Forthcoming and Online First Articles

International Journal of Metadata, Semantics and Ontologies

International Journal of Metadata, Semantics and Ontologies (IJMSO)

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International Journal of Metadata, Semantics and Ontologies (6 papers in press)

Regular Issues

  • A fuzzy logic and ontology based approach for improving the CV and job offer matching in recruitment process   Order a copy of this article
    by Amine Habous, El Habib Nfaoui 
    Abstract: The recruitment process is a critical activity for every organisation, it allows to find the appropriate candidate for a job offer and its employer work criteria. The competitive nature of the recruitment environment makes the task of hiring new employees very hard for companies owing to the high number of CV (resume) and profiles to process, the personal job interests, the customised requirements and precise skills requested by employees, etc. The time becomes crucial for recruiters' choices; consequently, it might impact the selection process quality. In this paper, we propose a retrieval system for automating the recruitment process. It is designed based on natural language processing, machine learning, and fuzzy logic to handle the matching between the job description and the CVs. It also considers the proficiency level for the technical skills. Moreover, it offers an estimation of the overall CV/Job offer expertise level. In that way, it overcomes the under-qualification and over-qualification issue in the ICT process. Experimental results on a ground-truth data of a recruiter company demonstrate that our proposal provides effective results.
    Keywords: text mining; natural language processing; feature extraction; metadata weighting; ICT recruitment; fuzzy logic; machine learning.

  • Systematic design and implementation of a semantic assistance system for aero-engine design and manufacturing   Order a copy of this article
    by Sonika Gogineni, Jörg Brünnhäußer, Jonas Nickel, Heiko Witte, Rainer Stark, Kai Lindow, Erik Konietzko 
    Abstract: Data in organisations is often spread across various information and communication technology (ICT) systems, leading to redundancies, lack of overview and time loss in searching for information while carrying out daily activities. This paper focuses on addressing these problems by using semantic technologies to design and develop an assistance system on existing infrastructure. The focus here is set around the aero-engine industry where complex data systems are common, but also a lot of unstructured data and information become available during production. A systematic approach is followed to design the system, which integrates data silos by using a common ontology. This paper highlights the problems being addressed, the approach selected to develop the system and implementation of two use cases to support user activities in an aerospace company.
    Keywords: knowledge management; assistance system; semantic integration; machine learning; ontologies; industrial implementation; manufacturing; product data management; Heterogeneous data; Interoperability.

  • Keyphrase extraction from single textual documents based on semantically defined background knowledge and co-occurrence graphs   Order a copy of this article
    by Mauro Dalle Lucca Tosi, Julio Cesar Dos Reis 
    Abstract: The keyphrase extraction task is a fundamental and challenging task designed to automatically extract a set of keyphrases from textual documents. Keyphrases are fundamental to assist publishers in indexing documents and readers in identifying the most relevant ones. They are short phrases composed of one or more terms used to best represent a textual document and its main topics. In this article, we extend our research on C-Rank, an unsupervised approach that automatically extracts keyphrases from single documents. C-Rank uses a concept-linking approach that links concepts in common between single documents and an external background knowledge base. Our approach uses those concepts as candidate keyphrases, which are modelled in a co-occurrence graph. On this basis, keyphrases are extracted relying on heuristics and their centrality in the graph. We advance our study over C-Rank by evaluating it using different concept-linking approaches - Babelfy and DBPedia Spotlight. The evaluation was performed in five gold-standard datasets composed of distinct types of data - academic articles, academic abstracts, and news articles. Our findings indicate that C-Rank achieves state-of-the-art results extracting keyphrases from scientific documents by experimentally comparing it with other unsupervised existing approaches.
    Keywords: keyphrase extraction; complex networks; semantic annotation.

  • Links between research artifacts: use cases for digital libraries   Order a copy of this article
    by Fidan Limani, Atif Latif, Klaus Tochtermann 
    Abstract: The generation and availability of links between scholarly resources continue to increase. Initiatives to support it - both in terms of a (standard) representation model and accompanying infrastructure for collection and exchange - make this emerging artifact interesting to explore. Its role towards a more transparent, reproducible, and, ultimately, richer research context, makes it a valuable proposition for information infrastructures such as digital libraries. In this paper, we assess the potential of link artifacts for such an environment. We rely on a public link collection subset of >4.8 M links, which we represent based on the Linked Data approach that results with a collection of >163.8 M RDF triples. The incorporated use cases demonstrate the usefulness of this artifact in this study. We claim that the adoption of links extends the scholarly data collection and advances the services that a digital library offers to its users.
    Keywords: research artifact links; digital library; semantic web.

  • Ontology-based knowledge management in income tax of Nepal   Order a copy of this article
    by Shib Raj Bhatta, Bhoj Raj Ghimire, Marut Buranarach 
    Abstract: Organizational knowledge management in government agencies is crucial to create a common understanding about data, systems and procedures for employees and external stakeholders. The main purpose of this work is to create a knowledge repository for income tax of Nepal using domain ontology. To create this ontology, we have used 'methontology', a broadly used ontology development methodology. After the knowledge acquisition, competency questions were created and conceptualization was specified. On the basis of this, a class hierarchy, object properties and data propertied are identified and specified. Further, an ontology was created by using the Prot
    Keywords: ontology; income tax; knowledge management; tax ontology; knowledge repository.

  • Introducing a novel bi-functional method for exploiting sentiment in complex information networks   Order a copy of this article
    by Paraskevas Koukaras, Dimitris Rousidis, Christos Tjortjis 
    Abstract: This paper elaborates on multilayer Information Network (IN) modelling, using graph mining and machine learning. Although Social Media (SM) INs may be modelled as homogeneous networks, real-world networks contain multi-typed entities, characterised by complex relations and interactions posing as heterogeneous INs. For mining data whilst retaining semantic context in such complex structures, we need better ways for handling multi-typed and interconnected data. This work conceives and performs several simulations on SM data. The first simulation models information, based on a bi-partite network schema. The second simulation uses a star network schema, along with a graph database offering querying for graph metrics. The third simulation handles data from the previous simulations to generate a multilayer IN. It proposes a novel bi-functional method for sentiment extraction of user reviews/opinions across multiple SM platforms, considering the concepts of supervised/unsupervised learning and sentiment analysis.
    Keywords: linked data; multi-layer information networks; graph modelling; social media; NoSQL; data mining; machine Learning; supervised/unsupervised learning; graph metrics; bi-functional algorithms.