Pub Date : 1900-01-01DOI: 10.4230/OASIcs.AIB.2022.1
A. Hogan
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. 2012 ACM Subject Classification Information systems → Graph-based database models; Information systems → Information integration; Computing methodologies → Artificial intelligence
{"title":"Knowledge Graphs: A Guided Tour (Invited Paper)","authors":"A. Hogan","doi":"10.4230/OASIcs.AIB.2022.1","DOIUrl":"https://doi.org/10.4230/OASIcs.AIB.2022.1","url":null,"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. 2012 ACM Subject Classification Information systems → Graph-based database models; Information systems → Information integration; Computing methodologies → Artificial intelligence","PeriodicalId":110801,"journal":{"name":"International Research School in Artificial Intelligence in Bergen","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130285501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.AIB.2022.6
M. Slavkovik
{"title":"Automating Moral Reasoning (Invited Paper)","authors":"M. Slavkovik","doi":"10.4230/OASIcs.AIB.2022.6","DOIUrl":"https://doi.org/10.4230/OASIcs.AIB.2022.6","url":null,"abstract":"","PeriodicalId":110801,"journal":{"name":"International Research School in Artificial Intelligence in Bergen","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133848334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.AIB.2022.2
Ricardo Guimarães, A. Ozaki
{"title":"Reasoning in Knowledge Graphs (Invited Paper)","authors":"Ricardo Guimarães, A. Ozaki","doi":"10.4230/OASIcs.AIB.2022.2","DOIUrl":"https://doi.org/10.4230/OASIcs.AIB.2022.2","url":null,"abstract":"","PeriodicalId":110801,"journal":{"name":"International Research School in Artificial Intelligence in Bergen","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127032012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.AIB.2022.3
Zied Bouraoui, Víctor Gutiérrez-Basulto, S. Schockaert
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.
{"title":"Integrating Ontologies and Vector Space Embeddings Using Conceptual Spaces (Invited Paper)","authors":"Zied Bouraoui, Víctor Gutiérrez-Basulto, S. Schockaert","doi":"10.4230/OASIcs.AIB.2022.3","DOIUrl":"https://doi.org/10.4230/OASIcs.AIB.2022.3","url":null,"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.","PeriodicalId":110801,"journal":{"name":"International Research School in Artificial Intelligence in Bergen","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134172010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.AIB.2022.5
M. Jaeger
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. 2012 ACM Subject Classification Computing methodologies → Logical and relational learning; Computing methodologies → Neural networks
{"title":"Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper)","authors":"M. Jaeger","doi":"10.4230/OASIcs.AIB.2022.5","DOIUrl":"https://doi.org/10.4230/OASIcs.AIB.2022.5","url":null,"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. 2012 ACM Subject Classification Computing methodologies → Logical and relational learning; Computing methodologies → Neural networks","PeriodicalId":110801,"journal":{"name":"International Research School in Artificial Intelligence in Bergen","volume":"32 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113995812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4230/OASIcs.AIB.2022.4
Armand Boschin, Nitisha Jain, Gurami Keretchashvili, Fabian M. Suchanek
{"title":"Combining Embeddings and Rules for Fact Prediction (Invited Paper)","authors":"Armand Boschin, Nitisha Jain, Gurami Keretchashvili, Fabian M. Suchanek","doi":"10.4230/OASIcs.AIB.2022.4","DOIUrl":"https://doi.org/10.4230/OASIcs.AIB.2022.4","url":null,"abstract":"","PeriodicalId":110801,"journal":{"name":"International Research School in Artificial Intelligence in Bergen","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126584795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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