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Modern developments in the state-of-the-art open-source activation functions for Convolutional Neural Networks (CNNs) have broadened the selection of benchmark activations for Deep Learning (DL)-aided classification. Nevertheless, achieving discrimination of non-linear input image data in CNN is still not straightforward and it is unclear how such novel activation functions can have translational applications with tangible impact. hyper-sinh, made freely available in TensorFlow and Keras, was demonstrated as a benchmark activation function on five (N=5) datasets in its ground-breaking paper. Measuring the value from deploying this activation in a specific application is pivotal to supply the required evidence of its performance on real-life supervised DL-based image classification tasks. In this study, a CNN was for the first time combined with hypersinh to aid early detection of Parkinson’s Disease (PD) from discriminating pathophysiological patterns extracted from spiral drawings. Thus, the hyper-sinh activation was deployed to maximise the separability of the input features from spiral drawings via automated pattern recognition. We demonstrate the accuracy and reliability of hyper-sinh-CNN to aid early diagnosis of PD, evaluated against other gold standard activation functions, including the recent Quantum ReLU (QReLU) and the modified Quantum ReLU (m-QReLU) that solved the ‘dying ReLU’ problem for the first time in the literature of DL. Two (N=2) benchmark datasets from the database of the Botucatu Medical School, São Paulo State University in Brazil, scaled to be in 28 by 28 pixels as the MNIST benchmark data, were used to discriminate between input image patterns of 158 subjects (53 healthy controls and 105 patients with PD) from spirals drawn on graphics tablets. Overtraining was avoided via early stopping and the models were developed and tested in TensorFlow and Keras (Python 3.6). The supervised model (hyper-sinh-CNN) could detect early Parkinson’s Disease with 81% and 91% classification accuracy from the two datasets respectively (F1-scores: 73% and 91% correspondingly). Furthermore, the model achieved high sensitivity (81% and 91%). Thus, this study validates the application of hyper-sinh to aid real-life supervised DL-based image classification, in particular early diagnosis of PD from spiral drawings.

This chapter describes what is meant by semantic interoperability of geographic information, the elements involved in semantic interoperability of geographic information, and how semantic interoperability of geographic information has been addressed in spatial data infrastructures (SDIs), standards, and in the Semantic Web. It is formed of ten sections. Semantics is a term used frequently in many contexts. Section 15.2 is dedicated to the description of semantics. Cognition is an area of study about the abstraction of phenomena by human beings. It is significantly related to semantics. Section 15.3 introduces basic notions of cognition related to semantics. Ontology is a well-grounded notion in the study of semantic interoperability and in the Semantic Web. Section 15.4 explains what ontology is about. This will lead to the description, in Sect. 15.5, of semantic interoperability in the context of geographic information, i.e., geosemantic interoperability. A spatial data infrastructure is the typical way in which interoperability of geographic information is implemented. Section 15.6 addresses geosemantic interoperability with respect to spatial data infrastructures. Standards underpin interoperability for geographic information and spatial data infrastructures. Section 15.7 provides an overview of the current contribution of standards bodies with respect to geosemantic interoperability. The Semantic Web makes an important contribution to semantic interoperability. Section 15.8 gives an account of the Semantic Web and its development. The Geospatial Semantic Web brings together the effort of geosemantic interoperability with the effort of the Semantic Web, as presented in Sect. 15.9. Section 15.10 provides concluding remarks on the chapter.

Personalization is becoming very important direction in semantic web search for the users that needs to find appropriate information. In this paper, a classification of web personalization is proposed and semantic web search tools are investigated. Building user interest profile is essential for personalizing. Nowadays, semantic web tools use ontologies for personalization because of their advantages. It is important to mention that most of the semantic web search tools use agent technologies for implementation., Comment: 5 pages

