introduces basic knowledge regarding updating both concept lattices and AR‐basis with new illustrations, formalization, and examples. Also, the article reviews and compares recent remarkable works and explores the emerging future research trends.This article is categorized under: Algorithmic Development > Association Rules Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Association Rules

A Parametric Model of Umbilical Cable with Siemens NX considering its Reliability

Abstract Umbilical cable is one of the key control equipment, for example, in the subsea oil and gas production system. That can be seen as a customized product related to specific parameters of use cases, e.g. installation site. In this paper, we first apply the calculation method for the reliability of the umbilical cable by advanced first-order second-moment method (AFOSM) and Monte Carlo method. Secondly, we demonstrate the use of Siemens NX and its framework for engineering knowledge representation called Knowledge Fusion (KF) to generate the parametric model of the umbilical cable design considering its reliability. Finally, we reveal the advantages of introducing a Knowledge-based Engineering (KBE) approach to integrate the CAD models extended with automated calculations for product reliability.

Enhanced semantic representation of coaxiality with double material requirements

SummaryIn order to improve the assemblability and reduce the cost of the parts, the material requirements can be applied to the toleranced feature and the datum feature of the coaxiality at the same time in the practical application. How to realize the information exchange between different heterogeneous systems and how to read and explain the semantics of this complex tolerance specification automatically is one of the urgent problems to be solved in intelligent digital manufacturing. Ontology described in OWL2 DL and SWRL language has strict mathematical basis of description logic, and the knowledge represented by it has clear semantics and can be read and understood directly by computer. In addition, because of its consistency checking, knowledge reasoning, and flexible query ability, it becomes the most potential knowledge representation technology in the future. On the other hand, the semantic interpretation of tolerance specification by the new generation GPS theory based on skin model has the advantages of clear semantics, no ambiguity, and consistency at different stages of the product life cycle. In this article, the enhanced semantic representation ontology for this kind of coaxiality tolerance specification is constructed based on the advantages of the new GPS theory and ontology technology and the effectiveness of the method is verified and analyzed through a case.

Survey of Knowledge Graph Approaches and Applications

With the advent of the era of big data, knowledge engineering has received extensive attention. How to extract useful knowledge from massive data is the key to big data analysis. Knowledge graph technology is an important part of artificial intelligence, which provides a method to extract structured knowledge from massive texts and images, and has broad application prospects. The knowledge base with semantic processing capability and open interconnection ability can be used to generate application value in intelligent information services such as intelligent search, intelligent question answering and personalized recommendation. Although knowledge graph has been applied to various systems, the basic theory and application technology still need further research. On the basis of comprehensively expounding the definition and architecture of knowledge graph, this paper reviews the key technologies of knowledge graph construction, including the research progress of four core technologies such as knowledge extraction technology, knowledge representation technology, knowledge fusion technology and knowledge reasoning technology, as well as some typical applications. Finally, the future development direction and challenges of the knowledge graph are prospected.

Object-oriented approach to product design using extended NX Open API

Knowledge-based engineering (KBE) helps to automate the design of products and corresponding manufacturing systems. The automation is achieved via engineering knowledge representation in computer-processable formats, i.e. languages. It becomes possible to capture product design and manufacturing processes knowledge and let software applications to handle it suggesting better options for a product and/or a manufacturing system. In this paper we demonstrate an application and corresponding methodology to design and evaluate the performance of a product. The approach can be extended to different engineering problem domains saving the time used for product design.

Knowledge representation and intelligent fault diagnosis technology for power grid faults

Aiming at that the automatic fault diagnosis method is difficult to locate the fault causes under uncertain circumstances which include malfunctions of the equipment and wrong alarm messages, a knowledge model was proposed to describe the relationship, status and operation of the equipment. And based on the model, the action logic between equipment after accidents is expressed in the form of rules combined with predicate logic. The corresponding interpretation and checking results of the relative alarm messages are given by reasoning the accident chain under different fault hypothesis. And the optimal judgment result is obtained through the calculation of prior probability. The validity of the method is verified by a practical fault case.

