Knowledge Graph Embeddings and Explainable AI

• URL: http://arxiv.org/abs/2004.14843v1
• Date: Thu, 30 Apr 2020 14:55:09 GMT
• Title: Knowledge Graph Embeddings and Explainable AI
• Authors: Federico Bianchi and Gaetano Rossiello and Luca Costabello and Matteo Palmonari and Pasquale Minervini
• Abstract summary: We introduce the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings.
• Score: 29.205234615756822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings.

Related papers

• From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures [2.6451388057494283]
  This paper introduces a post-hoc explainable AI method tailored for Knowledge Graph Embedding models. Our approach directly decodes the latent representations encoded by Knowledge Graph Embedding models. By identifying distinct structures within the subgraph neighborhoods of similarly embedded entities, our method translates these insights into human-understandable symbolic rules and facts.
  arXiv  Detail & Related papers  (2024-06-03T19:54:11Z)
• Knowledge Graph Extension by Entity Type Recognition [2.8231106019727195]
  We propose a novel knowledge graph extension framework based on entity type recognition. The framework aims to achieve high-quality knowledge extraction by aligning the schemas and entities across different knowledge graphs.
  arXiv  Detail & Related papers  (2024-05-03T19:55:03Z)
• Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference [0.5439020425819]
  This work bridges the gap between traditional transductive knowledge graph embedding approaches and more recent inductive relation prediction models. We introduce a generalized form of harmonic extension which leverages representations learned through transductive embedding methods to infer representations of new entities introduced at inference time as in the inductive setting. In experiments on a number of large-scale knowledge graph embedding benchmarks, we find that this approach for extending the functionality of transductive knowledge graph embedding models is competitive with--and in some scenarios outperforms--several state-of-the-art models derived explicitly for such inductive tasks.
  arXiv  Detail & Related papers  (2023-09-07T15:24:18Z)
• Large Language Models and Knowledge Graphs: Opportunities and Challenges [51.23244504291712]
  Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge.
  arXiv  Detail & Related papers  (2023-08-11T20:16:57Z)
• Computing Rule-Based Explanations of Machine Learning Classifiers using Knowledge Graphs [62.997667081978825]
  We use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier. In particular, we introduce a novel method for extracting and representing black-box explanations of its operation, in the form of first-order logic rules expressed in the terminology of the knowledge graph.
  arXiv  Detail & Related papers  (2022-02-08T16:21:49Z)
• Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation [74.14009444678031]
  We propose Knowledge-aware Conditional Attention Networks (KCAN) to incorporate knowledge graph into a recommender system. We use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations.
  arXiv  Detail & Related papers  (2021-11-03T09:40:43Z)
• Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding [1.5469452301122175]
  We show that knowledge graph embedding is naturally expressed in the topological and categorical language of textitcellular sheaves A knowledge graph embedding can be described as an approximate global section of an appropriate textitknowledge sheaf over the graph. The resulting embeddings can be easily adapted for reasoning over composite relations without special training.
  arXiv  Detail & Related papers  (2021-10-07T20:54:40Z)
• KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA [107.7091094498848]
  One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types of knowledge representations and reasoning. First, implicit knowledge which can be learned effectively from unsupervised language pre-training and supervised training data with transformer-based models.
  arXiv  Detail & Related papers  (2020-12-20T20:13:02Z)
• KompaRe: A Knowledge Graph Comparative Reasoning System [85.72488258453926]
  This paper introduces comparative reasoning over knowledge graphs, which aims to infer the commonality and inconsistency with respect to multiple pieces of clues. We develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
  arXiv  Detail & Related papers  (2020-11-06T04:57:37Z)
• A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts [81.4757750425247]
  We study question answering over a dynamic textual environment. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner.
  arXiv  Detail & Related papers  (2020-04-25T04:53:54Z)
• Knowledge Graphs [43.06435841693428]
  We motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.
  arXiv  Detail & Related papers  (2020-03-04T20:20:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.