Η συγκεκριμένη διατριβή στοχεύει στον σχεδιασμό της μεθοδολογίας που θα εφαρμοστεί για την υλοποίηση ενός προσκομιστή πληροφορίας από τον Παγκόσμιο Ιστό, ο οποίος θα λειτουργεί λαμβάνοντας υπόψη θεματικά κριτήρια. Τέτοιου είδους προγράμματα ανίχνευσης πληροφορίας, είναι ευρέως γνωστά ως θεματικά εστιασμένοι προσκομιστές ιστοσελίδων. Κατά τη διάρκεια της μελέτης μας, σχεδιάσαμε και υλοποιήσαμε ένα καινοτόμο σύστημα θεματικής κατηγοριοποίησης ιστοσελίδων που κάνει εκτεταμένη χρήση των σημασιολογικών δεδομένων τα οποία περιέχονται στο σημασιολογικό δίκτυο WordNet. Η απόφαση για την αξιοποίηση του WordNet ελήφθη με τη φιλοδοξία να αντιμετωπιστούν αποτελεσματικά φαινόμενα ασάφειας εννοιών που μειώνουν τις επιδόσεις των διαθέσιμων θεματικών κατηγοριοποιητών. Η καταλληλότητα του WordNet για την επίλυση της σημασιολογικής ασάφειας έχει αποδειχθεί στο παρελθόν, αλλά ποτέ δεν εξετάστηκε σε ένα σύστημα εστιασμένης προσκόμισης ιστοσελίδων με τον συγκεκριμένο τρόπο, ενώ ποτέ δεν έχει αξιοποιηθεί στην κατηγοριοποίηση ιστοσελίδων για την ελληνική γλώσσα. Ως εκ τούτου, ο θεματικός κατηγοριοποιητής που υλοποιήσαμε, και κατά συνέπεια, και ο εστιασμένος προσκομιστής στον οποίο ενσωματώνεται ο κατηγοριοποιητής, είναι καινοτόμοι όσο αφορά τον τρόπο με τον οποίο αποσαφηνίζουν έννοιες λέξεων με στόχο την αποτελεσματική ανίχνευση του θεματικού προσανατολισμού μίας ιστοσελίδας .Ένας προσκομιστής ιστοσελίδων είναι ένα πρόγραμμα που με αφετηρία μία λίστα διευθύνσεων ιστοσελίδων (URLs) αρχικοποίησης προσκομίζει το περιεχόμενο των ιστοσελίδων που συναντά και συνεχίζει ακολουθώντας τους εσωτερικούς τους συνδέσμους με απώτερο σκοπό την προσκόμιση όσο το δυνατό μεγαλύτερου υποσυνόλου δεδομένων του Παγκόσμιου Ιστού (ανάλογα με τους διαθέσιμους πόρους, την χωρητικότητα του δικτύου, κλπ.). Δεδομένου ότι ο όγκος των δεδομένων που είναι διαθέσιμα στον Παγκόσμιο Ιστό αυξάνεται με εκθετικό ρυθμό, είναι πρακτικά αδύνατο να προσκομιστούν όλες οι ζητούμενες πηγές πληροφορίας ανά πάσα στιγμή. Ένας τρόπος για να αντιμετωπίσουμε το συγκεκριμένο πρόβλημα είναι η εκμετάλλευση συστημάτων εστιασμένης προσκόμισης ιστοσελίδων που στοχεύουν στη λήψη ιστοσελίδων συγκεκριμένης θεματολογίας που εκφράζουν κάθε φορά το θεματικό προφίλ του χρήστη, σε αντίθεση με τους προσκομιστές ιστοσελίδων γενικού σκοπού που καταναλώνουν πόρους άσκοπα προσπαθώντας να προσκομίσουν κάθε πιθανή πηγή πληροφορίας που συναντούν. Οι εστιασμένοι προσκομιστές χρησιμοποιούνται εκτενώς, για την κατασκευή θεματικά προσανατολισμένων ευρετηρίων ιστοσελίδων, κάθε ένα από τα οποία έχει την δυνατότητα να εξυπηρετήσει αιτήσεις χρηστών με συγκεκριμένο θεματικό προσανατολισμό. Με αυτό τον τρόπο είναι δυνατόν να αντιμετωπιστεί το πρόβλημα της υπερφόρτωσης πληροφοριών.Προκειμένου να επιτελέσουμε την συγκεκριμένη εργασία μελετήσαμε εκτενώς υπάρχουσες τεχνικές εστιασμένης προσκόμισης, στις οποίες στηριχθήκαμε ώστε να ορίσουμε την μεθοδολογία που θα ακολουθήσουμε. Το αποτέλεσμα είναι η υλοποίηση ενός θεματικά εστιασμένου πολυνηματικού προσκομιστή, ο οποίος ενσωματώνει τις εξής καινοτομίες: είναι ρυθμισμένος προκειμένου να εκτελεί εστιασμένες προσκομίσεις σε ιστοσελίδες ελληνικού ενδιαφέροντος, αποσαφηνίζει το κείμενο που αντιστοιχεί σε ιστοσελίδες προκειμένου να ανακαλύψει τον θεματικό τους προσανατολισμό. Επιπλέον προτείνουμε μία σειρά υποσυστημάτων τα οποία θα μπορούσαν να ενσωματωθούν στο σύστημα εστιασμένης προσκόμισης προκειμένου να ενισχύσουμε την απόδοσή του. Τέτοια συστήματα είναι το υποσύστημα ανίχνευσης όψεων που αντιστοιχίζονται σε επώνυμες οντότητες καθώς και το υποσύστημα εξαγωγής λέξεων κλειδιών που μπορούν να χρησιμοποιηθούν ως χαρακτηριστικά κατηγοριοποίσης από το αλφαριθμητικό των διευθύνσεων (URL) ιστοσελίδων. Για να παρουσιάσουμε την αποτελεσματικότητα της προτεινόμενης μεθόδου, διενεργήσαμε μία σειρά πειραματικών μετρήσεων. Συγκεκριμένα αξιολογήσαμε πειραματικά τα ακόλουθα: την αποτελεσματικότητα του αλγορίθμου αποσαφήνισης που ενσωματώσαμε στον προσκομιστή, την απόδοση του θεματικού κατηγοριοποιητή ο οποίος καθορίζει την συμπεριφορά του εστιασμένου προσκομιστή σχετικά με το αν μια σελίδα θα πρέπει να κατέβει ως θεματικά σχετική με το θέμα ενδιαφέροντος ή όχι, την απόδοση του εστιασμένου προσκομιστή καταγράφοντας τον ρυθμό απόκτησης που επιτυγχάνει κατά την διάρκεια της εστιασμένης προσκόμισης χρησιμοποιώντας κάθε φορά διαφορετικά χαρακτηριστικά κατηγοριοποίησης, την καταλληλότητα του υποσυστήματος εξαγωγής λέξεων-κλειδιών από το αλφαριθμητικό URL για την περιγραφή του θεματικού προσανατολισμού της ιστοσελίδας και τέλος τη χρησιμότητα του συστήματος αναγνώρισης επώνυμων οντοτήτων στην οργάνωση ιστοσελίδων των οποίων η σημασιολογία δεν αναπαρίσταται ικανοποιητικά σε σημασιολογικούς πόρους γενικού σκοπού συμπεριλαμβανομένου του σημασιολογικού δικτύου WordNet.Τα πειραματικά αποτελέσματα επιβεβαιώνουν τη συμβολή του θεματικά εστιασμένου προσκομιστή που προτείνουμε στην προσκόμιση περιεχομένου ειδικού ενδιαφέροντος από τον Παγκόσμιο Ιστό. Παράλληλα αποδεικνύουμε ότι όλες οι μέθοδοι που ενσωματώσαμε στο σύστημα εστιασμένης προσκόμισης είναι δυνατό να συνεργαστούν κατά τρόπο που να βελτιώνει την απόδοση του προσκομιστή .Τέλος από τα πειραματικά αποτελέσματα αποδεικνύεται ότι η προτεινόμενη τεχνική είναι εξίσου αποτελεσματική για ιστοσελίδες στα αγγλικά και στα ελληνικά. Επιπλέον πιστεύουμε ότι μπορεί να εφαρμοστεί με επιτυχία και σε ιστοσελίδες που περιέχουν κείμενα άλλων φυσικών γλωσσών, με προϋπόθεση την ύπαρξη σημασιολογικών πόρων, αντίστοιχων με το WordNet και διαθέσιμων εργαλείων που θα επιτρέπουν την ανάλυση των δεδομένων κειμένου τους.

