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- ItemLabour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners(Setúbal, Portugal : Science and Technology Publications, Lda, 2020) Tavakoli, Mohammadreza; Mol, Stefan; Kismihók, Gábor; Lane, H. Chad; Zvacek, Susan; Uhomoibhi, JamesIn this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as us eful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.
- ItemAn OER Recommender System Supporting Accessibility Requirements(New York : Association for Computing Machinery, 2020) Elias, Mirette; Tavakoli, Mohammadreza; Lohmann, Steffen; Kismihok, Gabor; Auer, Sören; Gurreiro, Tiago; Nicolau, Hugo; Moffatt, KarynOpen Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.
- ItemModelling Archival Hierarchies in Practice: Key Aspects and Lessons Learned(Aachen, Germany : RWTH Aachen, 2021) Vafaie, Mahsa; Bruns, Oleksandra; Pilz, Nastasja; Dessì, Danilo; Sack, Harald; Sumikawa, Yasunobu; Ikejiri, Ryohei; Doucet, Antoine; Pfanzelter, Eva; Hasanuzzaman, Mohammed; Dias, Gaël; Milligan, Ian; Jatowt, AdamAn increasing number of archival institutions aim to provide public access to historical documents. Ontologies have been designed, developed and utilised to model the archival description of historical documents and to enable interoperability between different information sources. However, due to the heterogeneous nature of archives and archival systems, current ontologies for the representation of archival content do not always cover all existing structural organisation forms equallywell. After briefly contextualising the heterogeneity in the hierarchical structure of German archives, this paper describes and evaluates differences between two archival ontologies, ArDO and RiC-O, and their approaches to modelling hierarchy levels and archive dynamics.
- ItemDDB-KG: The German Bibliographic Heritage in a Knowledge Graph(Aachen, Germany : RWTH Aachen, 2021) Tan, Mary Ann; Tietz, Tabea; Bruns, Oleksandra; Oppenlaender, Jonas; Dessì, Danilo; Harald, Sack; Sumikawa, Yasunobu; Ikejiri, Ryohei; Doucet, Antoine; Pfanzelter, Eva; Hasanuzzaman, Mohammed; Dias, Gaël; Milligan, Ian; Jatowt, AdamUnder the German government’s initiative “NEUSTART Kultur”, the German Digital Library or Deutsche Digitale Bibliothek (DDB) is undergoing improvements to enhance user-experience. As an initial step, emphasis is placed on creating a knowledge graph from the bibliographic record collection of the DDB. This paper discusses the challenges facing the DDB in terms of retrieval and the solutions in addressing them. In particular, limitations of the current data model or ontology to represent bibliographic metadata is analyzed through concrete examples. This study presents the complete ontological mapping from DDB-Europeana Data Model (DDB-EDM) to FaBiO, and a prototype of the DDB-KG made available as a SPARQL endpoint. The suitabiliy of the target ontology is demonstrated with SPARQL queries formulated from competency questions.
- ItemQuestion Answering on Scholarly Knowledge Graphs(Cham : Springer, 2020) Jaradeh, Mohamad Yaser; Stocker, Markus; Auer, Sören; Hall, Mark; Merčun, Tanja; Risse, Thomas; Duchateau, FabienAnswering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publications. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Such tables can be found in a variety of shapes in the scholarly literature (e.g., surveys, comparisons or results). Our system can retrieve direct answers to a variety of different questions asked on tabular data in articles. Furthermore, we present a preliminary dataset of related tables and a corresponding set of natural language questions. This dataset is used as a benchmark for our system and can be reused by others. Additionally, JarvisQA is evaluated on two datasets against other baselines and shows an improvement of two to three folds in performance compared to related methods.
