Special Issue "Knowledge Engineering and Semantic Web"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 October 2018)

Special Issue Editors

Guest Editor
Dr. Przemysław Różewski

West Pomeranian University of Technology, Szczecin, Poland
Website | E-Mail
Interests: cooperative/collaborative processes; competences management; ontology engineering; ontologies in education; educational technology
Guest Editor
Dr. Christoph Lange

University of Bonn, 53113 Bonn, Germany
Fraunhofer IAIS, 53757 Sankt Augustin, Germany
Website | E-Mail
Interests: semantic web; linked data; ontology engineering; mathematical knowledge management; scholarly communication
Guest Editor
Dr. Dmitry Mouromtsev

ITMO University, Sankt-Peterburg, Russia
Website | E-Mail
Interests: semantic web;linked open data; ontology engineering; IoT

Special Issue Information

Dear Colleagues,

The International Conference on Knowledge Engineering and Semantic Web (KESW 2017) has been held on November 08–November 10, 2017 in Szczecin, Poland. For more information about the conference please use this link: http://2017.kesw.ru/calls/.

KESW 2017 is the 8th conference in the series. KESW is a top international event dedicated to discussing research results and directions in the areas related to Knowledge Representation and Reasoning, Semantic Web, and Linked Data. Its aim is to bring together researchers, practitioners, and educators, in particular from ex-USSR, Eastern and Northern Europe, to present and share ideas regarding Semantic Web, and popularize the area in these regions.

The scope and topics of interest of the Special Issue papers follow those of KESW 2017 and are listed below: Scalable Data Access, Ontologies and Controlled Vocabularies, Linked Data, Natural language Processing and Semantic Web, Human Computer Interaction, Knowledge Representation and Reasoning, Storage Solutions for the Semantic Web, Semantic Technologies in Industry, Semantic Social Web, Trust, security, and privacy

The authors of a number of selected full papers of high quality will be invited after the conference to submit revised and extended versions of their originally accepted conference papers to this Special Issue of Information published by MDPI in open access. The selection of these best papers will be based on their ratings in the conference review process, quality of the presentation during the conference, and expected impact to the research community. Submitted papers should be extended to the size of regular research or review articles with 50% extension of new results. All submitted papers will undergo our standard peer-review procedure.

Accepted papers will be published in Open Access format in Information and collected together in this Special Issue website. We would like to publish the extended best papers of the conference with Article Processing Fees waived. Since the MDPI organizes the peer review, only if the papers receive all positive review reports, the publishing fees will be waived. The "positive reports" mean no reject reports. If other papers are accepted with reject reports (in all rounds of peer review), they will be charged for Article Processing fees.

The deadline for submission to this Special Issue is 15 October, 2018.

Please prepare and format your paper according to the Instructions for Authors. Use the LaTeX or Microsoft Word template file of the journal (both are available from the Instructions for Authors page). Manuscripts should be submitted online via our susy.mdpi.com editorial system.

Dr. Przemysław Różewski
Dr. Christoph Lange
Dr. Dmitry Mouromtsev
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (8 papers)

