Canonicalizing Knowledge Base Literals
Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching.
Code (2)
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Incorporating Literals into Knowledge Graph Embeddings
Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entiti…
Entity EmbeddingsKnowledge Graph EmbeddingsKnowledge GraphsLink PredictionA Probabilistic Model for Canonicalizing Named Entity Mentions
SM-based Semantics for Answer Set Programs Containing Conditional Literals and Arithmetic
Modern answer set programming solvers such as CLINGO support advanced language constructs that improve the expressivity and conciseness of logic programs. Conditional literals are one such construct. They form "subformul…
Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets
Link Prediction(LP) is an essential task over Knowledge Graphs(KGs), traditionally focussed on using and predicting the relations between entities. Textual entity descriptions have already been shown to be valuable, but …
Knowledge GraphsLink PredictionHow Contentious Terms About People and Cultures are Used in Linked Open Data
Web resources in linked open data (LOD) are comprehensible to humans through literal textual values attached to them, such as labels, notes, or comments. Word choices in literals may not always be neutral. When outdated …
DescriptiveWord Sense Disambiguation