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Indexed in the SCIE (2018 Impact Factor 0.854), and in Scopus

Journal of Web Engineering

Martin Gaedke, Chemnitz University of Technology, Germany
Geert-Jan Houben, Delft University of Technology, The Netherlands
Flavius Frasincar, Erasmus University Rotterdam, The Netherlands
Florian Daniel, Politecnico di Milano, Italy

ISSN: 1540-9589 (Print Version),

ISSN: 1544-5976 (Online Version)
Vol: 18   Issue: Combined Issue 1, 2 & 3

Published In:   January 2019

Publication Frequency: 8 issues per year

Articles in 2020

Search Available Volume and Issue for Journal of Web Engineering

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Integrating Semantic Run-Time Models for Adaptive Software Systems

Francesco Poggi1, Davide Rossi1 and Paolo Ciancarini2

1Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, Italy
2University of Bologna (Italy) and Innopolis University (Russia)

Abstract: [+]    |    Download File [ 14444KB ]    |   Read Article Online

Abstract: Software-intensive systems work in ever-changing environments requiring expensive technical efforts to manage their evolution. In order to mitigate their risks and costs they should dynamically selfadapt to any modification of their environment. MAPE-K (Monitor, Analyze, Plan, Execute – Knowledge) is the basic architectural pattern for building software-intensive self-adaptable systems. In this paper we propose an approach in which all the information about a system and its environment is unified by using Semantic Web technologies into a set of semantic run-time models which enhance the Knowledge in MAPE-K.Ontologies are used to manage the interaction and integration of these models with disparate data sources. The resulting knowledge base is then used to drive adaptation activities exploiting well known languages and notations. We discuss how MAPE-K can be exploited in order to take advantage of ontological representations, along with SemanticWeb languages and tools, by studying a real-word case study: a legacy system that was not designed to perform automatic adaptation. We discuss merits and limits of our approach based on semantic runtime models both in the context of this specific case study and in a broader scope.

Keywords: Autonomic systems, adaptive software, MAPE-K, SemanticWeb, ontology.

RiAiR: A Framework for Sensitive RDF Protection

Irvin Dongo1,2 and Richard Chbeir3

1Univ. Bordeaux, ESTIA, Bidart, France
2Universidad Católica San Pablo, Arequipa, Peru
3Univ Pau & Pays Adour, LIUPPA, EA3000, 64600, Anglet, France

Abstract: [+]    |    Download File [ 4355KB ]    |   Read Article Online

Abstract: The Semantic Web and the Linked Open Data (LOD) initiatives promote the integration and combination of RDF data on theWeb. In some cases, data need to be analyzed and protected before publication in order to avoid the disclosure of sensitive information. However, existingRDF techniques do not ensure that sensitive information cannot be discovered since all RDF resources are linked in the Semantic Web and the combination of different datasets could produce or disclose unexpected sensitive information. In this context, we propose a framework, called RiAiR, which reduces the complexity of the RDF structure in order to decrease the interaction of the expert user for the classification of RDF data into identifiers, quasi-identifiers, etc.An intersection process suggests disclosure sources that can compromise the data. Moreover, by a generalization method, we decrease the connections among resources to comply with the main objectives of integration and combination of the Semantic Web. Results show a viability and high performance for a scenario where heterogeneous and linked datasets are present.

Keywords: RDF protection, Sensitive information, Semantic Web, Disclosure source.

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

Frank van Harmelen and Annette ten Teije

Department of Computer Science, Vrije Universiteit Amsterdam, Netherlands

Abstract: [+]    |    Download File [ 3174KB ]    |   Read Article Online

Abstract: We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.

Keywords: Hybrid systems, neurosymbolic systems, knowledge representation, machine learning, design patters.

Temporal Extensions to RDF

Hsien-Tseng Wang1and Abdullah Uz Tansel1,2,3

1Department of Computer Science, The Graduate Center, The City University of New York, USA
2Paul H. Chook Department of Information Systems and Statistics, Baruch College, The City University of New York, USA
3School of Engineering, Thammasat University Thailand.

