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Comparative Verification of the Digital Library of Mathematical Functions and Computer Algebra Systems
2022, Greiner-Petter, André, Cohl, Howard S., Youssef, Abdou, Schubotz, Moritz, Trost, Avi, Dey, Rajen, Aizawa, Akiko, Gipp, Bela, Fisman, Dana, Rosu, Grigore
Digital mathematical libraries assemble the knowledge of years of mathematical research. Numerous disciplines (e.g., physics, engineering, pure and applied mathematics) rely heavily on compendia gathered findings. Likewise, modern research applications rely more and more on computational solutions, which are often calculated and verified by computer algebra systems. Hence, the correctness, accuracy, and reliability of both digital mathematical libraries and computer algebra systems is a crucial attribute for modern research. In this paper, we present a novel approach to verify a digital mathematical library and two computer algebra systems with one another by converting mathematical expressions from one system to the other. We use our previously developed conversion tool (referred to as ) to translate formulae from the NIST Digital Library of Mathematical Functions to the computer algebra systems Maple and Mathematica. The contributions of our presented work are as follows: (1) we present the most comprehensive verification of computer algebra systems and digital mathematical libraries with one another; (2) we significantly enhance the performance of the underlying translator in terms of coverage and accuracy; and (3) we provide open access to translations for Maple and Mathematica of the formulae in the NIST Digital Library of Mathematical Functions.
Data Protection Impact Assessments in Practice: Experiences from Case Studies
2022, Friedewald, Michael, Schiering, Ina, Martin, Nicholas, Hallinan, Dara, Katsikas, Sokratis, Lambrinoudakis, Costas, Cuppens, Nora, Mylopoulos, John, Kalloniatis, Christos, Meng, Weizhi, Furnell, Steven, Pallas, Frank, Pohle, Jörg, Sasse, M. Angela, Abie, Habtamu, Ranise, Silvio, Verderame, Luca, Cambiaso, Enrico, Vidal, Jorge Maestre, Monge, Marco Antonio Sotelo
In the context of the project A Data Protection Impact Assessment (DPIA) Tool for Practical Use in Companies and Public Administration an operationalization for Data Protection Impact Assessments was developed based on the approach of Forum Privatheit. This operationalization was tested and refined during twelve tests with startups, small- and medium sized enterprises, corporations and public bodies. This paper presents the operationalization and summarizes the experience from the tests.
DDB-KG: The German Bibliographic Heritage in a Knowledge Graph
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, Adam
Under 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.
The Concept of Identifiability in ML Models
2022, von Maltzan, Stephanie, Bastieri, Denis, Wills, Gary, Kacsuk, Péter, Chang, Victor
Recent research indicates that the machine learning process can be reversed by adversarial attacks. These attacks can be used to derive personal information from the training. The supposedly anonymising machine learning process represents a process of pseudonymisation and is, therefore, subject to technical and organisational measures. Consequently, the unexamined belief in anonymisation as a guarantor for privacy cannot be easily upheld. It is, therefore, crucial to measure privacy through the lens of adversarial attacks and precisely distinguish what is meant by personal data and non-personal data and above all determine whether ML models represent pseudonyms from the training data.
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.
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.
Knowledge Extraction for Art History: the Case of Vasari’s The Lives of The Artists (1568)
2022, Santini, Cristian, Tan, Mary Ann, Tietz, Tabea, Bruns, Oleksandra, Posthumus, Etienne, Sack, Harald, Paschke, Adrian, Rehm, Georg, Neudecker, Clemens, Pintscher, Lydia
Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari's The Lives of The Artists. This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed.
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.
Leveraging Literals for Knowledge Graph Embeddings
2021, Gesese, Genet Asefa, Tamma, Valentina, Fernandez, Miriam, Poveda-Villalón, María
Nowadays, Knowledge Graphs (KGs) have become invaluable for various applications such as named entity recognition, entity linking, question answering. However, there is a huge computational and storage cost associated with these KG-based applications. Therefore, there arises the necessity of transforming the high dimensional KGs into low dimensional vector spaces, i.e., learning representations for the KGs. Since a KG represents facts in the form of interrelations between entities and also using attributes of entities, the semantics present in both forms should be preserved while transforming the KG into a vector space. Hence, the main focus of this thesis is to deal with the multimodality and multilinguality of literals when utilizing them for the representation learning of KGs. The other task is to extract benchmark datasets with a high level of difficulty for tasks such as link prediction and triple classification. These datasets could be used for evaluating both kind of KG Embeddings, those using literals and those which do not include literals.
On the Impact of Temporal Representations on Metaphor Detection
2022, Giorgio Ottolina, Matteo Palmonari, Manuel Vimercati, Mehwish Alam, Calzolari, Nicoletta, Béchet, Frédéric, Blache, Philippe, Choukri, Khalid, Cieri, Christopher, Declerck, Thierry, Goggi, Sara, Isahara, Hitoshi, Maegaard, Bente, Mariani, Joseph, Mazo, Hélène, Odijk, Jan, Piperidis, Stelios
State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution. This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings. Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods. However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments. This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented.