6 research outputs found

    Projection in a Description Logic of Context with Actions: Extended Version

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    Projection is the problem of checking whether the execution of a given sequence of actions will achieve its goal starting from some initial state. In this paper, we study a setting where we combine a two-dimensional Description Logic of context (ConDL) with an action formalism. We choose a well-studied ConDL where both: the possible states of a dynamical system itself (object level) and also different context-dependent views on this system state (context level) are organised in relational structures and can be described using usual DL constructs. To represent how such a system and its views evolve we introduce a suitable action formalism. It allows to describe change on both levels. Furthermore, the observable changes on the object level due to an action execution can also be contextdependent. We show that the formalism is well-behaved in the sense that projection has the same complexity as standard reasoning tasks in case ALCO is the underlying DL

    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 Entitäten wie die Höhe von Gebäuden, die Laufzeit von Sportlern, die Energieeffizienz von Automodellen oder die Energieerzeugung von Kraftwerken - jeweils ausgedrückt durch Zahlen mit zugehörigen Einheiten. Entitätszentriete Anfragen und direktes Question-Answering werden von Suchmaschinen häufig gut unterstützt. 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 über 2 Milliarden US-Dollar. Selbst moderne Systeme schaffen es nicht, Quantitäten, einschließlich der genannten Bedingungen (weniger als, über, 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 Quantitäten voranzutreiben. Unsere Hauptbeiträge sind die folgenden: • Zunächst präsentieren wir Qsearch [Ho et al., 2019, Ho et al., 2020] – ein System, das mit erweiterten Fragen mit Quantitätsfiltern umgehen kann, indem es Hinweise verwendet, die sowohl in der Frage als auch in den Textquellen vorhanden sind. Qsearch umfasst zwei Hauptbeiträge. Der erste Beitrag ist ein tiefes neuronales Netzwerkmodell, das für die Extraktion quantitätszentrierter 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 Quantitätsinformationen aus Webquellen, insbesondere Ad-hoc Webtabellen in HTML-Seiten. Der Beitrag von QuTE umfasst eine Methode zur Verknüpfung von Quantitäts- und Entitätsspalten, für die externe Textquellen genutzt werden. Zur Beantwortung von Fragen kontextualisieren wir die extrahierten Entitäts-Quantitäts-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 Entitäten und ihre relevanten Informationen ab, übersehen aber oft wichtige quantitative Eigenschaften. QL ist frage-gesteuert und basiert auf iterativem Lernen mit zwei Hauptbeiträgen, um die KB-Abdeckung zu verbessern. Der erste Beitrag ist eine Methode zur Expansion von Fragen, um einen größeren Pool an Faktenkandidaten zu erfassen. Der zweite Beitrag ist eine Technik zur Selbstkonsistenz durch Berücksichtigung der Werteverteilungen von Quantitäten

    Semantic Representation and Inference for NLP

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    Semantic representation and inference is essential for Natural Language Processing (NLP). The state of the art for semantic representation and inference is deep learning, and particularly Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and transformer Self-Attention models. This thesis investigates the use of deep learning for novel semantic representation and inference, and makes contributions in the following three areas: creating training data, improving semantic representations and extending inference learning. In terms of creating training data, we contribute the largest publicly available dataset of real-life factual claims for the purpose of automatic claim verification (MultiFC), and we present a novel inference model composed of multi-scale CNNs with different kernel sizes that learn from external sources to infer fact checking labels. In terms of improving semantic representations, we contribute a novel model that captures non-compositional semantic indicators. By definition, the meaning of a non-compositional phrase cannot be inferred from the individual meanings of its composing words (e.g., hot dog). Motivated by this, we operationalize the compositionality of a phrase contextually by enriching the phrase representation with external word embeddings and knowledge graphs. Finally, in terms of inference learning, we propose a series of novel deep learning architectures that improve inference by using syntactic dependencies, by ensembling role guided attention heads, incorporating gating layers, and concatenating multiple heads in novel and effective ways. This thesis consists of seven publications (five published and two under review).Comment: PhD thesis, the University of Copenhage

