99 research outputs found

    Beyond Quantity: Research with Subsymbolic AI

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    How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately

    Knowledge extraction from unstructured data

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    Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable data in a structured format. Thus, techniques for addressing the problem of capturing knowledge from unstructured data sources are needed. Knowledge extraction methods are used by the research communities to address this problem; methods that are able to capture knowledge in a natural language text and map the extracted knowledge to existing knowledge presented in knowledge graphs (KGs). These knowledge extraction methods include Named-entity recognition, Named-entity Disambiguation, Relation Recognition, and Relation Linking. This thesis addresses the problem of extracting knowledge over unstructured data and discovering patterns in the extracted knowledge. We devise a rule-based approach for entity and relation recognition and linking. The defined approach effectively maps entities and relations within a text to their resources in a target KG. Additionally, it overcomes the challenges of recognizing and linking entities and relations to a specific KG by employing devised catalogs of linguistic and domain-specific rules that state the criteria to recognize entities in a sentence of a particular language, and a deductive database that encodes knowledge in community-maintained KGs. Moreover, we define a Neuro-symbolic approach for the tasks of knowledge extraction in encyclopedic and domain-specific domains; it combines symbolic and sub-symbolic components to overcome the challenges of entity recognition and linking and the limitation of the availability of training data while maintaining the accuracy of recognizing and linking entities. Additionally, we present a context-aware framework for unveiling semantically related posts in a corpus; it is a knowledge-driven framework that retrieves associated posts effectively. We cast the problem of unveiling semantically related posts in a corpus into the Vertex Coloring Problem. We evaluate the performance of our techniques on several benchmarks related to various domains for knowledge extraction tasks. Furthermore, we apply these methods in real-world scenarios from national and international projects. The outcomes show that our techniques are able to effectively extract knowledge encoded in unstructured data and discover patterns over the extracted knowledge presented as machine-readable data. More importantly, the evaluation results provide evidence to the effectiveness of combining the reasoning capacity of the symbolic frameworks with the power of pattern recognition and classification of sub-symbolic models

    Geographic information extraction from texts

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    A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction

    Exploiting general-purpose background knowledge for automated schema matching

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    The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process. In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources. A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems. One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented. In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications

    Christian Thought in the Medieval Islamicate World: ÊżAbdÄ«shĆÊż of Nisibis and the Apologetic Tradition

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    This book is the first monograph-length study and intellectual biography of ÊżAbdÄ«shĆÊż of Nisibis (d. 1318), bishop and polymath of the Church of the East. Focusing on his works of apologetic theology, this study examines the intellectual strategies he employs to justify Christianity against Muslim (and to a lesser extent Jewish) criticisms. Better known to scholars of Syriac literature as a poet, jurist, and cataloguer, ÊżAbdÄ«shĆÊż wrote a considerable number of works in the Arabic language, many of which have only recently come to light. He flourished at a time when Syriac Christian writers were becoming increasingly indebted to Islamic models of intellectual production. Yet he also composed many of his works during mounting religious tensions between Christians and Muslims following the official conversion of the Ilkhanate to Islam in 1295. In the midst of these challenges, ÊżAbdÄ«shĆÊż negotiates a centuries-long tradition of Syriac and Arabic apologetics to remind his readers of the verity of the Christian faith. His engagement with this tradition reveals how anti-Muslim apologetics had long shaped the articulation of Christian identity in the Middle East since the emergence of Islam. Through a selective process of encyclopaedism and systematization, ÊżAbdÄ«shĆÊż navigates a vast corpus of Syriac and Arabic apologetics to create a synthesis and theological canon that remains authoritative to this day

    Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

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    On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

