72 research outputs found
Type prediction in RDF knowledge bases using hierarchical multilabel classification
Large Semantic Web knowledge bases are often noisy, incorrect, and incomplete with respect to type information. Automatic type prediction can help reduce such incompleteness, and, as previous works show, statistical methods are well-suited for this kind of data. Since most Semantic Web knowledge bases come with an ontology defining a type hierarchy, in this paper, we rephrase the type prediction problem as a hierarchical multilabel classification problem. We propose SLCN, a modification of the local classifier per node approach, which performs feature selection, instance sampling, and class balancing for each local classifier. Our approach improves scalability, facilitating its application on large Semantic Web datasets with high-dimensional feature and label spaces. We compare the performance of our proposed method with a state-of-the-art type prediction approach and popular hierarchical multilabel classifiers, and report on experiments with large-scale RDF datasets
Entity Type Prediction in Knowledge Graphs using Embeddings
Open Knowledge Graphs (such as DBpedia, Wikidata, YAGO) have been recognized
as the backbone of diverse applications in the field of data mining and
information retrieval. Hence, the completeness and correctness of the Knowledge
Graphs (KGs) are vital. Most of these KGs are mostly created either via an
automated information extraction from Wikipedia snapshots or information
accumulation provided by the users or using heuristics. However, it has been
observed that the type information of these KGs is often noisy, incomplete, and
incorrect. To deal with this problem a multi-label classification approach is
proposed in this work for entity typing using KG embeddings. We compare our
approach with the current state-of-the-art type prediction method and report on
experiments with the KGs
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
An entropy-based class assignment detection approach for RDF data
The RDF-style Knowledge Bases usually contain a certain level of noises known as Semantic Web data quality issues. This paper has introduced a new Semantic Web data quality issue called Incorrect Class Assignment problem that shows the incorrect assignment between instances in the instance-level and corresponding classes in an ontology. We have proposed an approach called CAD (Class Assignment Detector) to find the correctness and incorrectness of relationships between instances and classes by analyzing features of classes in an ontology. Initial experiments conducted on a dataset demonstrate the effectiveness of CAD
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions
The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task of assigning or inferring the semantic type of an entity in a KG. This paper presents \textit{GRAND}, a novel approach for entity typing leveraging different graph walk strategies in RDF2vec together with textual entity descriptions. RDF2vec first generates graph walks and then uses a language model to obtain embeddings for each node in the graph. This study shows that the walk generation strategy and the embedding model have a significant effect on the performance of the entity typing task. The proposed approach outperforms the baseline approaches on the benchmark datasets DBpedia and FIGER for entity typing in KGs for both fine-grained and coarse-grained classes. The results show that the combination of order-aware RDF2vec variants together with the contextual embeddings of the textual entity descriptions achieve the best results
Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier
The Swiss classification of surgical interventions (CHOP) has to be used in daily practice by physicians to classify clinical procedures. Its purpose is to encode the delivered healthcare services for the sake of quality assurance and billing. For encoding a procedure, a code of a maximal of 6-digits has to be selected from the classification system, which is currently realized by a rule-based system composed of encoding experts and a manual search in the CHOP catalog. In this paper, we will investigate the possibility of automatic CHOP code generation based on a short query to enable automatic support of manual classification. The wide and deep hierarchy of CHOP and the differences between text used in queries and catalog descriptions are two apparent obstacles for training and deploying a learning-based algorithm. Because of these challenges, there is a need for an appropriate classification approach. We evaluate different strategies (multi-class non-terminal and per-node classifications) with different configurations so that a flexible modular solution with high accuracy and efficiency can be provided. The results clearly show that the per-node binary classification outperforms the non-terminal multi-class classification with an F1-micro measure between 92.6 and 94%. The hierarchical prediction based on per-node binary classifiers achieved a high exact match by the single code assignment on the 5-fold cross-validation. In conclusion, the hierarchical context from the CHOP encoding can be employed by both classifier training and representation learning. The hierarchical features have all shown improvement in the classification performances under different configurations, respectively: the stacked autoencoder and training examples aggregation using true path rules as well as the unified vocabulary space have largely increased the utility of hierarchical features. Additionally, the threshold adaption through Bayesian aggregation has largely increased the vertical reachability of the per node classification. All the trainable nodes can be triggered after the threshold adaption, while the F1 measures at code levels 3–6 have been increased from 6 to 89% after the threshold adaption
Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Embedding based Knowledge Graph (KG) Completion has gained much attention
over the past few years. Most of the current algorithms consider a KG as a
multidirectional labeled graph and lack the ability to capture the semantics
underlying the schematic information. In a separate development, a vast amount
of information has been captured within the Large Language Models (LLMs) which
has revolutionized the field of Artificial Intelligence. KGs could benefit from
these LLMs and vice versa. This vision paper discusses the existing algorithms
for KG completion based on the variations for generating KG embeddings. It
starts with discussing various KG completion algorithms such as transductive
and inductive link prediction and entity type prediction algorithms. It then
moves on to the algorithms utilizing type information within the KGs, LLMs, and
finally to algorithms capturing the semantics represented in different
description logic axioms. We conclude the paper with a critical reflection on
the current state of work in the community and give recommendations for future
directions
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