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Research

Open AccessArticle Comparing Supervised Machine Learning Strategies and Linguistic Features to Search for Very Negative Opinions
Information 2019, 10(1), 16; https://doi.org/10.3390/info10010016
Received: 16 November 2018 / Revised: 14 December 2018 / Accepted: 24 December 2018 / Published: 4 January 2019
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Abstract
In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and [...] Read more.
In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised machine learning classifiers: Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM). The experiments we have carried out show that SVM clearly outperforms NB and DT in all datasets by taking into account all features individually as well as their combinations. Full article
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Open AccessArticle Semantic Distance Spreading Across Entities in Linked Open Data
Information 2019, 10(1), 15; https://doi.org/10.3390/info10010015
Received: 27 November 2018 / Revised: 21 December 2018 / Accepted: 24 December 2018 / Published: 2 January 2019
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Abstract
Recommender systems can utilize Linked Open Data (LOD) to overcome some challenges, such as the item cold start problem, as well as the problem of explaining the recommendation. There are several techniques in exploiting LOD in recommender systems; one approach, called Linked Data [...] Read more.
Recommender systems can utilize Linked Open Data (LOD) to overcome some challenges, such as the item cold start problem, as well as the problem of explaining the recommendation. There are several techniques in exploiting LOD in recommender systems; one approach, called Linked Data Semantic Distance (LDSD), considers nearby resources to be recommended by calculating a semantic distance between resources. The LDSD approach, however, has some drawbacks such as its inability to measure the semantic distance resources that are not directly linked to each other. In this paper, we first propose another variation of the LDSD approach, called wtLDSD, by extending indirect distance calculations to include the effect of multiple links of differing properties within LOD, while prioritizing link properties. Next, we introduce an approach that broadens the coverage of LDSD-based approaches beyond resources that are more than two links apart. Our experimental results show that approaches we propose improve the accuracy of the LOD-based recommendations over our baselines. Furthermore, the results show that the propagation of semantic distance calculation to reflect resources further away in the LOD graph extends the coverage of LOD-based recommender systems. Full article
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Open AccessArticle A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection
Information 2019, 10(1), 6; https://doi.org/10.3390/info10010006
Received: 6 November 2018 / Revised: 14 December 2018 / Accepted: 20 December 2018 / Published: 25 December 2018
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Abstract
This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: [...] Read more.
This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution. Full article
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Open AccessArticle Improving the Accuracy in Sentiment Classification in the Light of Modelling the Latent Semantic Relations
Information 2018, 9(12), 307; https://doi.org/10.3390/info9120307
Received: 15 October 2018 / Revised: 19 November 2018 / Accepted: 28 November 2018 / Published: 4 December 2018
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Abstract
The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discriminant and probabilistic methods for LSR [...] Read more.
The research presents the methodology of improving the accuracy in sentiment classification in the light of modelling the latent semantic relations (LSR). The objective of this methodology is to find ways of eliminating the limitations of the discriminant and probabilistic methods for LSR revealing and customizing the sentiment classification process (SCP) to the more accurate recognition of text tonality. This objective was achieved by providing the possibility of the joint usage of the following methods: (1) retrieval and recognition of the hierarchical semantic structure of the text and (2) development of the hierarchical contextually-oriented sentiment dictionary in order to perform the context-sensitive SCP. The main scientific contribution of this research is the set of the following approaches: at the phase of LSR revealing (1) combination of the discriminant and probabilistic models while applying the rules of adjustments to obtain the final joint result; at all SCP phases (2) considering document as a complex structure of topically completed textual components (paragraphs) and (3) taking into account the features of persuasive documents’ type. The experimental results have demonstrated the enhancement of the SCP accuracy, namely significant increase of average values of recall and precision indicators and guarantee of sufficient accuracy level. Full article
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Open AccessArticle Ontology-Based Representation for Accessible OpenCourseWare Systems
Information 2018, 9(12), 302; https://doi.org/10.3390/info9120302
Received: 29 October 2018 / Revised: 23 November 2018 / Accepted: 26 November 2018 / Published: 29 November 2018
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Abstract
OpenCourseWare (OCW) systems have been established to provide open educational resources that are accessible by anyone, including learners with special accessibility needs and preferences. We need to find a formal and interoperable way to describe these preferences in order to use them in [...] Read more.
