Explainable methods for knowledge graph refinement and exploration via symbolic reasoning

Knowledge Graphs (KGs) have applications in many domains such as Finance, Manufacturing, and Healthcare. While recent efforts have created large KGs, their content is far from complete and sometimes includes invalid statements. Therefore, it is crucial to refine the constructed KGs to enhance their coverage and accuracy via KG completion and KG validation. It is also vital to provide human-comprehensible explanations for such refinements, so that humans have trust in the KG quality. Enabling KG exploration, by search and browsing, is also essential for users to understand the KG value and limitations towards down-stream applications. However, the large size of KGs makes KG exploration very challenging. While the type taxonomy of KGs is a useful asset along these lines, it remains insufficient for deep exploration. In this dissertation we tackle the aforementioned challenges of KG refinement and KG exploration by combining logical reasoning over the KG with other techniques such as KG embedding models and text mining. Through such combination, we introduce methods that provide human-understandable output. Concretely, we introduce methods to tackle KG incompleteness by learning exception-aware rules over the existing KG. Learned rules are then used in inferring missing links in the KG accurately. Furthermore, we propose a framework for constructing human-comprehensible explanations for candidate facts from both KG and text. Extracted explanations are used to insure the validity of KG facts. Finally, to facilitate KG exploration, we introduce a method that combines KG embeddings with rule mining to compute informative entity clusters with explanations.Wissensgraphen haben viele Anwendungen in verschiedenen Bereichen, beispielsweise im Finanz- und Gesundheitswesen. Wissensgraphen sind jedoch unvollständig und enthalten auch ungültige Daten. Hohe Abdeckung und Korrektheit erfordern neue Methoden zur Wissensgraph-Erweiterung und Wissensgraph-Validierung. Beide Aufgaben zusammen werden als Wissensgraph-Verfeinerung bezeichnet. Ein wichtiger Aspekt dabei ist die Erklärbarkeit und Verständlichkeit von Wissensgraphinhalten für Nutzer. In Anwendungen ist darüber hinaus die nutzerseitige Exploration von Wissensgraphen von besonderer Bedeutung. Suchen und Navigieren im Graph hilft dem Anwender, die Wissensinhalte und ihre Limitationen besser zu verstehen. Aufgrund der riesigen Menge an vorhandenen Entitäten und Fakten ist die Wissensgraphen-Exploration eine Herausforderung. Taxonomische Typsystem helfen dabei, sind jedoch für tiefergehende Exploration nicht ausreichend. Diese Dissertation adressiert die Herausforderungen der Wissensgraph-Verfeinerung und der Wissensgraph-Exploration durch algorithmische Inferenz über dem Wissensgraph. Sie erweitert logisches Schlussfolgern und kombiniert es mit anderen Methoden, insbesondere mit neuronalen Wissensgraph-Einbettungen und mit Text-Mining. Diese neuen Methoden liefern Ausgaben mit Erklärungen für Nutzer. Die Dissertation umfasst folgende Beiträge: Insbesondere leistet die Dissertation folgende Beiträge: • Zur Wissensgraph-Erweiterung präsentieren wir ExRuL, eine Methode zur Revision von Horn-Regeln durch Hinzufügen von Ausnahmebedingungen zum Rumpf der Regeln. Die erweiterten Regeln können neue Fakten inferieren und somit Lücken im Wissensgraphen schließen. Experimente mit großen Wissensgraphen zeigen, dass diese Methode Fehler in abgeleiteten Fakten erheblich reduziert und nutzerfreundliche Erklärungen liefert. • Mit RuLES stellen wir eine Methode zum Lernen von Regeln vor, die auf probabilistischen Repräsentationen für fehlende Fakten basiert. Das Verfahren erweitert iterativ die aus einem Wissensgraphen induzierten Regeln, indem es neuronale Wissensgraph-Einbettungen mit Informationen aus Textkorpora kombiniert. Bei der Regelgenerierung werden neue Metriken für die Regelqualität verwendet. Experimente zeigen, dass RuLES die Qualität der gelernten Regeln und ihrer Vorhersagen erheblich verbessert. • Zur Unterstützung der Wissensgraph-Validierung wird ExFaKT vorgestellt, ein Framework zur Konstruktion von Erklärungen für Faktkandidaten. Die Methode transformiert Kandidaten mit Hilfe von Regeln in eine Menge von Aussagen, die leichter zu finden und zu validieren oder widerlegen sind. Die Ausgabe von ExFaKT ist eine Menge semantischer Evidenzen für Faktkandidaten, die aus Textkorpora und dem Wissensgraph extrahiert werden. Experimente zeigen, dass die Transformationen die Ausbeute und Qualität der entdeckten Erklärungen deutlich verbessert. Die generierten unterstützen Erklärungen unterstütze sowohl die manuelle Wissensgraph- Validierung durch Kuratoren als auch die automatische Validierung. • Zur Unterstützung der Wissensgraph-Exploration wird ExCut vorgestellt, eine Methode zur Erzeugung von informativen Entitäts-Clustern mit Erklärungen unter Verwendung von Wissensgraph-Einbettungen und automatisch induzierten Regeln. Eine Cluster-Erklärung besteht aus einer Kombination von Relationen zwischen den Entitäten, die den Cluster identifizieren. ExCut verbessert gleichzeitig die Cluster- Qualität und die Cluster-Erklärbarkeit durch iteratives Verschränken des Lernens von Einbettungen und Regeln. Experimente zeigen, dass ExCut Cluster von hoher Qualität berechnet und dass die Cluster-Erklärungen für Nutzer informativ sind

