Search Results
Temporal Role Annotation for Named Entities
2018, Koutraki, Maria, Bakhshandegan-Moghaddam, Farshad, Sack, Harald, Fensel, Anna, de Boer, Victor, Pellegrini, Tassilo, Kiesling, Elmar, Haslhofer, Bernhard, Hollink, Laura, Schindler, Alexander
Natural 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.
Steps towards a Dislocation Ontology for Crystalline Materials
2021, Ihsan, Ahmad Zainul, Dessì, Danilo, Alam, Mehwish, Sack, Harald, Sandfeld, Stefan, GarcÃa-Castro, Raúl, Davies, John, Antoniou, Grigoris, Fortuna, Carolina
The 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.
DDB-EDM to FaBiO: The Case of the German Digital Library
2021, Tan, Mary Ann, Tietz, Tabea, Bruns, Oleksandra, Oppenlaender, Jonas, Dessì, Danilo, Sack, Harald, Seneviratne, Oshani, Pesquita, Catia, Sequeda, Juan, Etcheverry, Lorena
Cultural heritage portals have the goal of providing users with seamless access to all their resources. This paper introduces initial efforts for a user-oriented restructuring of the German Digital Library (DDB). At present, cultural heritage objects (CHOs) in the DDB are modeled using an extended version of the Europeana Data Model (DDBEDM), which negatively impacts usability and exploration. These challenges can be addressed by leveraging ontologies, and building a knowledge graph from the DDB's voluminous collection. Towards this goal, an alignment of bibliographic metadata from DDB-EDM to FRBR-Aligned Bibliographic Ontology (FaBiO) is presented.
Temporal Evolution of the Migration-related Topics on Social Media
2021, Chen, Yiyi, Gesese, Genet Asefa, Sack, Harald, Alam, Mehwish, Seneviratne, Oshani, Pesquita, Catia, Sequeda, Juan, Etcheverry, Lorena
This 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.
Contextual Language Models for Knowledge Graph Completion
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, Volker
Knowledge 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.
Designing Intelligent Systems for Online Education: Open Challenges and Future Directions
2021, Dessì, Danilo, Käser, Tanja, Marras, Mirko, Popescu, Elvira, Sack, Harald, Dessì, Danilo, Käser, Tanja, Marras, Mirko, Popescu, Elvira, Sack, Harald
The design and delivering of platforms for online education is fostering increasingly intense research. Scaling up education online brings new emerging needs related with hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely, as examples. However, with the impressive progress of the data mining and machine learning fields, combined with the large amounts of learning-related data and high-performance computing, it has been possible to gain a deeper understanding of the nature of learning and teaching online. Methods at the analytical and algorithmic levels are constantly being developed and hybrid approaches are receiving an increasing attention. Recent methods are analyzing not only the online traces left by students a posteriori, but also the extent to which this data can be turned into actionable insights and models, to support the above needs in a computationally efficient, adaptive and timely way. In this paper, we present relevant open challenges lying at the intersection between the machine learning and educational communities, that need to be addressed to further develop the field of intelligent systems for online education. Several areas of research in this field are identified, such as data availability and sharing, time-wise and multi-modal data modelling, generalizability, fairness, explainability, interpretability, privacy, and ethics behind models delivered for supporting education. Practical challenges and recommendations for possible research directions are provided for each of them, paving the way for future advances in this field.
Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification
2021, Hoppe, Fabian, Dessì, Danilo, Sack, Harald, Alam, Mehwish, Buscaldi, Davide, Cochez, Michael, Osborne, Francesco, Reforgiato Recupero, Diego, Sack, Harald
Frequently, 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.
Modelling Archival Hierarchies in Practice: Key Aspects and Lessons Learned
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, Adam
An 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.
Ontology Modelling for Materials Science Experiments
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, Victor
Materials 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.
Towards a Representation of Temporal Data in Archival Records: Use Cases and Requirements
2021, Bruns, Oleksandra, Tietz, Tabea, Vafaie, Mahsa, Dessì, Danilo, Sack, Harald, Lopes, Carla Teixeira, Ribeiro, Cristina, Niccolucci, Franco, Rodrigues, Irene, Freire, Nuno
Archival records are essential sources of information for historians and digital humanists to understand history. For modern information systems they are often analysed and integrated into Knowledge Graphs for better access, interoperability and re-use. However, due to restrictions of the representation of RDF predicates temporal data within archival records is a challenge to model. This position paper explains requirements for modeling temporal data in archival records based on running research projects in which archival records are analysed and integrated in Knowledge Graphs for research and exploration.