1,853 research outputs found
Standardizing Knowledge Engineering Practices with a Reference Architecture
Full text linkKnowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, the scope of knowledge engineering, as apparent from its target tasks and use cases, has been shifting, together with its paradigms such as expert systems, semantic web, and language modeling. The intended use cases and supported user requirements between these paradigms have not been analyzed globally, as new paradigms often satisfy prior pain points while possibly introducing new ones. The recent abstraction of systemic patterns into a boxology provides an opening for aligning the requirements and use cases of knowledge engineering with the systems, components, and software that can satisfy them best, however, this direction has not been explored to date. This paper proposes a vision of harmonizing the best practices in the field of knowledge engineering by leveraging the software engineering methodology of creating reference architectures. We describe how a reference architecture can be iteratively designed and implemented to associate user needs with recurring systemic patterns, building on top of existing knowledge engineering workflows and boxologies. We provide a six-step roadmap that can enable the development of such an architecture, consisting of scope definition, selection of information sources, architectural analysis, synthesis of an architecture based on the information source analysis, evaluation through instantiation, and, ultimately, instantiation into a concrete software architecture. We provide an initial design and outcome of the definition of architectural scope, selection of information sources, and analysis. As the remaining steps of design, evaluation, and instantiation of the architecture are largely use-case specific, we provide a detailed description of their procedures and point to relevant examples. We expect that following through on this vision will lead to well-grounded reference architectures for knowledge engineering, will advance the ongoing initiatives of organizing the neurosymbolic knowledge engineering space, and will build new links to the software architectures and data science communities
Generalization by People and Machines (Dagstuhl Seminar 24192)
No full textToday’s AI systems are powerful to the extent that they have largely entered the mainstream and divided the world between those who believe AI will solve all our problems and those who fear that AI will be destructive for humanity. Meanwhile, trusting AI is very difficult given its lack of robustness to novel situations, consistency of its outputs, and interpretability of its reasoning process. Building trustworthy AI requires a paradigm shift from the current oversimplified practice of crafting accuracy-driven models to a human-centric design that can enhance human ability on manageable tasks, or enable humans and AIs to solve complex tasks together that are difficult for either separately. At the core of this problem is the unrivaled human generalization and abstraction ability. While today’s AI is able to provide a response to any input, its ability to transfer knowledge to novel situations is still limited by oversimplification practices, as manifested by tasks that involve pragmatics, agent goals, and understanding of narrative structures. As there are currently no venues that allow cross-disciplinary research on the topic of reliable AI generalization, this discrepancy is problematic and requires dedicated efforts to bring in one place generalization experts from different fields within AI, but also with Cognitive Science. This Dagstuhl Seminar thus provided a unique opportunity for discussing the discrepancy between human and AI generalization mechanisms and crafting a vision on how to align the two streams in a compelling and promising way that combines the strengths of both. To ensure an effective seminar, we brought together cross-disciplinary perspectives across computer and cognitive science fields. Our participants included experts in Interpretable Machine Learning, Neuro-Symbolic Reasoning, Explainable AI, Commonsense Reasoning, Case-based Reasoning, Analogy, Cognitive Science, and Human-AI Teaming. Specifically, the seminar participants focused on the following questions: How can cognitive mechanisms in people be used to inspire generalization in AI? What Machine Learning methods hold the promise to enable such reasoning mechanisms? What is the role of data and knowledge engineering for AI and human generalization? How can we design and model human-AI teams that can benefit from their complementary generalization capabilities? How can we evaluate generalization in humans and AI in a satisfactory manner
Filip Spannbrucker
Full text linkFilip Spannbrucker Filip Spannbrucker, česká barokní architektura, hrabě František Josef Schlik, Jean Baptiste Mathey, Jičínsko The main aim of this thesis is to present the life and work of Filip Spannbrucker (approximately 1672?1729), Prague architect and builder, in the context of contemporary artistic and social course of events. In the first part of this work there will be outlined Spannbrucker's life and professional career based on elaborate archival research. Special attention will be devoted to proprietors and a patronage of building projects wherein Spannbrucker participated or whereof was an author. In the connection, his work will be brought in contiguity of contemporaneous cultural, socioeconomical and aesthetical notions and relations. In the second part of this thesis there will be pointed out a characteristic and spring of inspiration of Spannbrucker's architecture by means of an accurate formal analysis. Final part will be a catalog of Filip Spannbrucker's works. Filip Spannbrucker, Czech baroque architecture, count František Josef Schlik, Jean Baptiste Mathey, region of Jičí
Missing Mr. Brown and buying an Abraham Lincoln – Dark Entities and DBpedia
No full textWe argue for the need for the community to address the issue of “darkentities”, those domain entities for which a knowledge base has no informationin the context of the entity linking task for building Event-Centric KnowledgeGraphs. Through an analysis of a large (1,2 million article) automotive newswirecorpus against DBpedia, we identify six classes of errors that lead to dark entities.Finally, we outline further steps that can be taken for tackling this issue
An analysis of the impact of the world heritage status on sustainable tourism development
No full textLocation-Based Discovery and Network Handover Management for Heterogeneous IEEE 802.11ah IoT Applications
No full textThis research was funded by the Flemish IDEAL-IoT project (FWO SBO, grant nr. S004017N). The author Serena Santi is funded by the Flemish FWO SB grant (nr. 1S82120N). The author Filip Lemic was supported by the EU MSCA grant (nr. 893760). The computational resources were provided by the VSC (Flemish Supercomputer Center), funded by FWO and the Flemish Government -department EWI
The Predicate Matrix and the Event and Implied Situation Ontology: Making More of Events
No full textThis paper presents the Event and Implied Situation Ontology (ESO), a resource which formalizes the pre and post situations of events and the roles of the entities affected by an event. The ontology reuses and maps across existing resources such as WordNet, SUMO, VerbNet, PropBank and FrameNet. We describe how ESO is injected into a new version of the Predicate Matrix and illustrate how these resources are used to detect information in large document collections that otherwise would have remained implicit. The model targets interpretations of situations rather than the semantics of verbs per se. The event is interpreted as a situation using RDF taking all event components into account. Hence, the ontology and the linked resources need to be considered from the perspective of this interpretation model
Evaluating entity linking: An analysis of current benchmark datasets and a roadmap for doing a better job
No full textModular System for Distributed User Data Management
Full text linkTitle: Modular System for Distributed User Data Management Author: Filip Pavliš, Author's email: [email protected] Department: Department of Distributed and Dependable Systems Supervisor: Mgr. Pavel Ježek, Department of Distributed and Dependable Systems Abstract: The main objective of this project was to create a multiplatform modular system for distributed data management. The system supports both local and remote accessible data storage. Therefore a part of the solution is also a server for remote data access. System also provides parallel usage of a local and remote storage which guarantees that the storage is accessible also when connection to server is lost. In this case, system is capable to synchronize changes between the server and a client. A client can use multiple instances of his storage and our system is able to synchronize them through central server. The system provides abstraction over data model to separate plugin developers from its specific behavior. As practical preview of system usability was created an extension application called TuaLoca. The application will contain implementation of preview plugins and basic user interaction. Keywords: Distributed Data Management, Modular system, Synchronization on the SQL Queries Level, Distributed Solution for Interface LIN
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