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- 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.
- ItemUnderstanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification(Aachen, Germany : RWTH Aachen, 2021) Hoppe, Fabian; Dessì, Danilo; Sack, Harald; Alam, Mehwish; Buscaldi, Davide; Cochez, Michael; Osborne, Francesco; Reforgiato Recupero, Diego; Sack, HaraldFrequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance.
- ItemImproving Zero-Shot Text Classification with Graph-based Knowledge Representations(Aachen, Germany : RWTH Aachen, 2022) Hoppe, Fabian; Hartig, Olaf; Seneviratne, OshaniInsufficient training data is a key challenge for text classification. In particular, long-tail class distributions and emerging, new classes do not provide any training data for specific classes. Therefore, such a zeroshot setting must incorporate additional, external knowledge to enable transfer learning by connecting the external knowledge of previously unseen classes to texts. Recent zero-shot text classifier utilize only distributional semantics defined by large language models and based on class names or natural language descriptions. This implicit knowledge contains ambiguities, is not able to capture logical relations nor is it an efficient representation of factual knowledge. These drawbacks can be avoided by introducing explicit, external knowledge. Especially, knowledge graphs provide such explicit, unambiguous, and complementary, domain specific knowledge. Hence, this thesis explores graph-based knowledge as additional modality for zero-shot text classification. Besides a general investigation of this modality, the influence on the capabilities of dealing with domain shifts by including domain-specific knowledge is explored.
- ItemTemporal Role Annotation for Named Entities(Amsterdam [u.a.] : Elsevier, 2018) Koutraki, Maria; Bakhshandegan-Moghaddam, Farshad; Sack, Harald; Fensel, Anna; de Boer, Victor; Pellegrini, Tassilo; Kiesling, Elmar; Haslhofer, Bernhard; Hollink, Laura; Schindler, AlexanderNatural language understanding tasks are key to extracting structured and semantic information from text. One of the most challenging problems in natural language is ambiguity and resolving such ambiguity based on context including temporal information. This paper, focuses on the task of extracting temporal roles from text, e.g. CEO of an organization or head of a state. A temporal role has a domain, which may resolve to different entities depending on the context and especially on temporal information, e.g. CEO of Microsoft in 2000. We focus on the temporal role extraction, as a precursor for temporal role disambiguation. We propose a structured prediction approach based on Conditional Random Fields (CRF) to annotate temporal roles in text and rely on a rich feature set, which extracts syntactic and semantic information from text. We perform an extensive evaluation of our approach based on two datasets. In the first dataset, we extract nearly 400k instances from Wikipedia through distant supervision, whereas in the second dataset, a manually curated ground-truth consisting of 200 instances is extracted from a sample of The New York Times (NYT) articles. Last, the proposed approach is compared against baselines where significant improvements are shown for both datasets.
- 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.
- 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.
- ItemFurther with Knowledge Graphs. Proceedings of the 17th International Conference on Semantic Systems(Berlin : AKA ; Amsterdam : IOS Press, 2021) Alam, Mehwish; Groth, Paul; de Boer, Victor; Pellegrini, Tassilo; Pandit, Harshvardhan J.; Montiel, Elena; Rodríguez-Doncel, Victor; McGillivray, Barbara; Meroño-Peñuela, AlbertThe field of semantic computing is highly diverse, linking areas such as artificial intelligence, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, and enterprise vocabulary management. As such it forms an essential part of the computing technology that underpins all our lives today. This volume presents the proceedings of SEMANTiCS 2021, the 17th International Conference on Semantic Systems. As a result of the continuing Coronavirus restrictions, SEMANTiCS 2021 was held in a hybrid form in Amsterdam, the Netherlands, from 6 to 9 September 2021. The annual SEMANTiCS conference provides an important platform for semantic computing professionals and researchers, and attracts information managers, ITarchitects, software engineers, and researchers from a wide range of organizations, such as research facilities, NPOs, public administrations and the largest companies in the world. The subtitle of the 2021 conference’s was “In the Era of Knowledge Graphs”, and 66 submissions were received, from which the 19 papers included here were selected following a rigorous single-blind reviewing process; an acceptance rate of 29%. Topics covered include data science, machine learning, logic programming, content engineering, social computing, and the Semantic Web, as well as the additional sub-topics of digital humanities and cultural heritage, legal tech, and distributed and decentralized knowledge graphs. Providing an overview of current research and development, the book will be of interest to all those working in the field of semantic systems.
- 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]
- ItemProceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2021)(Aachen : RWTH Aachen, 2021) Alam, Mehwish; Buscaldi, Davide; Cochez, Michael; Osborne, Francesco; Reforgiato Recupero, Diego, Sack, Harald[no abstract available]
- ItemSteps towards a Dislocation Ontology for Crystalline Materials(Aachen, Germany : RWTH Aachen, 2021) Ihsan, Ahmad Zainul; Dessì, Danilo; Alam, Mehwish; Sack, Harald; Sandfeld, Stefan; García-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.