Browsing by Author "Paulheim, Heiko"
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- ItemContextual Language Models for Knowledge Graph Completion(Aachen, Germany : RWTH Aachen, 2021) Russa, Biswas; Sofronova, Radina; Alam, Mehwish; Sack, Harald; Mehwish, Alam; Ali, Medi; Groth, Paul; Hitzler, Pascal; Lehmann, Jens; Paulheim, Heiko; Rettinger, Achim; Sack, Harald; Sadeghi, Afshin; Tresp, VolkerKnowledge Graphs (KGs) have become the backbone of various machine learning based applications over the past decade. However, the KGs are often incomplete and inconsistent. Several representation learning based approaches have been introduced to complete the missing information in KGs. Besides, Neural Language Models (NLMs) have gained huge momentum in NLP applications. However, exploiting the contextual NLMs to tackle the Knowledge Graph Completion (KGC) task is still an open research problem. In this paper, a GPT-2 based KGC model is proposed and is evaluated on two benchmark datasets. The initial results obtained from the _ne-tuning of the GPT-2 model for triple classi_cation strengthens the importance of usage of NLMs for KGC. Also, the impact of contextual language models for KGC has been discussed.
- ItemA Knowledge Graph for Industry 4.0(Cham : Springer, 2020) Bader, Sebastian R.; Grangel-Gonzalez, Irlan; Nanjappa, Priyanka; Vidal, Maria-Esther; Maleshkova, Maria; Harth, Andreas; Kirrane, Sabrina; Ngonga Ngomo, Axel-Cyrille; Paulheim, Heiko; Rula, Anisa; Gentile, Anna Lisa; Haase, Peter; Cochez, MichaelOne of the most crucial tasks for today’s knowledge workers is to get and retain a thorough overview on the latest state of the art. Especially in dynamic and evolving domains, the amount of relevant sources is constantly increasing, updating and overruling previous methods and approaches. For instance, the digital transformation of manufacturing systems, called Industry 4.0, currently faces an overwhelming amount of standardization efforts and reference initiatives, resulting in a sophisticated information environment. We propose a structured dataset in the form of a semantically annotated knowledge graph for Industry 4.0 related standards, norms and reference frameworks. The graph provides a Linked Data-conform collection of annotated, classified reference guidelines supporting newcomers and experts alike in understanding how to implement Industry 4.0 systems. We illustrate the suitability of the graph for various use cases, its already existing applications, present the maintenance process and evaluate its quality.
- ItemMachine Learning with Symbolic Methods and Knowledge Graphs(Aachen : RWTH Aachen, 2021) Alam, Mehwish; Ali, Mehdi; Groth, Paul; Hitzler, Pascal; Lehmann, Jens; Paulheim, Heiko; Rettinger, Achim; Sack, Harald; Sadeghi, Afshi; Tresp, Volker[no abstract available]
- ItemTowards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection(New York,NY,United States : Association for Computing Machinery, 2022) Alam, Mehwish; Iana, Andreea; Grote, Alexander; Ludwig, Katharina; Müller, Philipp; Paulheim, Heiko; Laforest, Frédérique; Troncy, Raphael; Médini, Lionel; Herman, IvanNews recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.