Volume
OASIcs, Volume 99
International Research School in Artificial Intelligence in Bergen (AIB 2022)
-
Part of:
Series:
Open Access Series in Informatics (OASIcs)
Conference: International Research School in Artificial Intelligence (AIB)
Event
AIB 2022, June 7-11, 2022, University of Bergen, NorwayEditors
Publication Details
- published at: 2022-05-25
- Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- ISBN: 978-3-95977-228-0
- DBLP: db/conf/aib/aib2022
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Document
Complete Volume
OASIcs, Volume 99, AIB 2022, Complete Volume
Abstract
OASIcs, Volume 99, AIB 2022, Complete Volume
Cite as
International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 1-180, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@Proceedings{bourgaux_et_al:OASIcs.AIB.2022,
title = {{OASIcs, Volume 99, AIB 2022, Complete Volume}},
booktitle = {International Research School in Artificial Intelligence in Bergen (AIB 2022)},
pages = {1--180},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-228-0},
ISSN = {2190-6807},
year = {2022},
volume = {99},
editor = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022},
URN = {urn:nbn:de:0030-drops-159976},
doi = {10.4230/OASIcs.AIB.2022},
annote = {Keywords: OASIcs, Volume 99, AIB 2022, Complete Volume}
}
Document
Front Matter
Front Matter, Table of Contents, Preface, Conference Organization
Abstract
Front Matter, Table of Contents, Preface, Conference Organization
Cite as
International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 0:i-0:x, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@InProceedings{bourgaux_et_al:OASIcs.AIB.2022.0,
author = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
title = {{Front Matter, Table of Contents, Preface, Conference Organization}},
booktitle = {International Research School in Artificial Intelligence in Bergen (AIB 2022)},
pages = {0:i--0:x},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-228-0},
ISSN = {2190-6807},
year = {2022},
volume = {99},
editor = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.0},
URN = {urn:nbn:de:0030-drops-159984},
doi = {10.4230/OASIcs.AIB.2022.0},
annote = {Keywords: Front Matter, Table of Contents, Preface, Conference Organization}
}
Document
Invited Paper
Knowledge Graphs: A Guided Tour (Invited Paper)
Abstract
Much has been written about knowledge graphs in the past years by authors coming from diverse communities. The goal of these lecture notes is to provide a guided tour to the secondary and tertiary literature concerning knowledge graphs where the reader can learn more about particular topics. In particular, we collate together brief summaries of relevant books, book collections, book chapters, journal articles and other publications that provide introductions, primers, surveys and perspectives regarding: knowledge graphs in general; graph data models and query languages; semantics in the form of graph schemata, ontologies and rules; graph theory, algorithms and analytics; graph learning, in the form of knowledge graph embeddings and graph neural networks; and the knowledge graph life-cycle, which incorporates works on constructing, refining and publishing knowledge graphs. Where available, we highlight and provide direct links to open access literature.
Cite as
Aidan Hogan. Knowledge Graphs: A Guided Tour (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 1:1-1:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@InProceedings{hogan:OASIcs.AIB.2022.1,
author = {Hogan, Aidan},
title = {{Knowledge Graphs: A Guided Tour}},
booktitle = {International Research School in Artificial Intelligence in Bergen (AIB 2022)},
pages = {1:1--1:21},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-228-0},
ISSN = {2190-6807},
year = {2022},
volume = {99},
editor = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.1},
URN = {urn:nbn:de:0030-drops-159999},
doi = {10.4230/OASIcs.AIB.2022.1},
annote = {Keywords: knowledge graphs}
}
Document
Invited Paper
Reasoning in Knowledge Graphs (Invited Paper)
Abstract
Knowledge Graphs (KGs) are becoming increasingly popular in the industry and academia. They can be represented as labelled graphs conveying structured knowledge in a domain of interest, where nodes and edges are enriched with metaknowledge such as time validity, provenance, language, among others. Once the data is structured as a labelled graph one can apply reasoning techniques to extract relevant and insightful information. We provide an overview of deductive and inductive reasoning approaches for reasoning in KGs.
Cite as
Ricardo Guimarães and Ana Ozaki. Reasoning in Knowledge Graphs (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 2:1-2:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@InProceedings{guimaraes_et_al:OASIcs.AIB.2022.2,
author = {Guimar\~{a}es, Ricardo and Ozaki, Ana},
title = {{Reasoning in Knowledge Graphs}},
booktitle = {International Research School in Artificial Intelligence in Bergen (AIB 2022)},
pages = {2:1--2:31},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-228-0},
ISSN = {2190-6807},
year = {2022},
volume = {99},
editor = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.2},
URN = {urn:nbn:de:0030-drops-160005},
doi = {10.4230/OASIcs.AIB.2022.2},
annote = {Keywords: Knowledge Graphs, Description Logics, Knowledge Graph Embeddings}
}
Document
Invited Paper
Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces (Invited Paper)
Abstract
Ontologies and vector space embeddings are among the most popular frameworks for encoding conceptual knowledge. Ontologies excel at capturing the logical dependencies between concepts in a precise and clearly defined way. Vector space embeddings excel at modelling similarity and analogy. Given these complementary strengths, there is a clear need for frameworks that can combine the best of both worlds. In this paper, we present an overview of our recent work in this area. We first discuss the theory of conceptual spaces, which was proposed in the 1990s by Gärdenfors as an intermediate representation layer in between embeddings and symbolic knowledge bases. We particularly focus on a number of recent strategies for learning conceptual space representations from data. Next, building on the idea of conceptual spaces, we discuss approaches where relational knowledge is modelled in terms of geometric constraints. Such approaches aim at a tight integration of symbolic and geometric representations, which unfortunately comes with a number of limitations. For this reason, we finally also discuss methods in which similarity, and other forms of conceptual relatedness, are derived from vector space embeddings and subsequently used to support flexible forms of reasoning with ontologies, thus enabling a looser integration between embeddings and symbolic knowledge.