Abstract: [+]    |    Download File [ 550KB ]    |   Read Article Online

Abstract: The SemanticWeb aims at building a foundation of semantic-based data models and languages for not only manipulating data and knowledge, but also in decision making by machines. Naturally, time-varying data and knowledge are required in Semantic Web applications to incorporate time and further reason about it. However, the original specifications of RDF and OWL do not include constructs for handling time-varying data and knowledge. For simplicity, RDF model is confined to binary predicates, hence some form of reification is needed to represent higher-arity predicates. To this date, there are many proposals extending RDF and OWL for handling temporal data and knowledge. They all focus on the valid time. In this paper, we examine each of these proposals and develop a taxonomy to classify them according to the form of reification employed: explicit reification or implicit reification. The implicit reification proposals are further divided into three sub-categories according to semantic constructs they use. Some of these proposals stay compliant to the RDF and OWL standards whereas others add new constructs to RDF model and SPARQL query language. Additionally, we compare these proposed models with respect to characteristics, such as their syntax and semantics, their compliance to RDF and OWL specifications, their need for additional objects, etc. The comparison provides a useful guideline for the researchers and practitioners of the Semantic Web in managing temporal data and knowledge.

Keywords: The Semantic Web, Resource Description Framework, Taxonomy, Temporal Data, Temporal Knowledge.

OPT+: A Monotonic Alternative to OPTIONAL in SPARQL

Sijin Cheng and Olaf Hartig

Department of Computer and Information Science (IDA), Linköping University, Sweden

Abstract: [+]    |    Download File [ 1266KB ]    |   Read Article Online

Abstract: Due to the OPTIONAL operator, the core fragment of the SPARQL query language is non-monotonic. That is, some solutions of a query result can be returned to the user only after having consulted all relevant parts of the queried dataset(s). This property presents an obstacle when developing query execution approaches that aim to reduce responses times rather than the overall query execution times. Reducing the response times–i.e., returning as many solutions as early as possible– is important in particular in Web-based client-server query processing scenarios in which network latencies dominate query execution times. Such scenarios are typical in the context of integration of Web data sources where a data integration component executes queries over a decentralized federation of such data sources. In this paper we introduce an alternative operator that is similar in spirit to OPTIONAL but without causing non-monotonicity. We show fundamental properties of this operator and observe that the downside of achieving the desired monotonicity property is a potentially significant increase in query result sizes.We study the extend of this trade-off in practice. Thereafter, we introduce different algorithms to implement the new operator and evaluate them regarding their potential to reduce response times.

Keywords: Semantic web, linked data, query language, optimization.

Citation Count Prediction using Abstracts

Takahiro Baba1, Kensuke Baba2, and Daisuke Ikeda1

1Kyushu University, 819-0395, Fukuoka, Japan
2Fujitsu Laboratories, Kawasaki, 211-8588, Japan

Abstract: [+]    |    Download File [ 872KB ]    |   Read Article Online

Abstract: Researchers are expected to find previous literature that is related to their research and potentially has a scientific impact from among a large number of publications. This paper addresses the problem of predicting the citation count of each research paper, that is, the number of citations from other papers to that paper. Previous literature related to the problem claims that the textual data of papers do not deeply affect the prediction compared with data about the authors and venues of publication. In contrast, the authors of this paper detected the citation counts of papers using only the paper abstracts. Additionally, they investigated the effect of technical terms used in the abstracts on the detection. They classified abstracts of papers with high and low citation counts and applied the classification to the abstracts modified by hiding the technical terms used in them. The results of their experiments indicate that the high and low of citation counts of research papers can be detected using their abstracts, and the effective features used in the prediction are related to the trend of research topics.

Keywords: Citation count prediction; Document classification; Text analysis; Machine learning.

LOD Construction Through Supervised Web Relation Extraction and Crowd Validation

Goran Rumin1 and Igor Mekterović2

1Infobip, Zagreb, Croatia
2University of Zagreb Faculty of Electrical Engineering and Computing, Croatia

Abstract: [+]    |    Download File [ 3838KB ]    |   Read Article Online

Abstract: Free, unstructured text is the dominant format in which information is stored and published. To interpret such vast amount of data one must employ a programmatic approach. In this paper, we describe a novel approach – a pipeline in which interesting relations are extracted from web portals news texts, stored as RDF triplets, and finally validated by end user via browser extension. In the process, different machine learning algorithms were tested on relation extraction, enhanced with our own set of features and thoroughly evaluated, with excellent precision and recall results compared to models used for semantic knowledge expansion. Building on those results, we implement and describe the component to resolve discovered entities to existing semantic entities from three major online repositories. Finally, we implement and describe the validation process in which RDF triplets are presented to the web portal reader for validation via Chrome extension.

Keywords: Relation Extraction, Machine Learning, RDF, Linked Open Data, Crowd validation, SemanticWeb,Web Application.

River Publishers: Journal of Web Engineering