Ontology has been increasingly implemented to facilitate the Internet of Things (IoT) activities, such as tracking and information discovery, storage, information exchange, and object addressing. However, a complete understanding of using ontology in the IoT mechanism remains lacking. The main goal of this research is to recognize the use of ontology in the IoT process and investigate the services of ontology in IoT activities. A systematic literature review (SLR) is conducted using predefined protocols to analyze the literature about the usage of ontologies in IoT. The following conclusions are obtained from the SLR. (1) Primary studies (i.e., selected 115 articles) have addressed the need to use ontologies in IoT for industries and the academe, especially to minimize interoperability and integration of IoT devices. (2) About 31.30% of extant literature discussed ontology development concerning the IoT interoperability issue, while IoT privacy and integration issues are partially discussed in the literature. (3) IoT styles of modeling ontologies are diverse, whereas 35.65% of total studies adopted the OWL style. (4) The 32 articles (i.e., 27.83% of the total studies) reused IoT ontologies to handle diverse IoT methodologies. (5) A total of 45 IoT ontologies are well acknowledged, but the IoT community has widely utilized none. An in-depth analysis of different IoT ontologies suggests that the existing ontologies are beneficial in designing new IoT ontology or achieving three main requirements of the IoT field: interoperability, integration, and privacy. This SLR is finalized by identifying numerous validity threats and future directions.

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Applications of Ontology in the Internet of Things: A Systematic Analysis

euskaraErabiltzailearen zereginak errazten dituzten sistema informatiko ugari erabiltzen dira, bai arlo profesionalean, bai eta arlo pertsonalean ere. Hala ere, kasu batzuetan erabiltzaileen beharren eta sistema informatikoak eskaintzen duenaren arteko distantzia handia da. Artikulu honetan web-scraping teknikarekin sortutako bi azpiegitura informatiko deskribatu dira, jatorrizko beste azpiegitura batzuen funtzionalitatea hobetu dutenak. Alde batetik, Xerka Online aplikazioak ikertzaileen curriculum vitaearen (CVaren) sortze- eta mantentze-lana errazten du, ikertzaileek egin behar izaten duten ataza nagusia modu automatizatuan eginez: argitalpenak bilatu eta haiei dagozkien kalitate-adierazle (eragin-faktore eta aipamen kopuru) eguneratuak ezarri. Minerva aplikazioak, ordea, Vitoria-Gasteizko Ingeniaritza Eskolan egiten diren kalitate-txostenak kudeatzen ditu. Horretarako, Euskal Herriko Unibertsitateko (UPV/EHUko) GAUR web-aplikaziotik automatikoki jaisten ditu itxitako aktak, jarritako kalifikazioen estatistikak kalkulatzen ditu, eta maila ezberdinetan egiten diren txostenak batzen ditu. Bi aplikazioen abantaila nagusiak lan horiek egiteko behar den denboraren eta giza akatsen murrizpena dira. EnglishComputational systems that facilitate the tasks of the customer are frequently used, both for professional and personal purposes. However, in some cases, the computer system does not meet the users’ needs. In this article, two computational infrastructures based on the use of web-scraping are described, which improve the functionality of the original infrastructures. Xerka Online allows the creation and maintenance of a researcher’s curriculum vitae (CV) by searching his/her publications and their updated quality indicators (impact factor and cites). Minerva manages the quality assessment reports in the Faculty of Engineering of Vitoria-Gasteiz. To that end, it downloads the grade records from GAUR (a web-application of the University of the Basque Country UPV/EHU), calculates statistics, and merges reports generated in different levels of the quality assessment process. The main advantages of these applications are time reduction and avoidance of human errors.

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Web-Scraping Teknikan Oinarritutako Azpiegitura Informatikoak. Xerka Online eta Minerva aplikazioak

Artificial Intelligence in Information Retrieval: forty years on

In healthcare, organisational changes are suggested and implemented routinely as an answer to increasing patient demands and financial restrictions. Computer simulations offer a way to test these n ...

