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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.
- ItemSteps towards a Dislocation Ontology for Crystalline Materials(Aachen, Germany : RWTH Aachen, 2021) Ihsan, Ahmad Zainul; Dessì, Danilo; Alam, Mehwish; Sack, Harald; Sandfeld, Stefan; GarcÃa-Castro, Raúl; Davies, John; Antoniou, Grigoris; Fortuna, CarolinaThe field of Materials Science is concerned with, e.g., properties and performance of materials. An important class of materials are crystalline materials that usually contain “dislocations" - a line-like defect type. Dislocation decisively determine many important materials properties. Over the past decades, significant effort was put into understanding dislocation behavior across different length scales both with experimental characterization techniques as well as with simulations. However, for describing such dislocation structures there is still a lack of a common standard to represent and to connect dislocation domain knowledge across different but related communities. An ontology offers a common foundation to enable knowledge representation and data interoperability, which are important components to establish a “digital twin". This paper outlines the first steps towards the design of an ontology in the dislocation domain and shows a connection with the already existing ontologies in the materials science and engineering domain.
- ItemExploring the Impact of Negative Sampling on Patent Citation Recommendation(Paris : CNRS, 2023) Dessi, Rima; Aras, Hidir; Alam, MehwishDue to the increasing number of patents being published every day, patent citation recommendations have become one of the challenging tasks. Since patent citations may lead to legal and economic consequences, patent recommendations are even more challenging as compared to scientific article citations. One of the crucial components of the patent citation algorithm is negative sampling which is also a part of many other tasks such as text classification, knowledge graph completion, etc. This paper, particularly focuses on proposing a transformer-based ranking model for patent recommendations. It further experimentally compares the performance of patent recommendations based on various state-of-the-art negative sampling approaches to measure and compare the effectiveness of these approaches to aid future developments. These experiments are performed on a newly collected dataset of US patents from Google patents.
- ItemOntology Modelling for Materials Science Experiments(Aachen, Germany : RWTH Aachen, 2021) Alam, Mehwish; Birkholz, Henk; Dessì, Danilo; Eberl, Christoph; Fliegl, Heike; Gumbsch, Peter; von Hartrott, Philipp; Mädler, Lutz; Niebel, Markus; Sack, Harald; Thomas, Akhil; Tiddi, Ilaria; Maleshkova, Maria; Pellegrini, Tassilo; de Boer, VictorMaterials are either enabler or bottleneck for the vast majority of technological innovations. The digitization of materials and processes is mandatory to create live production environments which represent physical entities and their aggregations and thus allow to represent, share, and understand materials changes. However, a common standard formalization for materials knowledge in the form of taxonomies, ontologies, or knowledge graphs has not been achieved yet. This paper sketches the e_orts in modelling an ontology prototype to describe Materials Science experiments. It describes what is expected from the ontology by introducing a use case where a process chain driven by the ontology enables the curation and understanding of experiments.
- ItemSpecial issue on conceptual structures(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2022) Alam, Mehwish; Braun, Tanya; Endres, Dominik; Yun, Bruno[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.
- ItemTemporal Evolution of the Migration-related Topics on Social Media(Aachen, Germany : RWTH Aachen, 2021) Chen, Yiyi; Gesese, Genet Asefa; Sack, Harald; Alam, Mehwish; Seneviratne, Oshani; Pesquita, Catia; Sequeda, Juan; Etcheverry, LorenaThis poster focuses on capturing the temporal evolution of migration-related topics on relevant tweets. It uses Dynamic Embedded Topic Model (DETM) as a learning algorithm to perform a quantitative and qualitative analysis of these emerging topics. TweetsKB is extended with the extracted Twitter dataset along with the results of DETM which considers temporality. These results are then further analyzed and visualized. It reveals that the trajectories of the migration-related topics are in agreement with historical events. The source codes are available online: https://bit.ly/3dN9ICB.
- ItemChallenges of Applying Knowledge Graph and their Embeddings to a Real-world Use-case(Aachen, Germany : RWTH Aachen, 2021) Petzold, Rick; Gesese, Genet Asefa; Bogdanova, Viktoria; Zylowski, Thorsten; Sack, Harald; Alam, Mehwish; Alam, Mehwish; Buscaldi, Davide; Cochez, Michael; Osborne, Francesco; Reforgiato Recupero, Diego; Sack, HaraldDifferent Knowledge Graph Embedding (KGE) models have been proposed so far which are trained on some specific KG completion tasks such as link prediction and evaluated on datasets which are mainly created for such purpose. Mostly, the embeddings learnt on link prediction tasks are not applied for downstream tasks in real-world use-cases such as data available in different companies/organizations. In this paper, the challenges with enriching a KG which is generated from a real-world relational database (RDB) about companies, with information from external sources such as Wikidata and learning representations for the KG are presented. Moreover, a comparative analysis is presented between the KGEs and various text embeddings on some downstream clustering tasks. The results of experiments indicate that in use-cases like the one used in this paper, where the KG is highly skewed, it is beneficial to use text embeddings or language models instead of KGEs.
- ItemAnalyzing social media for measuring public attitudes toward controversies and their driving factors: a case study of migration(Wien : Springer, 2022) Chen, Yiyi; Sack, Harald; Alam, MehwishAmong other ways of expressing opinions on media such as blogs, and forums, social media (such as Twitter) has become one of the most widely used channels by populations for expressing their opinions. With an increasing interest in the topic of migration in Europe, it is important to process and analyze these opinions. To this end, this study aims at measuring the public attitudes toward migration in terms of sentiments and hate speech from a large number of tweets crawled on the decisive topic of migration. This study introduces a knowledge base (KB) of anonymized migration-related annotated tweets termed as MigrationsKB (MGKB). The tweets from 2013 to July 2021 in the European countries that are hosts of immigrants are collected, pre-processed, and filtered using advanced topic modeling techniques. BERT-based entity linking and sentiment analysis, complemented by attention-based hate speech detection, are performed to annotate the curated tweets. Moreover, external databases are used to identify the potential social and economic factors causing negative public attitudes toward migration. The analysis aligns with the hypothesis that the countries with more migrants have fewer negative and hateful tweets. To further promote research in the interdisciplinary fields of social sciences and computer science, the outcomes are integrated into MGKB, which significantly extends the existing ontology to consider the public attitudes toward migrations and economic indicators. This study further discusses the use-cases and exploitation of MGKB. Finally, MGKB is made publicly available, fully supporting the FAIR principles.
- ItemEditorial of the Special issue on Cultural heritage and semantic web(Amsterdam : IOS Press, 2022) Alam, Mehwish; de Boer, Victor; Daga, Enrico; van Erp, Marieke; Hyvönen, Eero; Meroño-Peñuela, Albert[no abstract available]