YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames

Rebele, Thomas; Suchanek, Fabian M.; Hoffart, Johannes; Biega, Joanna; Kuzey, Erdal; Weikum, Gerhard

doi:10.1007/978-3-319-46547-0_19

Cited by 241 publications

(172 citation statements)

References 23 publications

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“…Multi-Aspect Entity Features. Existing KGs Suchanek et al, 2008;Rebele et al, 2016) provide multi-aspect information of entities. In this section, we mainly focus on the following three aspects: topological connections, relations, and attributes.…”

Section: Cross-lingual Graph Embeddingsmentioning

confidence: 99%

Aligning Cross-Lingual Entities with Multi-Aspect Information

Yang

Zou

Shi

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

134

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Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a source language with their counterparts in target languages. In this work, we investigate embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other. Specifically, we apply graph convolutional networks (GCNs) to combine multiaspect information of entities, including topological connections, relations, and attributes of entities, to learn entity embeddings. To exploit the literal descriptions of entities expressed in different languages, we propose two uses of a pretrained multilingual BERT model to bridge cross-lingual gaps. We further propose two strategies to integrate GCN-based and BERTbased modules to boost performance. Extensive experiments on two benchmark datasets demonstrate that our method significantly outperforms existing systems.

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Section: Cross-lingual Graph Embeddingsmentioning

confidence: 99%

Aligning Cross-Lingual Entities with Multi-Aspect Information

Yang

Zou

Shi

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

134

View full text Add to dashboard Cite

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“…Freebase (Bollacker et al, 2008(Bollacker et al, , 2007, later acquired by Google and dissolved in Google Knowledge Graph, mines resources such as Wikipedia and enables a collaborative environment to handle the organization, representation, and integration of large and diverse data sets, thus facilitating continuous growth. On the other hand, Yago (Rebele et al, 2016) follows the Resource Description Framework (RDF) (World Wide Web Consortium, 2014) triplets to store relational data mined from Wikipedia, WordNet, and GeoNames and filtered to fit predefined relational structures.…”

Section: Semantic Representation Of Informationmentioning

confidence: 99%

“…A large semantic network is normally composed of a library of semantic entities (e.g., words or phrases) and their semantic relations, which are often statistically or linguistically "learned" based on collaboratively edited and accumulated knowledge databases, such as Wikipedia. Over the past decade, semantic networks have been enabled by the development of large-scale knowledge basis and ontology databases, such as WordNet (Fellbaum, 2012;Miller et al, 1990), ConceptNet (Speer et al, 2016;Speer & Havasi, 2012;Speer & Lowry-Duda, 2017), never-ending language learning (NeLL) (Mitchell et al, 2015), Freebase (Bollacker et al, 2008(Bollacker et al, , 2007 and Yago (Rebele et al, 2016), for various general applications in text data mining, natural language processing (NLP), knowledge discovery, information retrieval and artificial intelligence. Likewise, the proprietary Google Knowledge Graph 1 provides the backbone behind Google's semantic search and answer features for web searches, Gmail, and Google Assistant, for example.…”

Section: Introductionmentioning

confidence: 99%

TechNet: Technology semantic network based on patent data

Sarica

Luo

Wood

2020

Expert Systems with Applications

133

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The growing developments in general semantic networks, knowledge graphs and ontology databases have motivated us to build a large-scale comprehensive semantic network of technology-related data for engineering knowledge discovery, technology search and retrieval, and artificial intelligence for engineering design and innovation. Specially, we constructed a technology semantic network (TechNet) that covers the elemental concepts in all domains of technology and their semantic associations by mining the complete U.S. patent database from 1976. To derive the TechNet, natural language processing techniques were utilized to extract terms from massive patent texts and recent word embedding algorithms were employed to vectorize such terms and establish their semantic relationships. We report and evaluate the TechNet for retrieving terms and their pairwise relevance that is meaningful from a technology and engineering design perspective. The TechNet may serve as an infrastructure to support a wide range of applications, e.g., technical text summaries, search query predictions, relational knowledge discovery, and design ideation support, in the context of engineering and technology, and complement or enrich existing semantic databases. To enable such applications, we made the TechNet public via an online interface and APIs for public users to retrieve technologyrelated terms and their relevancies.

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“…In order to produce cartographic visualizations, the necessity to adapt to different contexts [3] and to complement existing resources with a precise historical gazetteer [7] has been highlighted, as combined approaches lead to more complete or finer knowledge bases [14]. Such databases of geographic locations featuring coordinates and relevant metadata exist, but their development is challenging [16] even for 20th century Europe [12].…”

Section: Description 21 Approachmentioning

confidence: 99%

Towards a toolbox to map historical text collections

Barbaresi

2017

Proceedings of the 11th Workshop on Geographic Information Retrieval

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This article presents an effort to integrate spatial and textual data processing tools into a modular software package which features preprocessing, geocoding, disambiguation and visualization. CCS CONCEPTS• Human-centered computing → Visualization toolkits; KEYWORDSInformation extraction, Geocoding, Maps, Open Source Software ACM Reference Format: Adrien Barbaresi. 2017. Towards a toolbox to map historical text collections.

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YAGO: A Multilingual Knowledge Base from Wikipedia, Wordnet, and Geonames

Cited by 241 publications

References 23 publications

Aligning Cross-Lingual Entities with Multi-Aspect Information

Aligning Cross-Lingual Entities with Multi-Aspect Information

TechNet: Technology semantic network based on patent data

Towards a toolbox to map historical text collections

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