Improving Neural Knowledge Base Completion with Cross-Lingual Projections

Klein, Patrick; Ponzetto, Simone Paolo; Glavaš, Goran

doi:10.18653/v1/e17-2083

Cited by 9 publications

(4 citation statements)

References 28 publications

(17 reference statements)

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“…RDF2Vec [19] uses local information of KB structures to generate sequences of entities and employs language modeling approaches to learn entity embeddings for machine learning tasks. For cross-lingual tasks, [12] extends NTNKBC [4] for cross-lingual KB completion. [7] uses a neural network approach that translates English KBs into Chinese to expand Chinese KBs.…”

Section: Kb Embeddingmentioning

confidence: 99%

Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding

Sun

2017

Lecture Notes in Computer Science

309

307

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Abstract. Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation.

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Section: Kb Embeddingmentioning

confidence: 99%

Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding

Sun

2017

Lecture Notes in Computer Science

309

307

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“…Using the knowledge bases for both the languages, the facts from the source language are projected with the target language for knowledge base completion. For instance, [26] and [27] developed a knowledge base completion model based on vector representation by representing the concepts in multiple languages in a unified vector space. But these models are not applicable in the absence of a knowledge base for a target language.…”

Section: Background and Related Workmentioning

confidence: 99%

Clustering and Bootstrapping Based Framework for News Knowledge Base Completion

Srinivasa¹,

Thilagam

2021

cai

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Extracting the facts, namely entities and relations, from unstructured sources is an essential step in any knowledge base construction. At the same time, it is also necessary to ensure the completeness of the knowledge base by incrementally extracting the new facts from various sources. To date, the knowledge base completion is studied as a problem of knowledge refinement where the missing facts are inferred by reasoning about the information already present in the knowledge base. However, facts missed while extracting the information from multilingual sources are ignored. Hence, this work proposed a generic framework for knowledge base completion to enrich a knowledge base of crime-related facts extracted from online news articles in the English language, with the facts extracted from low resourced Indian language Hindi news articles. Using the framework, information from any low-resourced language news articles can be extracted without using language-specific tools like POS tags and using an appropriate machine translation tool. To achieve this, a clustering algorithm is proposed, which explores the redundancy among the bilingual collection of news articles by representing the clusters with knowledge base facts unlike the existing Bag of Words representation. From each cluster, the facts extracted from English language articles are bootstrapped to extract the facts from comparable Hindi language articles. This way of bootstrapping within the cluster helps to identify the sentences from a low-resourced language that are enriched with new information related to the facts extracted from a high-resourced language like English. The empirical result shows that the proposed clustering algorithm produced more accurate and high-quality clusters for monolingual and cross-lingual facts, respectively. Experiments also proved that the proposed framework achieves a high recall rate in extracting the new facts from Hindi news articles.

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“…Chen et al [14], Wang et al [15], and Klein et al [16] represented concepts in multiple languages in a common vector space and ensured a concept in source language has a similar vector representation to its target-side counterpart. Xu et al [17] treated the cross-lingual knowledge projection as a graph-matching problem and proposed a graph-attention-based solution, which matches all the entities in two topic entity graphs and jointly models the local matching information to derive a graph-level matching vector.…”

Section: Related Workmentioning

confidence: 99%

Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection

et al. 2019

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Although considerable effort has been devoted to building commonsense knowledge bases (CKB), it is still not available for many low-resource languages such as Uyghur because of expensive construction cost. Focusing on this issue, we proposed a cross-lingual knowledge-projection method to construct an Uyghur CKB by projecting ConceptNet’s Chinese facts into Uyghur. We used a Chinese–Uyghur bilingual dictionary to get high-quality entity translation in facts and employed a back-translation method to eliminate the entity-translation ambiguity. Moreover, to tackle the inner relation ambiguity in translated facts, we made a hand-crafted rule to convert the structured facts into natural-language phrases and built the Chinese–Uyghur lingual phrases based on the similarity of phrases that corresponded to the bilingual semantic similarity scoring model. Experimental results show that the accuracy of our semantic similarity scoring model reached 94.75% for our task, and they successfully project 55,872 Chinese facts into Uyghur as well as obtain 67,375 Uyghur facts within a very short period.

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Improving Neural Knowledge Base Completion with Cross-Lingual Projections

Cited by 9 publications

References 28 publications

Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding

Cross-Lingual Entity Alignment via Joint Attribute-Preserving Embedding

Clustering and Bootstrapping Based Framework for News Knowledge Base Completion

Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection

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