Ο Σημασιολογικός Ιστός (ΣΙ) είναι μια απόπειρα ανάπτυξης προτύπων και τεχνολογιών που θα επιτρέπουν στους υπολογιστές να εντοπίζουν και να επεξεργάζονται ευκολότερα πόρους (έγγραφα, δεδομένα, υπηρεσίες) του Παγκόσμιου Ιστού. Κεντρικό ρόλο στο ΣΙ παίζουν οι οντολογίες. Μια οντολογία αποτελεί τον ορισμό μιας θεματικής περιοχής (π.χ., ηλεκτρονικά προϊόντα) ορίζοντας τις τάξεις και υποτάξεις των αντικειμένων του πεδίου, τους τύπους πιθανών σχέσεων μεταξύ τους κ.τ.λ. Το τρέχον επικρατών πρότυπο για τον ορισμό του Σημασιολογικού Ιστού είναι η OWL, μια τυπική γλώσσα που βασίζεται στην περιγραφική λογική και στην RDF, με την OWL 2 να αποτελεί την πιο πρόσφατη έκδοση του προτύπου OWL. Δεδομένης μιας οντολογίας OWL για κάποια θεματική περιοχή, κάποιος μπορεί να εκδώσει στον Ιστό δεδομένα, τα οποία μπορούν να διαβαστούν από υπολογιστές, σχετικά με αυτή την περιοχή (π.χ., καταλόγους προϊόντων, τα στοιχεία τους κ.τ.λ.), με τα δεδομένα αυτά να έχουν τυπικά ορισμένη σημασιολογία που βασίζεται στον ορισμό της οντολογίας. Έχουν αναπτυχθεί διάφορα συντακτικά OWL, αλλά όσοι δεν είναι είναι εξοικειωμένοι με τυπικές αναπαραστάσεις γνώσης συχνά έχουν δυσκολίες στην κατανόηση τους. Αυτή η διατριβή εξέτασε μεθόδους που επιτρέπουν σε τελικούς χρήστες να δουν αναπαραστάσεις γνώσης βασισμένες σε οντολογίες του Σημασιολογικού Ιστού στην μορφή αυτόματα παραγόμενων κειμένων σε πολλαπλές φυσικές γλώσσες.Το πρώτο μέρος της διατριβής βελτιώνει το NaturalOWL, ένα σύστημα Παραγωγής Φυσικής Γλώσσας από οντολογίες OWL που είχε αναπτυχθεί παλιότερα στο ΟΠΑ. Το σύστημα τροποποιήθηκε ώστε να υποστηρίζει την OWL 2 και να μπορεί να παράγει υψηλότερης ποιότητας κείμενα. Πειράματα έδειξαν ότι τα κείμενα που παράχθηκαν από την νέα έκδοση του NaturalOWL είναι πράγματι υψηλής ποιότητας και σημαντικά καλύτερα από κείμενα που παράχθηκαν από απλούστερα συστήματα, συχνά γνωστά ως ontology verbalizers, δεδομένου ότι οι απαραίτητοι γλωσσικοί πόροι (π.χ., σχέδια προτάσεων που εκφράζουν σχέσεις) είναι διαθέσιμοι στο NaturalOWL. Το δεύτερο μέρος της διατριβής εξέτασε μεθόδους εξόρυξης κειμένου και μηχανικής μάθησης για να εξάγει από τον Ιστό αυτόματα ή ήμι-αυτόματα τους πιο σημαντικούς γλωσσικούς πόρους που το NaturalOWL χρειάζεται για να παραγάγει κείμενα υψηλής ποιότητας. Πειράματα έδειξαν ότι η ήμι-αυτόματη προσέγγιση, όπου ένας άνθρωπος εξετάζει τους αυτόματα παραγόμενους γλωσσικούς πόρους, επιτρέπει στο NaturaOWL να παράγει κείμενα σχεδόν της ίδιας ποιότητας όσο με γλωσσικούς πόρους που έχουν συγγραφεί χειρωνακτικά από το μηδέν. Το τρίτο μέρος της διατριβής στόχευε να βελτιώσει περαιτέρω την ποιότητα των παραγόμενων κειμένων αναπτύσσοντας ένα μοντέλο Ακέραιο Γραμμικού Προγραμματισμού που εξετάζει παράλληλα την επιλογή περιεχομένου, την λεξικοποίηση, τον συνδυασμό προτάσεων, και μια περιορισμένη μορφή της παραγωγής αναφορικών εκφράσεων, σε αντίθεση με την αρχιτεκτονική διασωλήνωσης των περισσότερων συστημάτων Παραγωγής Φυσικής Γλώσσας, όπου τα τέσσερα στάδια εξετάζονται άπληστα το ένα μετά το άλλο. Πειράματα έδειξαν ότι το νέο μοντέλο επιτρέπει στο NaturalOWL να εκφράζει περισσότερες πληροφορίες ανά λέξη, το οποίο είναι χρήσιμο όταν ο χώρος είναι περιορισμένος (π.χ., στις διαφημίσεις), χωρίς κάποια αντιληπτή αλλοίωση στην ποιότητα των παραγόμενων κειμένων.Καθ’ όλη τη διατριβή, χρησιμοποιήθηκαν οντολογίες από διάφορες θεματικές περιοχές (π.χ., πολιτιστική κληρονομιά, ηλεκτρονικά προϊόντα, βιοπληροφορική). Χρησιμοποιώντας τις μεθόδους αυτής της διατριβής, οργανισμοί (π.χ., εταιρίες, βιβλιοθήκες, μουσεία) μπορούν να εκδώσουν πληροφορίες στον Ιστό που να διαβάζονται από υπολογιστές (π.χ., πληροφορίες που προέρχονται από βάσεις δεδομένων) και είναι διαθέσιμες σε πολλαπλές φυσικές γλώσσες (κείμενα που παράγονται αυτόματα από δεδομένα). Με αυτό τον τρόπο οι πληροφορίες γίνονται πιο εύκολα διαθέσιμες και σε υπολογιστές και σε τελικούς χρήστες.

Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. While existing approaches and tools focus on facilitating the development of semantic annotations on web services, the validation of the quality of annotations is largely overlooked. Meanwhile, the refinement of semantic annotations mostly goes through manual processes, which not only is time-consuming but also requires significant domain knowledge. To enhance the Quality of Semantic Annotation (QoSA), we have developed a technique to incrementally assess and correct semantic annotations of web services. Aiming at supporting web service interoperation, we have formalized the QoSA of input and output parameters. Based on such formalism, test cases are automatically generated to validate service annotations. Learned semantic instances are then accumulated to iteratively validate semantic annotations of other services. Furthermore, a three-phase optimization methodology including local-feedback, global-feedback, and global-propagate is developed to improve the QoSA by incrementally correcting inaccurate annotations. Experiments over a real-world web services repository have demonstrated that our technique can effectively improve QoSA of services, gaining a 78.68 percent improvement in input parameters annotations and identifying 36.47 percent inaccurate output parameters annotations. The proposed technique can be equipped at various service repositories to enhance service discovery and recommendation.

There has been recently a growth of interest in developing the current machine-readable Web towards the next generation of machine-understandable Web - Semantic Web. The development of the Web to a global business was reasonably fast, whereas Semantic Web development has taken time from a plan to be used as the mainstream Web. It is also important to note that the use of Semantic Web would only be successful in small technologies. However, the goal of Semantic Web is to be used in big technologies and to be the mainstream Web. Some challenges may impede make further progress of Semantic Web. In this review paper, an overview of the current status and future needs of Semantic Web will be presented. Specifically, the challenges and needs of Semantic Web in the hope of shedding some light on the adoption or infusion of Semantic Web in the future will be discussed. Then, a critical evaluation of these challenges and needs will be presented. Semantic Web has a clear vision. It is moving, in line with this vision, towards overcoming the challenges and usability in real world applications.

Recent technologies in the Internet of Things (IoT) environment aim to provide intelligent services to users. Intelligent services can be managed and executed by systems that handle context information sets. Handling intelligent services leads to three major considerations: objects in the real world that should be described as metadata, a data enrichment procedure from sensing values for representing states, and controlling functionalities to manage services. In this study, an extensible data-enrichment scheme is proposed. The proposed scheme provides a way to describe profiles, data abstraction procedures, and functionalities that support the building of context information sets derived from raw datasets in the manner of a semantic web stack. Finally, data enrichment will help any system that uses context information by providing improved, understandable, and readable datasets to the service developers or the systems themselves.

Key fields in the humanities, such as history, art and language, are central to a major transformation that is changing scholarly practice in these fields: the so-called Digital Humanities (DH). A fundamental question in DH is how humanities datasets can be represented digitally, in such a way that machines can process them, understand their meaning, facilitate their inquiry, and exchange them on the Web. In this paper, we survey current efforts within the Semantic Web and Linked Data, a family of Webcompatible knowledge representation formalisms and standards, to represent DH objects in quantitative history and symbolic music. We also argue that the technological gap between the Semantic Web and Linked Data, and DH data owners is currently too wide for effective access and consumption of these semantically enabled humanities data. To this end, we proposegrlc, a thin middleware that leverages currently existing queries on the Web (expressed in, e.g., SPARQL) to transparently build standard Web APIs that facilitate access to any Linked Data.