Enabling smart environments through scalable policy reasoning and Internet of Things

In this paper, we discuss how to combine ontology‐based policy reasoning mechanisms with in‐use Internet of Things applications to customize and automate device behaviors. We discuss how the policy framework can be extended with data federation to handle diverse and distributed data sources. We demonstrate that smart devices and sensors can be orchestrated through policies in diverse settings, from smart home environments to hazardous workplaces, such as coal mines. Lastly, we evaluate our approach using real applications with real data and demonstrate that it is scalable under high load of data and devices.This article is categorized under: Technologies > Computational Intelligence Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence

Expression of Concern: Facial feature discovery for ethnicity recognition

The salient facial feature discovery is one of the important research tasks in ethnical group face recognition. In this paper, we first construct an ethnical group face dataset including Chinese Uyghur, Tibetan, and Korean. Then, we show that the effective sparse sensing approach to general face recognition is not working anymore for ethnical group facial recognition if the features based on whole face image are used. This is partially due to a fact that each ethnical group may have its own characteristics manifesting only in specified face regions. Therefore, we will analyze the particularity of three ethnical groups and aim to find the common characterizations in some local regions for the three ethnical groups. For this purpose, we first use the facial landmark detector STASM to find some important landmarks in a face image, then, we use the well‐known data mining technique, the mRMR algorithm, to select the salient geometric length features based on all possible lines connected by any two landmarks. Second, based on these selected salient features, we construct three “T” regions in a face image for ethnical feature representation and prove them to be effective areas for ethnicity recognition. Finally, some extensive experiments are conducted and the results reveal that the proposed “T” regions with extracted features are quite effective for ethnical group facial recognition when the L2‐norm is adopted using the sparse sensing approach. In comparison to face recognition, the proposed three “T” regions are evaluated on the olivetti research laboratory face dataset, and the results show that the constructed “T” regions for ethnicity recognition are not suitable for general face recognition.This article is categorized under: Algorithmic Development > Structure Discovery Algorithmic Development > Biological Data Mining Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Classification

Knowledge discovery for enabling smart Internet of Things: A survey

The use of knowledge discovery on the Internet of Things (IoT) and its allied domains is undeniably one of the most indispensable ones, which results in optimized placement architectures, efficient routing protocols, device energy savings, and enhanced security measures for the implementation. The absence of knowledge discovery in IoT results in just an implementation of large‐scale sensor networks, which generates a huge amount of data, and which needs, an often under‐optimized, processing for actionable outputs. In this survey, we explore various domains of IoT for which knowledge discovery is inseparable from the application, and show how it benefits the overall implementation of the IoT architecture.This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Machine Learning Application Areas > Internet and Web‐Based Applications Algorithmic Development > Spatial and Temporal Data Mining

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction

Constitutive metaphor and mental mappings: meaning construction in the language of science and technology

The study of scientific and technical language, where metaphor is central, is enriched by the cognitive linguistics approach. This paper is based on the research project that culminated in the Bilingual Dictionary of Scientific and Technical Metaphors and Metonymies, developed to depict metaphorical terms and mental mappings, thus unfolding cognitive metaphors in science and technology. Although terminological metaphors as a whole exceed widely in number those that constitute part of conceptual metaphors, the importance of the latter radicates in its relevance as a constitutive element of scientific thought and language. Focusing on the analysis of metaphorical terms from earth sciences, agronomy, and mechanical engineering, the study reveals the presence of several conceptual metaphors, typified as ‘humanizing’, ‘organicist’, and ‘objectual’, according to their source domain’s nature. The work presents some very productive conceptual metaphorical patterns found in knowledge representation in engineering, in English and in Spanish, and shows evidence that metaphorical reasoning is a mechanism present at the core of creative scientific development albeit certain socio-cultural variations. This contribution opens a door for further research on the role of metaphor in constructing meaning within all branches of science and technology, as well as on the study of knowledge representation variations in different languages and cultures.