Specifying an ontology framework to model processes in hospitals

In database development, a conceptual model is created, in the form of an Entity-relationship(ER) model, and transformed to a relational database schema (RDS) to create the database. However, some important information represented on the ER model may not be transformed and represented on the RDS. This situation causes a loss of information during the transformation process. With a view to preserving information, in our previous study, we standardized the transformation process as a one-to-one and onto mapping from the ER model to the RDS. For this purpose, we modified the ER model and the transformation algorithm resolving some deficiencies existed in them. Since the mapping was established using a few real-world cases as a basis and for verification purposes, a formal-proof is necessary to validate the work. Thus, the ongoing research aiming to create a proof will show how a given ER model can be partitioned into a unique set of segments and use it to represent the ER model itself. How the findings can be used to complete the proof in the future will also be explained. Significance of the research on automating database development, teaching conceptual modeling, and using formal methods will also be discussed.

ER model Partitioning: Towards Trustworthy Automated Systems Development

Ontology engineering is an important aspect of semantic web vision to attain the meaningful representation of data. Although various techniques exist for the creation of ontology, most of the methods involve the number of complex phases, scenario-dependent ontology development, and poor validation of ontology. This research work presents a lightweight approach to build domain ontology using Entity Relationship (ER) model. Firstly, a detailed analysis of intended domain is performed to develop the ER model. In the next phase, ER to ontology (EROnt) conversion rules are outlined, and finally the system prototype is developed to construct the ontology. The proposed approach investigates the domain of information technology curriculum for the successful interpretation of concepts, attributes, relationships of concepts and constraints among the concepts of the ontology. The experts’ evaluation of accurate identification of ontology vocabulary shows that the method performed well on curriculum data with 95.75% average precision and 90.75% average recall.

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A Methodology for Engineering Domain Ontology using Entity Relationship Model

HTML pages contain unstructured and diverse information. However, these documents lack semantics and are not machine understandable. Semantic webs aim to add formal semantics to web data, whereas ontology provides formal semantics to a domain and is thus considered a foundation of semantic webs. Domain ontologies can be constructed manually, but this process is tedious and inefficient. Thus, this study presents an ontology learning (OL) model to create domain ontologies automatically from a set of HTML pages. The key insight of this research is that it combines the list structure and headings of HTML pages to recognize the ontology vocabulary. The approach also incorporates synonym relationships with ontology and allows the semantic interpretation of ontology concepts. We implement the proposed OL approach to build sports ontology from a collection of sports domain HTML documents. The new sports ontology is tested using FaCT++ reasoner; results show no inconsistency in the ontology. Furthermore, experts evaluate the successful mapping of HTML lists and headings to the ontology vocabulary. The proposed OL approach performs effectively and achieves 92.7% and 95.4% precision values for list and heading mapping, respectively.

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Using Combined List Hierarchy and Headings of HTML Documents for Learning Domain-Specific Ontology

Retrieving user-relevant content from a large volume of data available on the Web via an input query is a difficult task. A user query may not be able to specify user information needs due to the ambiguous and limited number of query terms. The semantic query expansion (QE) strategy offers a solution to this problem by expanding the query with additional terms, which are semantically similar to the original query. However, this strategy does not consider individual user interest in the generation of expansion terms. In this article, semantic QE is improved by combining the notion of ontology knowledge and user interest. The proposed semantic QE technique involves a computing domain of the input query via ontology, generates expansion terms from the user browsing history, and finally selects expansion terms that represent user preferences on the basis of the semantic similarity between expansion terms and query and user feedback. The experimental evaluation indicates that expanded queries produced by the proposed technique retrieve more personalized contents over Web search than initial user queries. The obtained results achieve 86.4% average precision, which proves a positive impact of incorporating user preferences in semantic QE.

Sehrish Raza

Papers

A Methodology for Engineering Domain Ontology using Entity Relationship Model

Using Combined List Hierarchy and Headings of HTML Documents for Learning Domain-Specific Ontology

User interest driven semantic query expansion for effective web search