57 research outputs found
Strategies for Managing Linked Enterprise Data
Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle
Demonstration of a customizable representation model for graph-based visualizations of ontologies – GIzMO
Visualizations can facilitate the development, exploration, communication, and sense-making of ontologies. Suitable visualizations, however, are highly dependent on individual use cases and targeted user groups. In this demo, we present a methodology that enables customizable definitions for ontology visualizations. We showcase its applicability by introducing GizMO, a representation model for graph-based visualizations in the form of node-link diagrams. Additionally, we present two applications that operate on the GizMO representation model and enable individual customizations for ontology visualizations
Inductive Logical Query Answering in Knowledge Graphs
Formulating and answering logical queries is a standard communication
interface for knowledge graphs (KGs). Alleviating the notorious incompleteness
of real-world KGs, neural methods achieved impressive results in link
prediction and complex query answering tasks by learning representations of
entities, relations, and queries. Still, most existing query answering methods
rely on transductive entity embeddings and cannot generalize to KGs containing
new entities without retraining the entity embeddings. In this work, we study
the inductive query answering task where inference is performed on a graph
containing new entities with queries over both seen and unseen entities. To
this end, we devise two mechanisms leveraging inductive node and relational
structure representations powered by graph neural networks (GNNs).
Experimentally, we show that inductive models are able to perform logical
reasoning at inference time over unseen nodes generalizing to graphs up to 500%
larger than training ones. Exploring the efficiency--effectiveness trade-off,
we find the inductive relational structure representation method generally
achieves higher performance, while the inductive node representation method is
able to answer complex queries in the inference-only regime without any
training on queries and scales to graphs of millions of nodes. Code is
available at https://github.com/DeepGraphLearning/InductiveQE.Comment: Accepted at NeurIPS 202
Query Embedding on Hyper-relational Knowledge Graphs
Multi-hop logical reasoning is an established problem in the field of
representation learning on knowledge graphs (KGs). It subsumes both one-hop
link prediction as well as other more complex types of logical queries.
Existing algorithms operate only on classical, triple-based graphs, whereas
modern KGs often employ a hyper-relational modeling paradigm. In this paradigm,
typed edges may have several key-value pairs known as qualifiers that provide
fine-grained context for facts. In queries, this context modifies the meaning
of relations, and usually reduces the answer set. Hyper-relational queries are
often observed in real-world KG applications, and existing approaches for
approximate query answering cannot make use of qualifier pairs. In this work,
we bridge this gap and extend the multi-hop reasoning problem to
hyper-relational KGs allowing to tackle this new type of complex queries.
Building upon recent advancements in Graph Neural Networks and query embedding
techniques, we study how to embed and answer hyper-relational conjunctive
queries. Besides that, we propose a method to answer such queries and
demonstrate in our experiments that qualifiers improve query answering on a
diverse set of query patterns
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings
Compositional generalization, the ability of an agent to generalize to unseen
combinations of latent factors, is easy for humans but hard for deep neural
networks. A line of research in cognitive science has hypothesized a process,
``iterated learning,'' to help explain how human language developed this
ability; the theory rests on simultaneous pressures towards compressibility
(when an ignorant agent learns from an informed one) and expressivity (when it
uses the representation for downstream tasks). Inspired by this process, we
propose to improve the compositional generalization of deep networks by using
iterated learning on models with simplicial embeddings, which can approximately
discretize representations. This approach is further motivated by an analysis
of compositionality based on Kolmogorov complexity. We show that this
combination of changes improves compositional generalization over other
approaches, demonstrating these improvements both on vision tasks with
well-understood latent factors and on real molecular graph prediction tasks
where the latent structure is unknown
Development of the St. Petersburg's linked open data site using Information Workbench
This paper discusses the Russian projects publishing open government data. The article also describes the development of the open linked data portal and its approach to convert open government data in the open linked data. Information Workbench is used to build this system. It allows storing, visualizing and converting data files in Semantic Web formats
Approximate Answering of Graph Queries
Knowledge graphs (KGs) are inherently incomplete because of incomplete world
knowledge and bias in what is the input to the KG. Additionally, world
knowledge constantly expands and evolves, making existing facts deprecated or
introducing new ones. However, we would still want to be able to answer queries
as if the graph were complete. In this chapter, we will give an overview of
several methods which have been proposed to answer queries in such a setting.
We will first provide an overview of the different query types which can be
supported by these methods and datasets typically used for evaluation, as well
as an insight into their limitations. Then, we give an overview of the
different approaches and describe them in terms of expressiveness, supported
graph types, and inference capabilities.Comment: Preprint of Ch. 17 "Approximate Answering of Graph Queries" in
"Compendium of Neurosymbolic Artificial Intelligence",
https://ebooks.iospress.nl/ISBN/978-1-64368-406-
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