    Reasoning in Description Logic Ontologies for Privacy Management

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    A rise in the number of ontologies that are integrated and distributed in numerous application systems may provide the users to access the ontologies with different privileges and purposes. In this situation, preserving confidential information from possible unauthorized disclosures becomes a critical requirement. For instance, in the clinical sciences, unauthorized disclosures of medical information do not only threaten the system but also, most importantly, the patient data. Motivated by this situation, this thesis initially investigates a privacy problem, called the identity problem, where the identity of (anonymous) objects stored in Description Logic ontologies can be revealed or not. Then, we consider this problem in the context of role-based access control to ontologies and extend it to the problem asking if the identity belongs to a set of known individuals of cardinality smaller than the number k. If it is the case that some confidential information of persons, such as their identity, their relationships or their other properties, can be deduced from an ontology, which implies that some privacy policy is not fulfilled, then one needs to repair this ontology such that the modified one complies with the policies and preserves the information from the original ontology as much as possible. The repair mechanism we provide is called gentle repair and performed via axiom weakening instead of axiom deletion which was commonly used in classical approaches of ontology repair. However, policy compliance itself is not enough if there is a possible attacker that can obtain relevant information from other sources, which together with the modified ontology still violates the privacy policies. Safety property is proposed to alleviate this issue and we investigate this in the context of privacy-preserving ontology publishing. Inference procedures to solve those privacy problems and additional investigations on the complexity of the procedures, as well as the worst-case complexity of the problems, become the main contributions of this thesis.:1. Introduction 1.1 Description Logics 1.2 Detecting Privacy Breaches in Information System 1.3 Repairing Information Systems 1.4 Privacy-Preserving Data Publishing 1.5 Outline and Contribution of the Thesis 2. Preliminaries 2.1 Description Logic ALC 2.1.1 Reasoning in ALC Ontologies 2.1.2 Relationship with First-Order Logic 2.1.3. Fragments of ALC 2.2 Description Logic EL 2.3 The Complexity of Reasoning Problems in DLs 3. The Identity Problem and Its Variants in Description Logic Ontologies 3.1 The Identity Problem 3.1.1 Description Logics with Equality Power 3.1.2 The Complexity of the Identity Problem 3.2 The View-Based Identity Problem 3.3 The k-Hiding Problem 3.3.1 Upper Bounds 3.3.2 Lower Bound 4. Repairing Description Logic Ontologies 4.1 Repairing Ontologies 4.2 Gentle Repairs 4.3 Weakening Relations 4.4 Weakening Relations for EL Axioms 4.4.1 Generalizing the Right-Hand Sides of GCIs 4.4.2 Syntactic Generalizations 4.5 Weakening Relations for ALC Axioms 4.5.1 Generalizations and Specializations in ALC w.r.t. Role Depth 4.5.2 Syntactical Generalizations and Specializations in ALC 5. Privacy-Preserving Ontology Publishing for EL Instance Stores 5.1 Formalizing Sensitive Information in EL Instance Stores 5.2 Computing Optimal Compliant Generalizations 5.3 Computing Optimal Safe^{\exists} Generalizations 5.4 Deciding Optimality^{\exists} in EL Instance Stores 5.5 Characterizing Safety^{\forall} 5.6 Optimal P-safe^{\forall} Generalizations 5.7 Characterizing Safety^{\forall\exists} and Optimality^{\forall\exists} 6. Privacy-Preserving Ontology Publishing for EL ABoxes 6.1 Logical Entailments in EL ABoxes with Anonymous Individuals 6.2 Anonymizing EL ABoxes 6.3 Formalizing Sensitive Information in EL ABoxes 6.4 Compliance and Safety for EL ABoxes 6.5 Optimal Anonymizers 7. Conclusion 7.1 Main Results 7.2 Future Work Bibliograph

    OWL-Miner: Concept Induction in OWL Knowledge Bases

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    The Resource Description Framework (RDF) and Web Ontology Language (OWL) have been widely used in recent years, and automated methods for the analysis of data and knowledge directly within these formalisms are of current interest. Concept induction is a technique for discovering descriptions of data, such as inducing OWL class expressions to describe RDF data. These class expressions capture patterns in the data which can be used to characterise interesting clusters or to act as classifica- tion rules over unseen data. The semantics of OWL is underpinned by Description Logics (DLs), a family of expressive and decidable fragments of first-order logic. Recently, methods of concept induction which are well studied in the field of Inductive Logic Programming have been applied to the related formalism of DLs. These methods have been developed for a number of purposes including unsuper- vised clustering and supervised classification. Refinement-based search is a concept induction technique which structures the search space of DL concept/OWL class expressions and progressively generalises or specialises candidate concepts to cover example data as guided by quality criteria such as accuracy. However, the current state-of-the-art in this area is limited in that such methods: were not primarily de- signed to scale over large RDF/OWL knowledge bases; do not support class lan- guages as expressive as OWL2-DL; or, are limited to one purpose, such as learning OWL classes for integration into ontologies. Our work addresses these limitations by increasing the efficiency of these learning methods whilst permitting a concept language up to the expressivity of OWL2-DL classes. We describe methods which support both classification (predictive induction) and subgroup discovery (descrip- tive induction), which, in this context, are fundamentally related. We have implemented our methods as the system called OWL-Miner and show by evaluation that our methods outperform state-of-the-art systems for DL learning in both the quality of solutions found and the speed in which they are computed. Furthermore, we achieve the best ever ten-fold cross validation accuracy results on the long-standing benchmark problem of carcinogenesis. Finally, we present a case study on ongoing work in the application of OWL-Miner to a real-world problem directed at improving the efficiency of biological macromolecular crystallisation
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