International audience; Model-driven software engineering fosters abstraction through the use of models and then automation by transforming them into various artefacts, in particular to code, for example: 1) from architectural models to code, 2) from metamodels to API code (with EMF in Eclipse), 3) from entity models to front-end and back-end code in Web stack application (with JHispter), etc. In all these examples, the generated code is usually enriched by developers with additional code implementing advanced functionalities (e.g., checkers, recommenders, etc.) to build a full coherent system. When the system must evolve, so are the models to regenerate the code. As a result, the developers' enriched code may be impacted and thus need to co-evolve accordingly. Many approaches support the co-evolution of various artifacts, but not the co-evolution of code. This paper sheds light on this issue and envisions to fill this gap. We formulate the hypothesis that the code co-evolution can be driven by the model changes by means of change propagation. To investigate this hypothesis, we implemented a prototype for the case of metamodels and their accompanying code in EMF Eclipse. As a preliminary evaluation, we considered the case of the OCL Pivot metamodel evolution and its code co-evolution in two projects from version 3.2.2 to 3.4.4. Preliminary results confirms our hypothesis that model-driven evolution changes can effectively drive the code co-evolution. On 562 impacts in two projects' code by 221 metamodel changes, our approach was able to reach the average of 89% and 92,5% respectively of precision and recall.

This paper presents Parallel World Framework as a solution for simulations of complex systems within a time-varying knowledge graph and its application to the electric grid of Jurong Island in Singapore. The underlying modeling system is based on the Semantic Web Stack. Its linked data layer is described by means of ontologies, which span multiple domains. The framework is designed to allow what-if scenarios to be simulated generically, even for complex, inter-linked, cross-domain applications, as well as conducting multi-scale optimizations of complex superstructures within the system. Parallel world containers, introduced by the framework, ensure data separation and versioning of structures crossing various domain boundaries. Separation of operations, belonging to a particular version of the world, is taken care of by a scenario agent. It encapsulates functionality of operations on data and acts as a parallel world proxy to all of the other agents operating on the knowledge graph. Electric network optimization for carbon tax is demonstrated as a use case. The framework allows to model and evaluate electrical networks corresponding to set carbon tax values by retrofitting different types of power generators and optimizing the grid accordingly. The use case shows the possibility of using this solution as a tool for CO2 reduction modeling and planning at scale due to its distributed architecture.

Today's factory planning is characterized by a multitude of different IT systems. This results in data silos that lead to problems with the provision of information within factory planning. This paper addresses this problem by integrating data from different data silos into a knowledge graph based on the Semantic Web Stack. To improve the information provision a function is developed, which automatically identifies data changes and makes the data available in line with the demands of the planners on the basis of a role-based concept. The developed concept is implemented and validated in the factory planning of a german automobile manufacturer.

With the development of mobile technology, the users browsing habits are gradually shifted from only information retrieval to active recommendation. The classification mapping algorithm between users interests and web contents has been become more and more difficult with the volume and variety of web pages. Some big news portal sites and social media companies hire more editors to label these new concepts and words, and use the computing servers with larger memory to deal with the massive document classification, based on traditional supervised or semi-supervised machine learning methods. This paper provides an optimized classification algorithm for massive web page classification using semantic networks, such as Wikipedia, WordNet. In this paper, we used Wikipedia data set and initialized a few category entity words as class words. A weight estimation algorithm based on the depth and breadth of Wikipedia network is used to calculate the class weight of all Wikipedia Entity Words. A kinship-relation association based on content similarity of entity was therefore suggested optimizing the unbalance problem when a category node inherited the probability from multiple fathers. The keywords in the web page are extracted from the title and the main text using N-gram with Wikipedia Entity Words, and Bayesian classifier is used to estimate the page class probability. Experimental results showed that the proposed method obtained good scalability, robustness and reliability for massive web pages.

End-user programming environments for the IoT such as IFTTT rely on a multitude of low-level trigger-action rules that categorize devices and services by technology or brand. EUPont is a Semantic Web ontology that enables users to meet their needs with fewer, higher-level rules that can be adapted to different contextual situations and as-yet-unknown IoT devices and services.

When researchers formulate search queries to find relevant content on the Web, those queries typically consist of keywords that can only be matched in the content or its metadata. The Web of Data extends this functionality by bringing structure and giving well-defined meaning to the content and it enables humans and machines to work together using controlled vocabularies. Due the high degree of mismatches between the structure of the content and the vocabularies in different sources, searching over multiple heterogeneous repositories of structured data is considered challenging. Therefore, the authors present a semantic search engine for researchers facilitating search in research related Linked Data. To facilitate high-precision interactive search, they annotated and interlinked structured research data with ontologies from various repositories in an effective semantic model. Furthermore, the authors' system is adaptive as researchers can synchronize using new social media accounts and efficiently explore new datasets.

