MMKG: Multi-modal Knowledge Graphs

Liu, Ye; Li, Hui; García-Durán, Alberto; Niepert, Mathias; Oñoro-Rubio, Daniel; Rosenblum, David S.

doi:10.1007/978-3-030-21348-0_30

Cited by 120 publications

(68 citation statements)

References 22 publications

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“…An entity could be associated with information in multiple modalities, such as texts, images, and even videos. To align such entities, the task of multi-modal entity alignment is worth further investigation [17]. -EA in the open world.…”

Section: Guidelines For Practitionersmentioning

confidence: 99%

Towards Knowledge Graphs Federations: Issues and Technologies

Zhao

2021

Communications in Computer and Information Science

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In recent decades, knowledge graph plays an increasingly significant role in intelligent information services. However, for some domains, knowledge graphs are constructed for special purposes and disconnected with each other, which fails to take advantage of knowledge from different knowledge graphs. To this end, we are motivated to propose the concept of knowledge graph federation. In this keynote, we first discuss the issues about knowledge graph federation, and then we introduce two technologies of automatic knowledge graph construction, i.e., relation extraction and entity alignment. The issues in this keynote will provide guidelines for the development of knowledge graph technology.

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Section: Guidelines For Practitionersmentioning

confidence: 99%

Towards Knowledge Graphs Federations: Issues and Technologies

Zhao

2021

Communications in Computer and Information Science

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“…FB15k-237 is a subset of FB15k, which removes redundant relations in FB15k and greatly reduces the number of relations. We collect the multi-modal information of FB15k-237 according to [22]. As for the Symptoms-in-Chinese dataset, we following [56].…”

Section: Datasetsmentioning

confidence: 99%

Multi-modal Multi-relational Feature Aggregation Network for Medical Knowledge Representation Learning

Zhang

Fang

Qian

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Representation learning of medical Knowledge Graph (KG) is an important task and forms the fundamental process for intelligent medical applications such as disease diagnosis and healthcare question answering. Therefore, many embedding models have been proposed to learn vector presentations for entities and relations but they ignore three important properties of medical KG: multimodal, unbalanced and heterogeneous. Entities in the medical KG can carry unstructured multi-modal content, such as image and text. At the same time, the knowledge graph consists of multiple types of entities and relations, and each entity has various number of neighbors. In this paper, we propose a Multi-modal Multi-Relational Feature Aggregation Network (MMRFAN) for medical knowledge representation learning. To deal with the multi-modal content of the entity, we propose an adversarial feature learning model to map the textual and image information of the entity into the same vector space and learn the multi-modal common representation. To better capture the complex structure and rich semantics, we design a sampling mechanism and aggregate the neighbors with intra and interrelation attention. We evaluate our model on three knowledge graphs, including FB15k-237, IMDb and Symptoms-in-Chinese with link prediction and node classification tasks. Experimental results show that our approach outperforms state-of-the-art method. CCS CONCEPTS • Computing methodologies → Knowledge representation and reasoning.

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“…Recent developments in machine learning (e.g., see [42,43]) propose approaches for scalable link predictions in complex KGs [44]. Even though these algorithms perform well on general purpose data, and they can be used, for example, in recommendation systems [9], none of them provide reliable solutions for link prediction based on geospatial information, especially in the context of multi-scale vector geometries.…”

Section: Five-star Data: Linkingmentioning

confidence: 99%

Kadaster Knowledge Graph: Beyond the Fifth Star of Open Data

Ronzhin

Folmer

Maria³

et al. 2019

Information

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After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, users do not think in terms of datasets when they search for information. Instead, they are interested in information that is most likely scattered across several datasets. In the world of proprietary in-company data, organizations invest heavily in connecting data in knowledge graphs and/or store data in data lakes with the intention of having an integrated view of the data for analysis. With the rise of machine learning, it is a common belief that governments can improve their services, for example, by allowing citizens to get answers related to government information from virtual assistants like Alexa or Siri. To provide high-quality answers, these systems need to be fed with knowledge graphs. In this paper, we share our experience of constructing and using the first open government knowledge graph in the Netherlands. Based on the developed demonstrators, we elaborate on the value of having such a graph and demonstrate its use in the context of improved data browsing, multicriteria analysis for urban planning, and the development of location-aware chat bots.

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MMKG: Multi-modal Knowledge Graphs

Cited by 120 publications

References 22 publications

Towards Knowledge Graphs Federations: Issues and Technologies

Towards Knowledge Graphs Federations: Issues and Technologies

Multi-modal Multi-relational Feature Aggregation Network for Medical Knowledge Representation Learning

Kadaster Knowledge Graph: Beyond the Fifth Star of Open Data

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