Adaptive Entity Alignment for Cross-Lingual Knowledge Graph

Public procurement is a large market affecting almost every organisation and individual; therefore, governments need to ensure its efficiency, transparency, and accountability, while creating healthy, competitive, and vibrant economies. In this context, open data initiatives and integration of data from multiple sources across national borders could transform the procurement market by such as lowering the barriers of entry for smaller suppliers and encouraging healthier competition, in particular by enabling cross-border bids. Increasingly more open data is published in the public sector; however, these are created and maintained in siloes and are not straightforward to reuse or maintain because of technical heterogeneity, lack of quality, insufficient metadata, or missing links to related domains. To this end, we developed an open linked data platform, called TheyBuyForYou, consisting of a set of modular APIs and ontologies to publish, curate, integrate, analyse, and visualise an EU-wide, cross-border, and cross-lingual procurement knowledge graph. We developed advanced tools and services on top of the knowledge graph for anomaly detection, cross-lingual document search, and data storytelling. This article describes the TheyBuyForYou platform and knowledge graph, reports their adoption by different stakeholders and challenges and experiences we went through while creating them, and demonstrates the usefulness of Semantic Web and Linked Data technologies for enhancing public procurement.

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Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/556 ◽

2018 ◽

Cited By ~ 29

Author(s):

Muhao Chen ◽

Yingtao Tian ◽

Kai-Wei Chang ◽

Steven Skiena ◽

Carlo Zaniolo

Keyword(s):

Experimental Results ◽

Knowledge Graph ◽

Semantic Representations ◽

Alignment Task ◽

Structured Knowledge ◽

Knowledge Graphs ◽

Cross Lingual ◽

Low Coverage

Multilingual knowledge graph (KG) embeddings provide latent semantic representations of entities and structured knowledge with cross-lingual inferences, which benefit various knowledge-driven cross-lingual NLP tasks. However, precisely learning such cross-lingual inferences is usually hindered by the low coverage of entity alignment in many KGs. Since many multilingual KGs also provide literal descriptions of entities, in this paper, we introduce an embedding-based approach which leverages a weakly aligned multilingual KG for semi-supervised cross-lingual learning using entity descriptions. Our approach performs co-training of two embedding models, i.e. a multilingual KG embedding model and a multilingual literal description embedding model. The models are trained on a large Wikipedia-based trilingual dataset where most entity alignment is unknown to training. Experimental results show that the performance of the proposed approach on the entity alignment task improves at each iteration of co-training, and eventually reaches a stage at which it significantly surpasses previous approaches. We also show that our approach has promising abilities for zero-shot entity alignment, and cross-lingual KG completion.

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Coordinated Reasoning for Cross-Lingual Knowledge Graph Alignment

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i05.6476 ◽

2020 ◽

Vol 34 (05) ◽

pp. 9354-9361

Author(s):

Kun Xu ◽

Linfeng Song ◽

Yansong Feng ◽

Yan Song ◽

Dong Yu

Keyword(s):

State Of The Art ◽

The Other ◽

Knowledge Graph ◽

Alignment Algorithm ◽

Graph Alignment ◽

Current State ◽

Alignment Model ◽

The Many ◽

The One ◽

Cross Lingual

Existing entity alignment methods mainly vary on the choices of encoding the knowledge graph, but they typically use the same decoding method, which independently chooses the local optimal match for each source entity. This decoding method may not only cause the “many-to-one” problem but also neglect the coordinated nature of this task, that is, each alignment decision may highly correlate to the other decisions. In this paper, we introduce two coordinated reasoning methods, i.e., the Easy-to-Hard decoding strategy and joint entity alignment algorithm. Specifically, the Easy-to-Hard strategy first retrieves the model-confident alignments from the predicted results and then incorporates them as additional knowledge to resolve the remaining model-uncertain alignments. To achieve this, we further propose an enhanced alignment model that is built on the current state-of-the-art baseline. In addition, to address the many-to-one problem, we propose to jointly predict entity alignments so that the one-to-one constraint can be naturally incorporated into the alignment prediction. Experimental results show that our model achieves the state-of-the-art performance and our reasoning methods can also significantly improve existing baselines.