With the advent of Big Data Era, users prefer to get knowledge rather than pages from Web. Linked Data, a new form of knowledge representation and publishing described by RDF, can provide a more precise and comprehensible semantic structure to satisfy the aforementioned requirement. Further, the SPARQL query language for RDF is the foundation of many current researches about Linked Data querying. However, these SPARQL-based methods cannot fully express the semantics of the query, so they cannot unleash the potential of Linked Data. To fill this gap, this paper designs a new querying method which extends the SPARQL pattern. Firstly, the authors present some new semantic properties for predicates in RDF triples and design a Semantic Matrix for Predicates (SMP). They then establish a well-defined framework for the notion of Semantically-Extended Query Model for the Linked Data (SEQMLD). Moreover, the authors propose some novel algorithms for executing queries by integrating semantic extension into SPARQL pattern. Lastly, experimental results show that the authors' proposal has a good generality and performs better than some of the most representative similarity search methods.

The Semantic Web opens up new opportunities for the data mining research. Semantic Web data is usually represented in the RDF triple format (subject, predicate, object). Large RDF-style Knowledge Bases contain hundreds of millions of RDF triples that represent knowledge in a machine-understandable format. Association rule mining is one of the most effective techniques for detecting frequent patterns. In the context of Semantic Web data mining, most existing methods rely on users intervention that is time-consuming and error-prone due to a large amount of data. Meanwhile, rule quality factors (e.g. support and confidence) usually consider knowledge at the instance-level. Namely, these factors disregard the knowledge embedded at the schema-level. In this paper, we demonstrate that ignoring knowledge encoded at the schema-level negatively impacts the interpretation of discovered rules. We introduce an approach called SWARM (Semantic Web Association Rule Mining) that automatically mines Semantic Association Rules from RDF data. The main achievement of SWARM is to reveal common behavioural patterns associated with knowledge at the instance-level and schema-level. We discuss how to utilize knowledge encoded at the schema-level to add more semantics to the rules. We compare the semantic of rules discovered by SWRAM with one of the latest approaches in this field to show the importance of considering schema-level knowledge. Initial experiments performed on RDF-style Knowledge Bases demonstrate the effectiveness of the proposed approach.

Modern Web search engines still have many limitations: search terms are not disambiguated, search terms in one query cannot be in different languages, the retrieved media items have to be in the same language as the search terms and search results are not integrated across a live stream of different media channels, including TV, online news and social media. The system described in this paper enables all of this by combining a media stream processing architecture with cross-lingual and cross-modal semantic annotation, search and recommendation. All those components were developed in the xLiMe project.

Internet is a common information space populated with many entities (e.g., Internet of Things) with different information system types. Each of them has its own context of how to build and process documents (e.g., form documents). This leads to heterogeneous documents in terms of syntax and semantics, which are difficult to make information fusion from one context to another. To resolve this problem, this paper uses semantic interoperability technique which consists of two automatic stages including consistent data understanding and reasonable data usage. To implement semantic interoperability, this paper proposes a novel automatic tabular document exchange (DocEx) framework comprised of a new tabular document model (TabDoc) and a semantic inference scheme to fit the two stages above respectively. In this TabDoc model, a new Tabular Document Language (DocLang) as a communication medium between users and devices is provided, which is not only an information representation language but also a rule language for semantic inference as well. Abstract sub-tree-based semantic relations constructing the logical structure of a tabular document are separated from their presentational structures, clarifying the relationship between semantic groups (e.g., a cell or a block) with the help of a common dictionary CONEX. Besides, this paper proposes a semantic inference algorithm (SIA) executing the inference procedure on received tabular documents created by a Table Designer system which integrates with SIA. Finally, the proposed framework is applied to the processing of flight ticket booking in a realistic e-business scenario. The results show that the proposed method in this paper improves the performance of information fusion among different information systems on the Internet. Information fusion is realized by consistent interpretation and usage of documents.The proposed DocLang syntax only defines an element and will not restrict user input.Automatic document understanding and processing is implemented by a rule language.TabDoc model contains syntactic and semantic representation for document reusability.Tabdoc rule syntax is used for semantic extraction in a declarative manner.

The synergy of Data Stream Management Systems and Semantic Web applications has steered towards a new paradigm known as Stream Reasoning. The Semantic Web standards for knowledge base modeling and querying, namely RDF, OWL and SPARQL, has extensively been used by the Stream Reasoning community. However, the Semantic Web rule languages, such as SWRL and RIF, have never been used in stream data applications. Instead, different non-Semantic Web rule systems have been approached. Since RIF is primarily intended for exchanging rules among systems, we focused on SWRL applications with stream data. This proves difficult following the SWRL’s open world semantics. To overcome SWRL’s expressivity issues we propose an infrastructure extension, which will enable SWRL reasoning with stream data. Namely, a query processing system, such as C-SPARQL, was layered under SWRL to support closed-world and time-aware reasoning. Moreover, OWLAPI constructs were utilized to enable non-monotonicity, while SPARQL constructs were used to enable negation as failure. Water quality monitoring was used as a validation domain of the proposed system.

Web usage mining is a knowledge discovery technique where a data analyst can discover useful information from the web users’ data. Web contains billions of web pages. The web access behaviour of one web user differs from that of another and also it changes with respect to their temporal property. By analyzing the users’ data, the web administrator can personalize the web pages according to individual web users’ interest. Personalizing the web page gives various advantages in this fast era such as low search time, less data transfer, higher availability of data, lower bandwidth traffic, targeted advertisement and identifying the threaded web users and high web users satisfaction. Due to the above advantages, it is very much essential in the present World Wide Web. Various algorithms, techniques and tools are available in the field of web usage mining. Although there are various techniques, algorithms and tools developed related to web usage mining, new techniques are required to make the discovery of knowledge more accurate. In this paper, a novel technique is proposed by using various methods such as web log, web ranking, web rating and web review based method to identify the success rate of various web pages and summarize the value to identify the accurate success rate of each web page. The success rate is normalized and aggregated into three categories for personalizing the web user. Personalizing the web user based on grouping relevant web access behaviour reduces the calculation complexity. It is very effective in very large websites. This technique is very much effective for analyzing the outreach of web advertisement to the web users.

