Knowledge Graphs for eXplainable Artificial Intelligence最新文献

Knowledge Graph Embeddings and Explainable AI 知识图谱嵌入与可解释人工智能

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 2020-04-30 DOI: 10.3233/SSW200011

Federico Bianchi, Gaetano Rossiello, Luca Costabello, M. Palmonari, Pasquale Minervini

引用次数: 69

Foundations of Explainable Knowledge-Enabled Systems 可解释的知识支持系统的基础

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 2020-03-17 DOI: 10.3233/SSW200010

Shruthi Chari, Daniel Gruen, O. Seneviratne, D. McGuinness

引用次数: 25

Neuro-symbolic Architectures for Context Understanding 上下文理解的神经符号体系结构

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 2020-03-09 DOI: 10.3233/SSW200016

A. Oltramari, Jonathan M Francis, C. Henson, Kaixin Ma, Ruwan Wickramarachchi

{"title":"Neuro-symbolic Architectures for Context Understanding","authors":"A. Oltramari, Jonathan M Francis, C. Henson, Kaixin Ma, Ruwan Wickramarachchi","doi":"10.3233/SSW200016","DOIUrl":"https://doi.org/10.3233/SSW200016","url":null,"abstract":"Computational context understanding refers to an agent's ability to fuse disparate sources of information for decision-making and is, therefore, generally regarded as a prerequisite for sophisticated machine reasoning capabilities, such as in artificial intelligence (AI). Data-driven and knowledge-driven methods are two classical techniques in the pursuit of such machine sense-making capability. However, while data-driven methods seek to model the statistical regularities of events by making observations in the real-world, they remain difficult to interpret and they lack mechanisms for naturally incorporating external knowledge. Conversely, knowledge-driven methods, combine structured knowledge bases, perform symbolic reasoning based on axiomatic principles, and are more interpretable in their inferential processing; however, they often lack the ability to estimate the statistical salience of an inference. To combat these issues, we propose the use of hybrid AI methodology as a general framework for combining the strengths of both approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks. We further ground our discussion in two applications of neuro-symbolism and, in both cases, show that our systems maintain interpretability while achieving comparable performance, relative to the state-of-the-art.","PeriodicalId":331476,"journal":{"name":"Knowledge Graphs for eXplainable Artificial Intelligence","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116347862","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}

引用次数: 17

Differentiable Reasoning on Large Knowledge Bases and Natural Language 基于大知识库和自然语言的可微推理

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 2019-12-17 DOI: 10.1609/AAAI.V34I04.5962

Pasquale Minervini, Matko Bovsnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette

{"title":"Differentiable Reasoning on Large Knowledge Bases and Natural Language","authors":"Pasquale Minervini, Matko Bovsnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette","doi":"10.1609/AAAI.V34I04.5962","DOIUrl":"https://doi.org/10.1609/AAAI.V34I04.5962","url":null,"abstract":"Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. General neural architectures that jointly learn representations and transformations of text are very data-inefficient, and it is hard to analyse their reasoning process. These issues are addressed by end-to-end differentiable reasoning systems such as Neural Theorem Provers (NTPs), although they can only be used with small-scale symbolic KBs. In this paper we first propose Greedy NTPs (GNTPs), an extension to NTPs addressing their complexity and scalability limitations, thus making them applicable to real-world datasets. This result is achieved by dynamically constructing the computation graph of NTPs and including only the most promising proof paths during inference, thus obtaining orders of magnitude more efficient models. Then, we propose a novel approach for jointly reasoning over KBs and textual mentions, by embedding logic facts and natural language sentences in a shared embedding space. We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models. Source code, datasets, and supplementary material are available online at this https URL.","PeriodicalId":331476,"journal":{"name":"Knowledge Graphs for eXplainable Artificial Intelligence","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129857681","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}

引用次数: 79

Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning 机器学习中解释错误的知识表示和推理方法

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.3233/SSW200017

Marjan Alirezaie, Martin Längkvist, A. Loutfi

引用次数: 0

Knowledge-Aware Interpretable Recommender Systems 知识感知可解释推荐系统

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.3233/SSW200014

V. W. Anelli, Vito Bellini, T. D. Noia, E. Sciascio

{"title":"Knowledge-Aware Interpretable Recommender Systems","authors":"V. W. Anelli, Vito Bellini, T. D. Noia, E. Sciascio","doi":"10.3233/SSW200014","DOIUrl":"https://doi.org/10.3233/SSW200014","url":null,"abstract":"Recommender systems are everywhere, from e-commerce to streaming platforms. They help users lost in the maze of available information, items and services to find their way. Among them, over the years, approaches based on machine learning techniques have shown particularly good performance for top-N recommendations engines. Unfortunately, they mostly behave as black-boxes and, even when they embed some form of description about the items to recommend, after the training phase they move such descriptions in a latent space thus loosing the actual explicit semantics of recommended items. As a consequence, the system designers struggle at providing satisfying explanations to the recommendation list provided to the end user. In this chapter, we describe two approaches to recommendation which make use of the semantics encoded in a knowledge graph to train interpretable models which keep the original semantics of the items description thus providing a powerful tool to automatically compute explainable results. The two methods relies on two completely different machine learning algorithms, namely, factorization machines and autoencoder neural networks. We also show how to measure the interpretability of the model through the introduction of two metrics: semantic accuracy and robustness.","PeriodicalId":331476,"journal":{"name":"Knowledge Graphs for eXplainable Artificial Intelligence","volume":"28 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":"127862314","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}

引用次数: 5

Explanations in Predictive Analytics: Case Studies 预测分析中的解释:案例研究

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.3233/SSW200019

Jiewen Wu, Minh-Thuan Nguyen, G. Ngo, Nancy F. Chen

引用次数: 0

Benchmarking the Lifecycle of Knowledge Graphs 对知识图谱生命周期进行基准测试

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.3233/SSW200012

Michael Röder, M. A. Sherif, Muhammad Saleem, Felix Conrads, A. N. Ngomo

引用次数: 0

Generating Explanations in Natural Language from Knowledge Graphs 从知识图生成自然语言解释

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.3233/SSW200020

Diego Moussallem, René Speck, A. N. Ngomo

引用次数: 4

Managing Identity in Knowledge-Based Explainable Systems 在基于知识的可解释系统中管理身份

Knowledge Graphs for eXplainable Artificial Intelligence Pub Date : 1900-01-01 DOI: 10.3233/SSW200025

Ilaria Tiddi, Joe Raad

引用次数: 0