Knowledge representation and reasoning ≈ Knowledge representation and reasoning
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A Survey on Knowledge Graphs: Representation, Acquisition, and Applications Open
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction toward cognition and human-level intelligence. In th…
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ERNIE: Enhanced Representation through Knowledge Integration Open
We present a novel language representation model enhanced by knowledge called ERNIE (Enhanced Representation through kNowledge IntEgration). Inspired by the masking strategy of BERT, ERNIE is designed to learn language representation enhan…
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Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems Open
Despite its great success, machine learning can have its limits when dealing\nwith insufficient training data. A potential solution is the additional\nintegration of prior knowledge into the training process which leads to the\nnotion of i…
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Representation Learning of Knowledge Graphs with Entity Descriptions Open
Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous low-dimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between …
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Modeling Relation Paths for Representation Learning of Knowledge Bases Open
Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space.Most existing methods only consider direct relations in representation learning.We argue that multiple-step relation paths al…
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KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning Open
Bill Yuchen Lin, Xinyue Chen, Jamin Chen, Xiang Ren. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Knowledge Graphs: Opportunities and Challenges Open
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowled…
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QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering Open
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, Jure Leskovec. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
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SenticNet 5: Discovering Conceptual Primitives for Sentiment Analysis by Means of Context Embeddings Open
With the recent development of deep learning, research in AI has gained new vigor and prominence. While machine learning has succeeded in revitalizing many research fields, such as computer vision, speech recognition, and medical diagnosis…
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An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge Open
With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Question answering over knowledge base (KB-QA) is one of the promising approaches to access the substantial knowl…
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Variational Reasoning for Question Answering With Knowledge Graph Open
Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to b…
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OpenKE: An Open Toolkit for Knowledge Embedding Open
We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to …
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Knowledge Graph Completion: A Review Open
Knowledge graph completion (KGC) is a hot topic in knowledge graph construction and related applications, which aims to complete the structure of knowledge graph by predicting the missing entities or relationships in knowledge graph and mi…
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Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level Open
Question Answering (QA) systems over Knowledge Graphs (KG) automatically answer natural language questions using facts contained in a knowledge graph. Simple questions, which can be answered by the extraction of a single fact, constitute a…
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Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning Open
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, Yejin Choi. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 20…
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A Survey on Application of Knowledge Graph Open
Knowledge graphs, representation of information as a semantic graph, have caused wide concern in both industrial and academic world. Their property of providing semantically structured information has brought important possible solutions f…
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A survey of ontology learning techniques and applications Open
Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificial…
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Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding Open
We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visual question answering (NS-VQA) system first recovers a struc…
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Image-embodied Knowledge Representation Learning Open
Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from ent…
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Applications of Answer Set Programming Open
Answer set programming (ASP) has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP s…
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KQML - A Language and Protocol for Knowledge and Information Exchange Open
This paper describes the design of and experimentation with the Knowledge Query and Manipulation Language( KQML), a new language and protocol for exchanging information and knowledge. This work is part a larger effort, the ARPA Knowledge S…
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The use of ontologies for effective knowledge modelling and information retrieval Open
The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex datab…
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Variational Reasoning for Question Answering with Knowledge Graph Open
Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts. However, it is challenging to b…
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Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding Open
We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visual question answering (NS-VQA) system first recovers a struc…
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Explicit Knowledge-based Reasoning for Visual Question Answering Open
We describe a method for visual question answering which is capable of reasoning about an image on the basis of information extracted from a large-scale knowledge base. The method not only answers natural language questions using concepts …
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CoLAKE: Contextualized Language and Knowledge Embedding Open
With the emerging branch of incorporating factual knowledge into pre-trained language models such as BERT, most existing models consider shallow, static, and separately pre-trained entity embeddings, which limits the performance gains of t…
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Generated Knowledge Prompting for Commonsense Reasoning Open
Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, Hannaneh Hajishirzi. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.
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KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning Open
Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language genera…
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Sentence Representation Method Based on Multi-Layer Semantic Network Open
With the development of artificial intelligence, more and more people hope that computers can understand human language through natural language technology, learn to think like human beings, and finally replace human beings to complete the…
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Neuro-symbolic representation learning on biological knowledge graphs Open
Motivation Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are appli…