1 research outputs found
Automatic refinement of large-scale cross-domain knowledge graphs
Knowledge graphs are a way to represent complex structured and unstructured information
integrated into an ontology, with which one can reason about the existing
information to deduce new information or highlight inconsistencies. Knowledge
graphs are divided into the terminology box (TBox), also known as ontology, and
the assertions box (ABox). The former consists of a set of schema axioms defining
classes and properties which describe the data domain. Whereas the ABox consists
of a set of facts describing instances in terms of the TBox vocabulary.
In the recent years, there have been several initiatives for creating large-scale
cross-domain knowledge graphs, both free and commercial, with DBpedia, YAGO,
and Wikidata being amongst the most successful free datasets. Those graphs are
often constructed with the extraction of information from semi-structured knowledge,
such as Wikipedia, or unstructured text from the web using NLP methods. It
is unlikely, in particular when heuristic methods are applied and unreliable sources
are used, that the knowledge graph is fully correct or complete. There is a tradeoff
between completeness and correctness, which is addressed differently in each
knowledge graph’s construction approach.
There is a wide variety of applications for knowledge graphs, e.g. semantic
search and discovery, question answering, recommender systems, expert systems
and personal assistants. The quality of a knowledge graph is crucial for its applications.
In order to further increase the quality of such large-scale knowledge graphs,
various automatic refinement methods have been proposed. Those methods try to
infer and add missing knowledge to the graph, or detect erroneous pieces of information.
In this thesis, we investigate the problem of automatic knowledge graph
refinement and propose methods that address the problem from two directions, automatic
refinement of the TBox and of the ABox.
In Part I we address the ABox refinement problem. We propose a method for
predicting missing type assertions using hierarchical multilabel classifiers and ingoing/
outgoing links as features. We also present an approach to detection of relation
assertion errors which exploits type and path patterns in the graph. Moreover,
we propose an approach to correction of relation errors originating from confusions
between entities. Also in the ABox refinement direction, we propose a knowledge
graph model and process for synthesizing knowledge graphs for benchmarking
ABox completion methods.
In Part II we address the TBox refinement problem. We propose methods for inducing flexible relation constraints from the ABox, which are expressed using
SHACL.We introduce an ILP refinement step which exploits correlations between
numerical attributes and relations in order to the efficiently learn Horn rules with
numerical attributes. Finally, we investigate the introduction of lexical information
from textual corpora into the ILP algorithm in order to improve quality of induced
class expressions