Search Results
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.
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.
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.
Challenges of Applying Knowledge Graph and their Embeddings to a Real-world Use-case
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, Harald
Different 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.
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.
Semantic role labeling for knowledge graph extraction from text
2021, Alam, Mehwish, Gangemi, Aldo, Pressutti, Valentina, Reforgiato Recupero, Diego
This paper introduces TakeFive, a new semantic role labeling method that transforms a text into a frame-oriented knowledge graph. It performs dependency parsing, identifies the words that evoke lexical frames, locates the roles and fillers for each frame, runs coercion techniques, and formalizes the results as a knowledge graph. This formal representation complies with the frame semantics used in Framester, a factual-linguistic linked data resource. We tested our method on the WSJ section of the Peen Treebank annotated with VerbNet and PropBank labels and on the Brown corpus. The evaluation has been performed according to the CoNLL Shared Task on Joint Parsing of Syntactic and Semantic Dependencies. The obtained precision, recall, and F1 values indicate that TakeFive is competitive with other existing methods such as SEMAFOR, Pikes, PathLSTM, and FRED. We finally discuss how to combine TakeFive and FRED, obtaining higher values of precision, recall, and F1 measure.