- ItemQuality Prediction of Open Educational Resources A Metadata-based Approach(Piscataway, NJ : IEEE, 2020) Tavakoli, Mohammadreza; Elias, Mirette; Kismihók, Gábor; Auer, Sören; Chang, Maiga; Sampson, Demetrios G.; Huang, Ronghuai; Hooshyar, Danial; Chen, Nian-Shing; Kinshuk; Pedaste, MargusIn the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%. © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
- ItemOntology Design for Pharmaceutical Research Outcomes(Cham : Springer, 2020) Say, Zeynep; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören; Hall, Mark; Merčun, Tanja; Risse, Thomas; Duchateau, FabienThe network of scholarly publishing involves generating and exchanging ideas, certifying research, publishing in order to disseminate findings, and preserving outputs. Despite enormous efforts in providing support for each of those steps in scholarly communication, identifying knowledge fragments is still a big challenge. This is due to the heterogeneous nature of the scholarly data and the current paradigm of distribution by publishing (mostly document-based) over journal articles, numerous repositories, and libraries. Therefore, transforming this paradigm to knowledge-based representation is expected to reform the knowledge sharing in the scholarly world. Although many movements have been initiated in recent years, non-technical scientific communities suffer from transforming document-based publishing to knowledge-based publishing. In this paper, we present a model (PharmSci) for scholarly publishing in the pharmaceutical research domain with the goal of facilitating knowledge discovery through effective ontology-based data integration. PharmSci provides machine-interpretable information to the knowledge discovery process. The principles and guidelines of the ontological engineering have been followed. Reasoning-based techniques are also presented in the design of the ontology to improve the quality of targeted tasks for data integration. The developed ontology is evaluated with a validation process and also a quality verification method.
- ItemToward Representing Research Contributions in Scholarly Knowledge Graphs Using Knowledge Graph Cells(New York City, NY : Association for Computing Machinery, 2020) Vogt, Lars; D'Souza, Jennifer; Stocker, Markus; Auer, SörenThere is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. Toward this end, in this work, we propose a novel semantic data model for modeling the contribution of scientific investigations. Our model, i.e. the Research Contribution Model (RCM), includes a schema of pertinent concepts highlighting six core information units, viz. Objective, Method, Activity, Agent, Material, and Result, on which the contribution hinges. It comprises bottom-up design considerations made from three scientific domains, viz. Medicine, Computer Science, and Agriculture, which we highlight as case studies. For its implementation in a knowledge graph application we introduce the idea of building blocks called Knowledge Graph Cells (KGC), which provide the following characteristics: (1) they limit the expressibility of ontologies to what is relevant in a knowledge graph regarding specific concepts on the theme of research contributions; (2) they are expressible via ABox and TBox expressions; (3) they enforce a certain level of data consistency by ensuring that a uniform modeling scheme is followed through rules and input controls; (4) they organize the knowledge graph into named graphs; (5) they provide information for the front end for displaying the knowledge graph in a human-readable form such as HTML pages; and (6) they can be seamlessly integrated into any existing publishing process thatsupports form-based input abstracting its semantic technicalities including RDF semantification from the user. Thus RCM joins the trend of existing work toward enhanced digitalization of scholarly publication enabled by an RDF semantification as a knowledge graph fostering the evolution of the scholarly publications beyond written text.
- ItemFunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation(Cham : Springer, 2020) Jozashoori, Samaneh; Chaves-Fraga, David; Iglesias, Enrique; Vidal, Maria-Esther; Corcho, Oscar; Pan, Jeff Z.; Tamma, Valentina; d'Amato, Claudia; Janowicz, Kryztof; Fu, Bo; Polleres, Axel; Seneviratne, Oshani; Kagal, LalanaData has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions – specified with function-based mapping languages like FunUL and RML+FnO – can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.
- ItemOn the Impact of Temporal Representations on Metaphor Detection(Paris : European Language Resources Association (ELRA), 2022) Giorgio Ottolina; Matteo Palmonari; Manuel Vimercati; Mehwish Alam; Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan; Piperidis, SteliosState-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.