    Entities with quantities : extraction, search, and ranking

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    Quantities are more than numeric values. They denote measures of the world’s entities such as heights of buildings, running times of athletes, energy efficiency of car models or energy production of power plants, all expressed in numbers with associated units. Entity-centric search and question answering (QA) are well supported by modern search engines. However, they do not work well when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. State-of-the-art systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases (KBs) also fail as quantities are poorly covered by state-of-the-art KBs. In this dissertation, we developed new methods to advance the state-of-the-art on quantity knowledge extraction and search.Zahlen sind mehr als nur numerische Werte. Sie beschreiben Maße von EntitĂ€ten wie die Höhe von GebĂ€uden, die Laufzeit von Sportlern, die Energieeffizienz von Automodellen oder die Energieerzeugung von Kraftwerken - jeweils ausgedrĂŒckt durch Zahlen mit zugehörigen Einheiten. EntitĂ€tszentriete Anfragen und direktes Question-Answering werden von Suchmaschinen hĂ€ufig gut unterstĂŒtzt. Sie funktionieren jedoch nicht gut, wenn die Fragen Zahlenfilter beinhalten, wie z. B. die Suche nach Sportlern, die 200m unter 20 Sekunden gelaufen sind, oder nach Unternehmen mit einem Quartalsumsatz von ĂŒber 2 Milliarden US-Dollar. Selbst moderne Systeme schaffen es nicht, QuantitĂ€ten, einschließlich der genannten Bedingungen (weniger als, ĂŒber, etc.), der Maßeinheiten (Sekunden, Dollar, etc.) und des Kontexts (200-Meter-Rennen, Quartalsumsatz usw.), zu verstehen. Auch QA-Systeme, die auf strukturierten Wissensbanken (“Knowledge Bases”, KBs) aufgebaut sind, versagen, da quantitative Eigenschaften von modernen KBs kaum erfasst werden. In dieser Dissertation werden neue Methoden entwickelt, um den Stand der Technik zur Wissensextraktion und -suche von QuantitĂ€ten voranzutreiben. Unsere HauptbeitrĂ€ge sind die folgenden: ‱ ZunĂ€chst prĂ€sentieren wir Qsearch [Ho et al., 2019, Ho et al., 2020] – ein System, das mit erweiterten Fragen mit QuantitĂ€tsfiltern umgehen kann, indem es Hinweise verwendet, die sowohl in der Frage als auch in den Textquellen vorhanden sind. Qsearch umfasst zwei HauptbeitrĂ€ge. Der erste Beitrag ist ein tiefes neuronales Netzwerkmodell, das fĂŒr die Extraktion quantitĂ€tszentrierter Tupel aus Textquellen entwickelt wurde. Der zweite Beitrag ist ein neuartiges Query-Matching-Modell zum Finden und zur Reihung passender Tupel. ‱ Zweitens, um beim Vorgang heterogene Tabellen einzubinden, stellen wir QuTE [Ho et al., 2021a, Ho et al., 2021b] vor – ein System zum Extrahieren von QuantitĂ€tsinformationen aus Webquellen, insbesondere Ad-hoc Webtabellen in HTML-Seiten. Der Beitrag von QuTE umfasst eine Methode zur VerknĂŒpfung von QuantitĂ€ts- und EntitĂ€tsspalten, fĂŒr die externe Textquellen genutzt werden. Zur Beantwortung von Fragen kontextualisieren wir die extrahierten EntitĂ€ts-QuantitĂ€ts-Paare mit informativen Hinweisen aus der Tabelle und stellen eine neue Methode zur Konsolidierung und verbesserteer Reihung von Antwortkandidaten durch Inter-Fakten-Konsistenz vor. ‱ Drittens stellen wir QL [Ho et al., 2022] vor – eine Recall-orientierte Methode zur Anreicherung von Knowledge Bases (KBs) mit quantitativen Fakten. Moderne KBs wie Wikidata oder YAGO decken viele EntitĂ€ten und ihre relevanten Informationen ab, ĂŒbersehen aber oft wichtige quantitative Eigenschaften. QL ist frage-gesteuert und basiert auf iterativem Lernen mit zwei HauptbeitrĂ€gen, um die KB-Abdeckung zu verbessern. Der erste Beitrag ist eine Methode zur Expansion von Fragen, um einen grĂ¶ĂŸeren Pool an Faktenkandidaten zu erfassen. Der zweite Beitrag ist eine Technik zur Selbstkonsistenz durch BerĂŒcksichtigung der Werteverteilungen von QuantitĂ€ten

