Inside YAGO2s

Biega, Joanna; Kuzey, Erdal; Suchanek, Fabian M.

doi:10.1145/2487788.2487935

Cited by 41 publications

(4 citation statements)

References 9 publications

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“…The agship of our work is a knowledge base called YAGO (Yet Another Great Ontology) [60,49,24,2,37,68]. This KB contains 50 million entities of general interest (such as people, organizations, or locations) and hundreds of millions of facts about them.…”

Section: Our Workmentioning

confidence: 99%

Knowledge Bases and Language Models: Complementing Forces

Suchanek,

Luu

2023

Lecture Notes in Computer Science

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Large language models (LLMs), as a particular instance of generative articial intelligence, have revolutionized natural language processing. In this invited paper, we argue that LLMs are complementary to structured data repositories such as databases or knowledge bases, which use symbolic knowledge representations. Hence, the two ways of knowledge representation will likely continue to co-exist, at least in the near future. We discuss ways that have been explored to make the two approaches work together, and point out opportunities and challenges for their symbiosis.

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Section: Our Workmentioning

confidence: 99%

Knowledge Bases and Language Models: Complementing Forces

Suchanek,

Luu

2023

Lecture Notes in Computer Science

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“…At present, some general domain knowledge maps have emerged at home and abroad. For example, the most well-known universal knowledge map in the world is Dbpedia 7 , and there is also YAGO 8 . Domestic related research started relatively late, with the main general knowledge maps being Zhishi.…”

Section: Introductionmentioning

confidence: 99%

Knowledge graph construction of traditional Tibetan medicine formulas

Zhuoma,

Jiayang,

Cai

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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In order to intuitively and succinctly reflect information, such as the dosage form and components of traditional Tibetan medicine prescriptions, and the correlation information between formulas and formulas, between formulas and traditional Tibetan medicine, we have collected and collated a certain amount of traditional Tibetan medicine formula data in this article, and constructed a knowledge map of traditional Tibetan medicine formulas. Through the construction of this atlas, traditional Tibetan medicine workers can intuitively display various direct or indirect related information of the formula, so as to facilitate a deeper understanding of the hidden knowledge in the formula information.

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“…However, knowledge graph is no longer a simple semantic network, but the sum of a series of representative technologies of knowledge engineering [7,8]. At present, the large-scale knowledge maps that have been built include ConceptNet [9], YAGO [10][11][12][13], and CN-DBpedia [14].…”

Section: Introductionmentioning

confidence: 99%

TransRFT: A Knowledge Representation Learning Model Based on a Relational Neighborhood and Flexible Translation

Wan,

Niu,

Chen

et al. 2023

Applied Sciences

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The use of knowledge graphs has grown significantly in recent years. However, entities and relationships must be transformed into forms that can be processed by computers before the construction and application of a knowledge graph. Due to its simplicity, effectiveness, and great interpretability, the translation model lead by TransE has garnered the most attention among the many knowledge representation models that have been presented. However, the performance of this model is poor when dealing with complex relations such as one-to-many, many-to-one, and reflexive relations. Therefore, a knowledge representation learning model based on a relational neighborhood and flexible translation (TransRFT) is proposed in this paper. Firstly, the triples are mapped to the relational hyperplane using the idea of TransH. Then, flexible translation is applied to relax the strict restriction h + r = t in TransE. Finally, the relational neighborhood information is added to further improve the performance of the model. The experimental results show that the model has good performance in triplet classification and link prediction.

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Inside YAGO2s

Cited by 41 publications

References 9 publications

Knowledge Bases and Language Models: Complementing Forces

Knowledge Bases and Language Models: Complementing Forces

Knowledge graph construction of traditional Tibetan medicine formulas

TransRFT: A Knowledge Representation Learning Model Based on a Relational Neighborhood and Flexible Translation

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