Marketing Decision Support Using Artificial Intelligence and Knowledge Modeling: Application to Tourist Destination Management

Abstract Knowledge-based information systems are advanced tools in the hands of the marketer, enabling him to take evidence-based decisions in complex situations. In this paper, advanced data analysis, neural networks and knowledge representation technologies are brought together towards an intelligent information system for tourist destination marketing. In previous work, knowledge engineering methods were proposed for the extraction and modeling from market survey data of factors, associations, clusters and hidden patterns that explain a market phenomenon or customer behavior. The feasibility of managing these findings in a Knowledge-Base, as reusable, sharable and machine understandable knowledge was shown using preliminary results from primary surveys on the tourism of Thessaloniki. In the current work, we present the continuation of these developments, including: (a) the final results of the survey on the tourism of Thessaloniki, (b) a refined Knowledge Base filled with real and validated content derived from the analysis of the full-scale survey data, (c) the extension of the methods with an artificial neural network classifier and (d) the deployment of an inference engine and a query mechanism in order to exercise the knowledge content for decision support. Pilot trials showed that the intelligent system was able to assist users who are not experts in analysis to solve typical destination marketing problems.

Symbolic Data Analysis: another look at the interaction of Data Mining and Statistics

Symbolic Data Analysis (SDA) provides a framework for the representation and analysis of data that comprehends inherent variability. While in Data Mining and classical Statistics the data to be analyzed usually presents one single value for each variable, that is no longer the case when the entities under analysis are not single elements, but groups gathered on the basis of some given criteria. Then, for each variable, variability inherent to each group should be taken into account. Also, when analysing concepts, such as botanic species, disease descriptions, car models, and so on, data entail intrinsic variability, which should be explicitly considered. To this purpose, new variable types have been introduced, whose realizations are not single real values or categories, but sets, intervals, or, more generally, distributions over a given domain. SDA provides methods for the (multivariate) analysis of such data, where the variability expressed in the data representation is taken into account, using various approaches.This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Structure Discovery and Clustering

Construction of Geological Knowledge-based Systems of Railway Route Selection

According to the diversity of geological conditions involved in railway location design, different types of geological objects were classified and indexed, and proposed the modeling method based on multiple sub-bases. It used the computer's internal and external representation in geological knowledge representation. In computer external, it used object-oriented class-rules knowledge representation model, added credibility to the object-oriented knowledge representation model, and then achieved fuzzy representation of knowledge. In order to achieve visual representation of external knowledge representation, text, images and three-dimensional virtual reality technology were used. According to the ambiguity of geological knowledge, it realized uncertainty reasoning of the part based on rule condition and conclusions.

Semantic Interoperability Architecture for Pervasive Computing and Internet of Things

Pervasive computing and Internet of Things (IoTs) paradigms have created a huge potential for new business. To fully realize this potential, there is a need for a common way to abstract the heterogeneity of devices so that their functionality can be represented as a virtual computing platform. To this end, we present novel semantic level interoperability architecture for pervasive computing and IoTs. There are two main principles in the proposed architecture. First, information and capabilities of devices are represented with semantic web knowledge representation technologies and interaction with devices and the physical world is achieved by accessing and modifying their virtual representations. Second, global IoT is divided into numerous local smart spaces managed by a semantic information broker (SIB) that provides a means to monitor and update the virtual representation of the physical world. An integral part of the architecture is a resolution infrastructure that provides a means to resolve the network address of a SIB either using a physical object identifier as a pointer to information or by searching SIBs matching a specification represented with SPARQL. We present several reference implementations and applications that we have developed to evaluate the architecture in practice. The evaluation also includes performance studies that, together with the applications, demonstrate the suitability of the architecture to real-life IoT scenarios. In addition, to validate that the proposed architecture conforms to the common IoT-A architecture reference model (ARM), we map the central components of the architecture to the IoT-ARM.