    Multi-modal post-editing of machine translation

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    As MT quality continues to improve, more and more translators switch from traditional translation from scratch to PE of MT output, which has been shown to save time and reduce errors. Instead of mainly generating text, translators are now asked to correct errors within otherwise helpful translation proposals, where repetitive MT errors make the process tiresome, while hard-to-spot errors make PE a cognitively demanding activity. Our contribution is three-fold: first, we explore whether interaction modalities other than mouse and keyboard could well support PE by creating and testing the MMPE translation environment. MMPE allows translators to cross out or hand-write text, drag and drop words for reordering, use spoken commands or hand gestures to manipulate text, or to combine any of these input modalities. Second, our interviews revealed that translators see value in automatically receiving additional translation support when a high CL is detected during PE. We therefore developed a sensor framework using a wide range of physiological and behavioral data to estimate perceived CL and tested it in three studies, showing that multi-modal, eye, heart, and skin measures can be used to make translation environments cognition-aware. Third, we present two multi-encoder Transformer architectures for APE and discuss how these can adapt MT output to a domain and thereby avoid correcting repetitive MT errors.Angesichts der stetig steigenden QualitĂ€t maschineller Übersetzungssysteme (MÜ) post-editieren (PE) immer mehr Übersetzer die MÜ-Ausgabe, was im Vergleich zur herkömmlichen Übersetzung Zeit spart und Fehler reduziert. Anstatt primĂ€r Text zu generieren, mĂŒssen Übersetzer nun Fehler in ansonsten hilfreichen ÜbersetzungsvorschlĂ€gen korrigieren. Dennoch bleibt die Arbeit durch wiederkehrende MÜ-Fehler mĂŒhsam und schwer zu erkennende Fehler fordern die Übersetzer kognitiv. Wir tragen auf drei Ebenen zur Verbesserung des PE bei: Erstens untersuchen wir, ob andere InteraktionsmodalitĂ€ten als Maus und Tastatur das PE unterstĂŒtzen können, indem wir die Übersetzungsumgebung MMPE entwickeln und testen. MMPE ermöglicht es, Text handschriftlich, per Sprache oder ĂŒber Handgesten zu verĂ€ndern, Wörter per Drag & Drop neu anzuordnen oder all diese EingabemodalitĂ€ten zu kombinieren. Zweitens stellen wir ein Sensor-Framework vor, das eine Vielzahl physiologischer und verhaltensbezogener Messwerte verwendet, um die kognitive Last (KL) abzuschĂ€tzen. In drei Studien konnten wir zeigen, dass multimodale Messung von Augen-, Herz- und Hautmerkmalen verwendet werden kann, um Übersetzungsumgebungen an die KL der Übersetzer anzupassen. Drittens stellen wir zwei Multi-Encoder-Transformer-Architekturen fĂŒr das automatische Post-Editieren (APE) vor und erörtern, wie diese die MÜ-Ausgabe an eine DomĂ€ne anpassen und dadurch die Korrektur von sich wiederholenden MÜ-Fehlern vermeiden können.Deutsche Forschungsgemeinschaft (DFG), Projekt MMP

    Automated Deduction – CADE 28

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    This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions

    Design by Play:Playfulness and Object-Oriented Philosophy for the design of IoT

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    The Internet of Things (IoT) has garnered heightened interest and momentum in recent years. These connected devices have extended the concurrent rise of data collection and processing within the everyday objects that cohabit our human lives. Though technology has always changed the way we live our lives these ‘smart’ devices are adding new challenges—particularly concerning privacy and security—not previously experienced when using their older ‘dumb’ predecessors. These challenges are not always apparent to their human cohabitors and often only come to the fore when something untoward happens as a consequence of the data being collected. These objects are not to blame, they exist in their worlds governed by their own rules established by their creators rather than their users. Designers have traditionally been taught to present these objects as neutral participants in our human lives; there to help, but not supersede. However, these objects exist within many independent and interdependent assemblages of human and non-human actants that go beyond the previously experienced human-object relationship. Through this discourse, I highlight the overall aim of this thesis to ask questions around our traditional practices of design concerning IoT. In particular, this research strives to do many things: it attempts to intertwine philosophical debate with the act of design; it moves towards an argument of rethinking design orthodoxies around human-centeredness in favour of object-oriented-ness; it explores an alternative side to the phenomenon of the IoT, arguing for agency in a post-anthropocentric perspective of the world and its implications; it tries to bridge the gap between practice-based design research and theory by passing through a veil of philosophical intrigue. But at its core, is an advocacy for the presence of a playful attitude within the practice of design, arguing for an attitude of playfulness as an integral part of the design process. How being playfully charged to create artefacts can usher in unique perspectives for design and technology. The research is enacted through an iterative Research through Design ideology, using a transdisciplinary approach of Ludic and Speculative Design practice that explore alternative perspectives towards the design of IoT. It is conducted through an exploration of Object-Oriented Philosophy as a means to enact a metaphorical ‘carpentry’ of artefacts that practice philosophical arguments through their execution. In the process of designing three artefacts—a model for a philosophical view of IoT, a board game, and a bespoke deck of tarot cards—this research builds upon the idea of More-than Human-Centeredness for the design of IoT, by introducing the creation of bespoken method assemblages as a means for playful design exploration. It concludes on a debate around the implications and potential of design thinking in a post-anthropocentric perspective through the inclusion of playfulness and philosophy as assets for design, and, the use of philosophical carpentry as a methodology for understanding the nebulous nature of IoT
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