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LYNX: Towards a Legal Knowledge Graph for Multilingual Europe

Law in Context. A Socio-legal Journal ◽

10.26826/law-in-context.v37i1.129 ◽

2020 ◽

Vol 37 (1) ◽

pp. 175-178

Author(s):

Víctor Rodríguez Doncel ◽

Elena Montiel Ponsoda

Keyword(s):

Information Retrieval ◽

Knowledge Graph ◽

Legal Knowledge ◽

Information Discovery ◽

Legal Compliance ◽

Semantic Web Technologies ◽

Legal Information ◽

Web Technologies ◽

Innovation Project ◽

Cross Lingual

Lynx is an innovation project in Europe whose objective is to develop services for legal compliance. A legal knowledge graph is built over multilingual, multijurisdictional documents using semantic web technologies. A collection of services implementing natural language techniques enables better legal information retrieval, cross-lingual answering of questions and information discovery. Three use cases are discussed, as well as the overall impact of the project.

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CLMed: A Cross-lingual Knowledge Graph Framework for Cardiovascular Diseases

Web Information Systems and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-30952-7_51 ◽

2019 ◽

pp. 512-517 ◽

Cited By ~ 3

Author(s):

Ming Sheng ◽

Han Zhang ◽

Yong Zhang ◽

Chao Li ◽

Chunxiao Xing ◽

...

Keyword(s):

Cardiovascular Diseases ◽

Knowledge Graph ◽

Cross Lingual

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Cross-Lingual Taxonomy Alignment with Bilingual Knowledge Graph Embeddings

Semantic Technology - Lecture Notes in Computer Science ◽

10.1007/978-3-319-70682-5_16 ◽

2017 ◽

pp. 251-258 ◽

Cited By ~ 2

Author(s):

Tianxing Wu ◽

Du Zhang ◽

Lei Zhang ◽

Guilin Qi

Keyword(s):

Knowledge Graph ◽

Graph Embeddings ◽

Cross Lingual

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XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and Application

Data Intelligence ◽

10.1162/dint_a_00003 ◽

2019 ◽

Vol 1 (1) ◽

pp. 77-98 ◽

Cited By ~ 3

Author(s):

Hailong Jin ◽

Chengjiang Li ◽

Jing Zhang ◽

Lei Hou ◽

Juanzi Li ◽

...

Keyword(s):

Large Scale ◽

Knowledge Bases ◽

Type Inference ◽

Knowledge Graph ◽

Entity Linking ◽

Fine Grained ◽

Cross Lingual

Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. Although XLORE is an English-Chinese bilingual knowledge graph, there are only 423,974 cross-lingual links between English instances and Chinese instances. We present XLORE2, an extension of the XLORE that is built automatically from Wikipedia, Baidu Baike and Hudong Baike. We add more facts by making cross-lingual knowledge linking, cross-lingual property matching and fine-grained type inference. We also design an entity linking system to demonstrate the effectiveness and broad coverage of XLORE2.

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Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/209 ◽

2017 ◽

Cited By ~ 42

Author(s):

Muhao Chen ◽

Yingtao Tian ◽

Mohan Yang ◽

Carlo Zaniolo

Keyword(s):

Knowledge Base ◽

Knowledge Bases ◽

Loss Functions ◽

Knowledge Graph ◽

Graph Embeddings ◽

Linear Transformations ◽

Human Labor ◽

Knowledge Graphs ◽

Cross Lingual ◽

Entity Relationships

Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across diverse human languages. Unfortunately, achieving this highly desirable cross-lingual alignment by human labor is very costly and error-prone. Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. By encoding entities and relations of each language in a separated embedding space, MTransE provides transitions for each embedding vector to its cross-lingual counterparts in other spaces, while preserving the functionalities of monolingual embeddings. We deploy three different techniques to represent cross-lingual transitions, namely axis calibration, translation vectors, and linear transformations, and derive five variants for MTransE using different loss functions. Our models can be trained on partially aligned graphs, where just a small portion of triples are aligned with their cross-lingual counterparts. The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results, with some variants consistently outperforming others on different tasks. We also explore how MTransE preserves the key properties of its monolingual counterpart.

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