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Using linked data to mine RDF from wikipedia's tables
The tables embedded in Wikipedia articles contain rich, semi-structured encyclopaedic content. However, the cumulative content of these tables cannot be queried against. We thus propose methods toExpand
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Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms
Learning embeddings of entities and relations using neural architectures is an effective method of performing statistical learning on large-scale relational data, such as knowledge graphs. In thisExpand
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Facilitating prediction of adverse drug reactions by using knowledge graphs and multi‐label learning models
Abstract Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect onExpand
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Triplifying Wikipedia's Tables
We are currently investigating methods to triplify the content of Wikipedia's tables. We propose that existing knowledge-bases can be leveraged to semi-automatically extract high-quality facts (inExpand
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Performance Analysis of Algorithms to Reason about XML Keys
Keys are fundamental for database management, independently of the particular data model used. In particular, several notions of XML keys have been proposed over the last decade, and theirExpand
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Using Drug Similarities for Discovery of Possible Adverse Reactions
We propose a new computational method for discovery of possible adverse drug reactions. The method consists of two key steps. First we use openly available resources to semi-automatically compile aExpand
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DRETa: Extracting RDF from Wikitables
Tables are widely used in Wikipedia articles to display relational information - they are inherently concise and information rich. However, aside from info-boxe s, there are no automatic methods toExpand
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Loss Functions in Knowledge Graph Embedding Models
Knowledge graph embedding (KGE) models have become popular for their efficient and scalable discoveries in knowledge graphs. The models learn low-rank vector representations from the knowledge graphExpand
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Learning Content Patterns from Linked Data
Linked Data (LD) datasets (e.g., DBpedia, Freebase) are used in many knowledge extraction tasks due to the high variety of domains they cover. Unfortunately, many of these datasets do not provide aExpand
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Mining Cardinalities from Knowledge Bases
Cardinality is an important structural aspect of data that has not received enough attention in the context of RDF knowledge bases (KBs). Information about cardinalities can be useful for data usersExpand
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