OpenCourseWare (OCW) systems have been established to provide open educational resources that are accessible by anyone, including learners with special accessibility needs and preferences. We need to find a formal and interoperable way to describe these preferences in order to use them in OCW systems and retrieve relevant educational resources. This formal representation should use standard accessibility definitions of OCW that can be reused by other OCW systems to represent accessibility concepts. In this article, we present an ontology to represent the accessibility needs of learners with respect to the IMS AfA specifications. The ontology definitions together with rule-based queries are used to retrieve relevant educational resources. Related to this, we developed a user interface component that enables users to create accessibility profiles representing their individual needs and preferences based on our ontology. We evaluated the approach with five examples profiles. Full article
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Open AccessArticle Alignment: A Hybrid, Interactive and Collaborative Ontology and Entity Matching Service
Information 2018, 9(11), 281; https://doi.org/10.3390/info9110281
Received: 9 October 2018 / Revised: 3 November 2018 / Accepted: 12 November 2018 / Published: 15 November 2018
Cited by 1 | PDF Full-text (1022 KB) | HTML Full-text | XML Full-text
Abstract
Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing [...] Read more.
Ontology matching is an essential problem in the world of Semantic Web and other distributed, open world applications. Heterogeneity occurs as a result of diversity in tools, knowledge, habits, language, interests and usually the level of detail. Automated applications have been developed, implementing diverse aligning techniques and similarity measures, with outstanding performance. However, there are use cases where automated linking fails and there must be involvement of the human factor in order to create, or not create, a link. In this paper we present Alignment, a collaborative, system aided, interactive ontology matching platform. Alignment offers a user-friendly environment for matching two ontologies with the aid of configurable similarity algorithms. Full article
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Open AccessArticle Smart Process Optimization and Adaptive Execution with Semantic Services in Cloud Manufacturing
Information 2018, 9(11), 279; https://doi.org/10.3390/info9110279
Received: 8 October 2018 / Revised: 3 November 2018 / Accepted: 9 November 2018 / Published: 13 November 2018
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Abstract
A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces [...] Read more.
A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. In order to address this collaboration challenge, we propose a novel pragmatic approach for the process analysis, implementation and execution. This is achieved through sets of semantic annotations of business process models encoded into BPMN 2.0 extensions. Building blocks for such manufacturing processes are the individual available services, which are also semantically annotated according to the Everything-as-a-Service (XaaS) principles and stored into a common marketplace. The optimization of such manufacturing processes combines pattern-based semantic composition of services with their non-functional aspects. This is achieved by means of Quality-of-Service (QoS)-based Constraint Optimization Problem (COP) solving, resulting in an automatic implementation of service-based manufacturing processes. The produced solution is mapped back to the BPMN 2.0 standard formalism by means of the introduced extension elements, fully detailing the enactable optimal process service plan produced. This approach allows enacting a process instance, using just-in-time service leasing, allocation of resources and dynamic replanning in the case of failures. This proposition provides the best compromise between external visibility, control and flexibility. In this way, it provides an optimal approach for business process models’ implementation, with a full service-oriented taste, by implementing user-defined QoS metrics, just-in-time execution and basic dynamic repairing capabilities. This paper presents the described approach and the technical architecture and depicts one initial industrial application in the manufacturing domain of aluminum forging for bicycle hull body forming, where the advantages stemming from the main capabilities of this approach are sketched. Full article
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Open AccessArticle Reducing the Deterioration of Sentiment Analysis Results Due to the Time Impact
Information 2018, 9(8), 184; https://doi.org/10.3390/info9080184
Received: 24 June 2018 / Revised: 16 July 2018 / Accepted: 24 July 2018 / Published: 25 July 2018
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Abstract
The research identifies and substantiates the problem of quality deterioration in the sentiment classification of text collections identical in composition and characteristics, but staggered over time. It is shown that the quality of sentiment classification can drop up to 15% in terms of [...] Read more.
The research identifies and substantiates the problem of quality deterioration in the sentiment classification of text collections identical in composition and characteristics, but staggered over time. It is shown that the quality of sentiment classification can drop up to 15% in terms of the F-measure over a year and a half. This paper presents three different approaches to improving text classification by sentiment in continuously-updated text collections in Russian: using a weighing scheme with linear computational complexity, adding lexicons of emotional vocabulary to the feature space and distributed word representation. All methods are compared, and it is shown which method is most applicable in certain cases. Experiments comparing the methods on sufficiently representative text collections are described. It is shown that suggested approaches could reduce the deterioration of sentiment classification results for collections staggered over time. Full article
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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