Cite as
Zied Bouraoui, Víctor Gutiérrez-Basulto, and Steven Schockaert. Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 3:1-3:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@InProceedings{bouraoui_et_al:OASIcs.AIB.2022.3,
author = {Bouraoui, Zied and Guti\'{e}rrez-Basulto, V{\'\i}ctor and Schockaert, Steven},
title = {{Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces}},
booktitle = {International Research School in Artificial Intelligence in Bergen (AIB 2022)},
pages = {3:1--3:30},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-228-0},
ISSN = {2190-6807},
year = {2022},
volume = {99},
editor = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.3},
URN = {urn:nbn:de:0030-drops-160015},
doi = {10.4230/OASIcs.AIB.2022.3},
annote = {Keywords: Conceptual Spaces, Ontologies, Vector Space Embeddings, Learning and Reasoning}
}
Document
Invited Paper
Combining Embeddings and Rules for Fact Prediction (Invited Paper)
Abstract
Knowledge bases are typically incomplete, meaning that they are missing information that we would expect to be there. Recent years have seen two main approaches to guess missing facts: Rule Mining and Knowledge Graph Embeddings. The first approach is symbolic, and finds rules such as "If two people are married, they most likely live in the same city". These rules can then be used to predict missing statements. Knowledge Graph Embeddings, on the other hand, are trained to predict missing facts for a knowledge base by mapping entities to a vector space. Each of these approaches has their strengths and weaknesses, and this article provides a survey of neuro-symbolic works that combine embeddings and rule mining approaches for fact prediction.
Cite as
Armand Boschin, Nitisha Jain, Gurami Keretchashvili, and Fabian Suchanek. Combining Embeddings and Rules for Fact Prediction (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 4:1-4:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@InProceedings{boschin_et_al:OASIcs.AIB.2022.4,
author = {Boschin, Armand and Jain, Nitisha and Keretchashvili, Gurami and Suchanek, Fabian},
title = {{Combining Embeddings and Rules for Fact Prediction}},
booktitle = {International Research School in Artificial Intelligence in Bergen (AIB 2022)},
pages = {4:1--4:30},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-228-0},
ISSN = {2190-6807},
year = {2022},
volume = {99},
editor = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.4},
URN = {urn:nbn:de:0030-drops-160021},
doi = {10.4230/OASIcs.AIB.2022.4},
annote = {Keywords: Rule Mining, Embeddings, Knowledge Bases, Deep Learning}
}
Document
Invited Paper
Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper)
Abstract
Graph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling tool for graph and network data. Though much of the work on GNNs has focused on graphs with a single edge relation, they have also been adapted to multi-relational graphs, including knowledge graphs. In such multi-relational domains, the objectives and possible applications of GNNs become quite similar to what for many years has been investigated and developed in the field of statistical relational learning (SRL). This article first gives a brief overview of the main features of GNN and SRL approaches to learning and reasoning with graph data. It analyzes then in more detail their commonalities and differences with respect to semantics, representation, parameterization, interpretability, and flexibility. A particular focus will be on relational Bayesian networks (RBNs) as the SRL framework that is most closely related to GNNs. We show how common GNN architectures can be directly encoded as RBNs, thus enabling the direct integration of "low level" neural model components with the "high level" symbolic representation and flexible inference capabilities of SRL.
Cite as
Manfred Jaeger. Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 5:1-5:42, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@InProceedings{jaeger:OASIcs.AIB.2022.5,
author = {Jaeger, Manfred},
title = {{Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches}},
booktitle = {International Research School in Artificial Intelligence in Bergen (AIB 2022)},
pages = {5:1--5:42},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-228-0},
ISSN = {2190-6807},
year = {2022},
volume = {99},
editor = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.5},
URN = {urn:nbn:de:0030-drops-160035},
doi = {10.4230/OASIcs.AIB.2022.5},
annote = {Keywords: Graph neural networks, Statistical relational learning}
}
Document
Invited Paper
Automating Moral Reasoning (Invited Paper)
Abstract
Artificial Intelligence ethics is concerned with ensuring a nonnegative ethical impact of researching, developing, deploying and using AI systems. One way to accomplish that is to enable those AI systems to make moral decisions in ethically sensitive situations, i.e., automate moral reasoning. Machine ethics is an interdisciplinary research area that is concerned with the problem of automating moral reasoning. This tutorial presents the problem of making moral decisions and gives a general overview of how a computational agent can be constructed to make moral decisions. The tutorial is aimed for students in artificial intelligence who are interested in acquiring a starting understanding of the basic concepts and a gateway to the literature in machine ethics.
Cite as
Marija Slavkovik. Automating Moral Reasoning (Invited Paper). In International Research School in Artificial Intelligence in Bergen (AIB 2022). Open Access Series in Informatics (OASIcs), Volume 99, pp. 6:1-6:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
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@InProceedings{slavkovik:OASIcs.AIB.2022.6,
author = {Slavkovik, Marija},
title = {{Automating Moral Reasoning}},
booktitle = {International Research School in Artificial Intelligence in Bergen (AIB 2022)},
pages = {6:1--6:13},
series = {Open Access Series in Informatics (OASIcs)},
ISBN = {978-3-95977-228-0},
ISSN = {2190-6807},
year = {2022},
volume = {99},
editor = {Bourgaux, Camille and Ozaki, Ana and Pe\~{n}aloza, Rafael},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.AIB.2022.6},
URN = {urn:nbn:de:0030-drops-160043},
doi = {10.4230/OASIcs.AIB.2022.6},
annote = {Keywords: Machine ethics, artificial morality, artificial moral agents}
}