Gothic Voids: Nineteenth-Century Reader Experience and Participation

Characterization of nineteenth-century literary Gothic is usually confined to affective response. This project argues that literary Gothic works constitute an intellectual, empirical endeavor. Because authors of literary Gothic intentionally left voids in their narratives, they invited their readers to participate in making narrative through speculation and conjecture about missing information. The practice of Gothic reading makes the reader an active partaker in filling those voids with rational conclusions. Reading is not just textual encounter. Rather, it incorporates making meaning of one’s surroundings. In their experiences, literary works’ characters “read” their environments: people, objects, events, etc. Chapter 1 characterizes Gothic reading as the employment of logical processes; Northanger Abbey’s Catherine Morland uses induction, deduction and syllogism to make sense of—read—her world. In Chapter 2, Frankenstein presents reading as audience engagement between characters as they tell their stories. Shelley uses sympathy as a social-bonding device in which characters read other characters through listening. Chapter 3 examines Jane Eyre through the motif of the legend of Bluebeard’s House. The house serves as a narrative that Bluebeard’s Wife must read and decode for its danger. With her keys, she can unlock the spaces of that narrative. Standing in for the reader, Jane attempts to unlock Thornfield’s narrative through investigation. Both Jane and the reader use “keys” of unexplainable things to unlock the secrets within the narrative; the Gothic author endows the reader with keys of mysterious objects, people, and events. Reading continually opens up answers. In the final chapter, this project argues for Gothic as detective fiction. In their narrative structure and storylines, Dracula and The Strange Case of Dr. Jekyll and Mr. Hyde allow for the reader to sleuth out mysteries. Dracula’s Seward and van Helsing perform detective work on Renfield and Dracula, respectively. Additionally, each man represents a different style of reasoning, Seward algorithmic and Helsing heuristic. Meanwhile, Utterson the attorney must play detective to determine the nature of the relationship between Henry Jekyll and Edward Hyde. All three characters read their environments as part of their detection

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Intelligent Information Access to Linked Data - Weaving the Cultural Heritage Web

The subject of the dissertation is an information alignment experiment of two cultural heritage information systems (ALAP): The Perseus Digital Library and Arachne. In modern societies, information integration is gaining importance for many tasks such as business decision making or even catastrophe management. It is beyond doubt that the information available in digital form can offer users new ways of interaction. Also, in the humanities and cultural heritage communities, more and more information is being published online. But in many situations the way that information has been made publicly available is disruptive to the research process due to its heterogeneity and distribution. Therefore integrated information will be a key factor to pursue successful research, and the need for information alignment is widely recognized. ALAP is an attempt to integrate information from Perseus and Arachne, not only on a schema level, but to also perform entity resolution. To that end, technical peculiarities and philosophical implications of the concepts of identity and co-reference are discussed. Multiple approaches to information integration and entity resolution are discussed and evaluated. The methodology that is used to implement ALAP is mainly rooted in the fields of information retrieval and knowledge discovery. First, an exploratory analysis was performed on both information systems to get a first impression of the data. After that, (semi-)structured information from both systems was extracted and normalized. Then, a clustering algorithm was used to reduce the number of needed entity comparisons. Finally, a thorough matching was performed on the different clusters. ALAP helped with identifying challenges and highlighted the opportunities that arise during the attempt to align cultural heritage information systems

Proceedings of LOAIT '07 : II Workshop on Legal Ontologies and Artificial Intelligence Techniques

Proceedings of the 2nd Workshop on Legal Ontologies and Artificial Intelligence Techniques June 4th, 2007, Stanford Universit

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

A Knowledge Graph Based Integration Approach for Industry 4.0

The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach. We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories