Global ETD Search
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Showing 1 to 20 of 77 for “"knowledge graphs"”.
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Enriching Knowledge Graphs Using Machine Learning Techniques
A knowledge graph represents millions of facts and reliable information about people, places, and things. These knowledge graphs have proven their reliability and their usage for providing better search results; answering ambiguous questions regarding entities; and training semantic parsers to …
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Exploiting knowledge graphs for entity-centric prediction
… data contains directly useful information and knowledge about the real world, making it possible to make predictions about real-world phenomena based on text. As all application domains involve humans, text-based prediction has widespread applications, especially for optimization of decision …
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Personalization of AI Tutor Based on Knowledge Graphs
Personalized tutoring, tailored to the specific knowledge and needs of individual students, has been shown to significantly enhance academic performance. Research by Schmidt and Moust, for example, highlights that tutors who engage with students on a personal level are more effective in guiding …
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EVIDENCE EVALUATION IN BIOMEDICAL KNOWLEDGE GRAPHS FOR PHARMACEUTICAL DISCOVERY
… assemble and query biomedical heterogeneous knowledge graphs in a computational discovery platform guided by rational, algorithmic measures of relevance and confidence, facilitating scientific discovery? And, how have continuing waves of scientific and technological progress informed and …
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Improving accessibility and multi-hop reasoning in knowledge graphs
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Multimodal Representation Learning for Textual Reasoning over Knowledge Graphs
Knowledge graphs (KGs) store relational information in a flexible triplet schema and have become ubiquitous for information storage in domains such as web search, e-commerce, social networks, and biology. Retrieval of information from KGs is generally achieved through logical reasoning, but this …
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A SEMANTIC APPROACH FOR CONSTRUCTING KNOWLEDGE GRAPHS EXTRACTED FROM TABLES
Knowledge graphs (KGs) are networks of real-world entities with their relationships and properties and are more and more used as a means for the integration of heterogeneous sources of information in a common model that facilitates the interoperability of different applications and generates a huge …
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Reflexive Composition: Bidirectional Enhancement of Language Models and Knowledge Graphs
… inconsistencies, and restricted domain-specific knowledge. Knowledge Graphs (KGs), by contrast, provide structured and verifiable information but are expensive to build and maintain manually. This thesis introduces Reflexive Composition, a bidirectional integration framework in which LLMs and KGs …
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Neurosymbolic Reasoning for Link Prediction in Supply Chain Knowledge Graphs
This thesis is motivated by recent developments in Supply Chain Management (SCM) and Artificial Intelligence (AI). On one side, as modern supply chains become complex and interconnected with invisible dependencies, we increasingly see disruptions emerging and propagating across the network. This …
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Log file anomaly detection using knowledge graphs and graph neural networks
… have proposed representing log files as knowledge graphs (KGs) and using KG completion (KGC) techniques to predict new facts. However, current research in this area is limited, and there is no end-to-end system that includes both KG generation and KGC for log-based anomaly detection. In …
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From Information Overload to Knowledge Graphs: An Automatic Information Process Model
… web scraping, natural language processing, and knowledge graphs. The model can automatically process the full cycle of information flow, from information Search to information Collection, Information Extraction, and Information Visualization, making it a comprehensive and intelligent information …
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A Composite Syntactic-Semantic Interpretable Text Entailment Approach Exploring Commonsense Knowledge Graphs
… the detriment of another. The commonsense world knowledge necessary to support more complex inferences is also usually employed in a limited way, with most approaches sticking to shallow semantic information, leaving more elaborate semantic relationships aside. Furthermore, most systems still …
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Towards Knowledge Representation of the Biowaste-to-Chemicals Domain Using Knowledge Graphs
… to contextualize, represent and enrich this knowledge. Specifically, the domain can be divided into two broad parts: the biowaste-to-feedstocks domain, which focuses on extracting feedstocks such as biopolymers through various processes, and the feedstocks-to-chemicals domain, which involves …
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About Me and You - iTelos - A cooperative methodology for diversity-aware Knowledge Graphs
… integration and distribution of diversity-aware Knowledge Graphs (KGs). iTelos is based on three main processes. The first aims at representing a new type of data, based on the Entity Base data model, capable of concretely representing the information diversity of a context. The second is a KG …
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Knowledge Graphs and Large Language Models for Intelligent Applications in the Tourism Domain
… pivotal technologies underpinning this shift are Knowledge Graphs (KGs) and Data Lakes. Concurrently, Artificial Intelligence has emerged as a potent means to leverage data, creating knowledge and pioneering new tools across various sectors. Among these advancements, Large Language Models (LLM) …
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Next Week Tonight: Simulating Counterfactual Narratives of the future using Agentic Knowledge Graphs
… and evidencebased. NWT exposes the underlying knowledge graph, allowing users to inspect inference pathways directly. This also enables the generation of multiple, diverse scenarios from a single condition—each following different but explainable causal chains. In testing 15 counterfactual …
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Multilingual Question Answering over Knowledge Graphs building on a Model of the Lexicon-ontology Interface
… for accessing and querying this body of knowledge become increasingly important. Most question answering over linked (QALD) are induced from pairs of questions and answers using various machine-learning techniques. These systems often suffer from a lack of controllability, which makes …
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