International audience; This editorial introduces the special issue based on the best papers from ESWC 2015. And since ESWC'15 marked 15 years of Semantic Web research, we extended this editorial to a position paper that reflects the path that we, as a community, traveled so far with the goal of transforming the Web of Pages to a Web of Resources. We discuss some of the key challenges, research topics and trends addressed by the Semantic Web community in its journey. We conclude that the symbiotic relation of our community with the Web requires a truly multidisciplinary research approach to support the Web's diversity.

Architecture, engineering, construction and facility management (AEC-FM) projects involve a large number of participants that must exchange information and combine their knowledge for successful completion of a project. Currently, most of the AEC-FM domains store their information about a project in text documents or use XML, relational, or object-oriented formats that make information integration difficult. The AEC-FM industry is not taking advantage of the full potential of the Semantic Web for streamlining sharing, connecting, and combining information from different domains. The Semantic Web is designed to solve the information integration problem by creating a web of structured and connected data that can be processed by machines. It allows combining information from different sources with different underlying schemas distributed over the Internet. In the Semantic Web, all data instances and data schema are stored in a graph data store, which makes it easy to merge data from different sources. This paper presents a shared ontology approach to semantic representation of building information. The semantic representation of building information facilitates finding and integrating building information distributed in several knowledge bases. A case study demonstrates the development of a semantic based building design knowledge base.

Huge volume of information is available in the WWW. However, the demand is on relevant information rather than available information, which are often heterogeneous and distributed. Agriculture is one such domain, which includes information like soil, crops, weather, etc., under one roof. This information is in different representations and structures e.g. weather. This scenario leads to a challenge that how to integrate the available and heterogeneous agricultural information to deliver better production. The information on the web is syntactically structured but, the need is to provide semantic linkage. The semantic web supports the existing web to easily process and interpret information. In this paper, a semantic based Agricultural Information System (AIS) is proposed which addresses heterogeneity issues among weather systems and integrates information like soil, weather, crop and fertilizers. AIS helps the farmers regarding the type of crop/soil, crop/climate, fertilizer applications, diseases and prevention methods using effective retrieval of information from integrated systems.

The Internet of Things (IoT) vision is to connect uniquely identifiable devices that surround us to the Internet, which is best described through ontologies. Thereby, new emerging technologies such as wireless sensor networks (WSN) are recognized as an essential enabling component of the IoT today. Hence, given the increasing interest to provide linked sensor data through the Web either following the Semantic Web Enablement (SWE) standard or the Linked Data approach, there is a need to also explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture SEMDPA has been developed. It supports linking sensors and other devices, as well as people via a single web by mean of a device-person-activity (DPA) crossroad ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and Linked WSN data. SEMDPA could be easily extensible to capture semantics of input sensor data from other domains as well.

An ontology is a conceptualisation of domain knowledge. It is employed in semantic web services technologies to describe the meanings of services so that they can be dynamically searched for and composed according to their meanings. It is essential for dynamic service discovery, composition, and invocation. Whether an ontology is well constructed has a tremendous impact on the accuracy of the semantic description of a web service, the complexity of the semantic definitions, the efficiency of processing messages passed between services, and the precision and recall rates of service retrieval from service registrations. However, measuring the quality of an ontology remains an open problem. Works on the evaluation of ontologies do exist, but they are not in the context of semantic web services. This paper addresses this problem by proposing a quality model of ontology and defining a set of metrics that enables the quality of an ontology to be measured objectively and quantitatively in the context of semantic descriptions of web services. These metrics cover the contents, presentation, and usage aspects of ontologies. The paper also presents a tool that implements these metrics and reports a case study on five real-life examples of web services.

Wikipedia has become a high coverage knowledge source which has been used in many research areas such as natural language processing, text mining and information retrieval. Several methods have been introduced for extracting explicit or implicit relations from Wikipedia to represent semantics of concepts/words. However, the main challenge in semantic representation is how to incorporate different types of semantic relations to capture more semantic evidences of the associations of concepts. In this article, we propose a semantic concept model that incorporates different types of semantic features extracting from Wikipedia. For each concept that corresponds to an article, four semantic features are introduced: template links, categories, salient concepts and topics. The proposed model is based on the probability distributions that are defined for these semantic features of a Wikipedia concept. The template links and categories are the document-level features which are directly extracted from the structured information included in the article. On the other hand, the salient concepts and topics are corpus-level features which are extracted to capture implicit relations among concepts. For the salient concepts feature, the distributional-based method is utilised on the hypertext corpus to extract this feature for each Wikipedia concept. Then, the probability product kernel is used to improve the weight of each concept in this feature. For the topic feature, the Labelled latent Dirichlet allocation is adapted on the supervised multi-label of Wikipedia to train the probabilistic model of this feature. Finally, we used the linear interpolation for incorporating these semantic features into the probabilistic model to estimate the semantic relation probability of the specific concept over Wikipedia articles. The proposed model is evaluated on 12 benchmark datasets in three natural language processing tasks: measuring the semantic relatedness of concepts/words in general and in the biomedical domain, semantic textual relatedness measurement and measuring the semantic compositionality of noun compounds. The model is also compared with five methods that depends on separate semantic features in Wikipedia. Experimental results show that the proposed model achieves promising results in three tasks and outperforms the baseline methods in most of the evaluation datasets. This implies that incorporation of explicit and implicit semantic features is useful for representing semantics of concepts in Wikipedia.

The increasing performance and wider spread use of automated semantic annotation and entity linking platforms has empowered the possibility of using semantic information in information retrieval. While keyword-based information retrieval techniques have shown impressive performance, the addition of semantic information can increase retrieval performance by allowing for more accurate sense disambiguation, intent determination, and instance identification, just to name a few. Researchers have already delved into the possibility of integrating semantic information into practical search engines using a combination of techniques such as using graph databases, hybrid indices and adapted inverted indices, among others. One of the challenges with the efficient design of a search engine capable of considering semantic information is that it would need to be able to index information beyond the traditional information stored in inverted indices, including entity mentions and type relationships. The objective of our work in this paper is to investigate various ways in which different data structure types can be adopted to integrate three types of information including keywords, entities and types. We will systematically compare the performance of the different data structures for scenarios where (i) the same data structure types are adopted for the three types of information, and (ii) different data structure types are integrated for storing and retrieving the three different information types. We report our findings in terms of the performance of various query processing tasks such as Boolean and ranked intersection for the different indices and discuss which index type would be appropriate under different conditions for semantic search.

Objective: To develop a novel pharmacovigilance inferential framework to infer mechanistic explanations for asserted drug-drug interactions (DDIs) and deduce potential DDIs. Materials and Methods: A mechanism-based DDI knowledge base was constructed by integrating knowledge from several existing sources at the pharmacokinetic, pharmacodynamic, pharmacogenetic, and multipathway interaction levels. A query-based framework was then created to utilize this integrated knowledge base in conjunction with 9 inference rules to infer mechanistic explanations for asserted DDIs and deduce potential DDIs. Results: The drug-drug interactions discovery and demystification (D3) system achieved an overall 85% recall rate in terms of inferring mechanistic explanations for the DDIs integrated into its knowledge base, while demonstrating a 61% precision rate in terms of the inference or lack of inference of mechanistic explanations for a balanced, randomly selected collection of interacting and noninteracting drug pairs. Discussion: The successful demonstration of the D3 system’s ability to confirm interactions involving well-studied drugs enhances confidence in its ability to deduce interactions involving less-studied drugs. In its demonstration, the D3 system infers putative explanations for most of its integrated DDIs. Further enhancements to this work in the future might include ranking interaction mechanisms based on likelihood of applicability, determining the likelihood of deduced DDIs, and making the framework publicly available. Conclusion: The D3 system provides an early-warning framework for augmenting knowledge of known DDIs and deducing unknown DDIs. It shows promise in suggesting interaction pathways of research and evaluation interest and aiding clinicians in evaluating and adjusting courses of drug therapy.

In recent years, Web service discovery has been a hot research topic. In this paper, we propose a novel Web services discovery approach, which can mine the underlying semantic structures of interaction interface parameters to help users find and employ Web services, and can match interfaces with high precision when the parameters of those interfaces contain meaningful synonyms, abbreviations, and combinations of disordered fragments. Our approach is based on mining the underlying semantics. First, we propose a conceptual Web services description model in which we include the type path for the interaction interface parameters in addition to the traditional text description. Then, based on this description model, we mine the underlying semantics of the interaction interface to create index libraries by clustering interaction interface names and fragments under the supervision of co-occurrence probability. This index library can help provide a high-efficiency interface that can match not only synonyms but also abbreviations and fragment combinations. Finally, we propose a Web service Operations Discovery algorithm (OpD). The OpD discovery results include two types of Web services: services with “Single” operations and services with “Composite” operations. The experimental evaluation shows that our approach performs better than other Web service discovery methods in terms of both discovery time and precision/recall rate.

In this paper, we present SeMFIS - a flexible engineering platform for semantic annotations of conceptual models. Conceptual models have in the past been used for many purposes in the context of information systems' engineering. These purposes include for example the elicitation of requirements, the simulation of the behavior of future information systems, the generation of code or the interaction with information systems through models at runtime. Semantic annotations of conceptual models constitute a recently established approach for dynamically extending the semantic representation and semantic analysis scope of conceptual modeling languages. Thereby, elements in conceptual models are linked to concepts in ontologies via anno- tations. Thus, additional knowledge aspects can be represented without modifications of the modeling languages. These aspects can then be analyzed using queries, specifically designed algorithms or external tools and services. At its core, SeMFIS provides a set of meta models for visually representing ontologies and semantic annotations as models. In addition, the tool contains an analysis component, a web service interface, and an import/export component to query and exchange model information. SeM- FIS has been implemented using the freely available ADOxx meta modeling platform. It can thus be easily added to the large variety of other modeling methods based on this platform or used as an additional service for other tools. We present the main features of SeMFIS and briefly discuss use cases where it has been applied. SeMFIS is freely available via the OMiLAB.org website at http://www.omilab.org/web/semfis.