1,719 research outputs found
Cross-lingual knowledge linking across wiki knowledge bases
Wikipedia becomes one of the largest knowledge bases on the Web. It has attracted 513 million page views per day in January 2012. However, one critical issue for Wikipedia is that articles in different language are very unbalanced. For example, the number of articles on Wikipedia in English has reached 3.8 million, while the number of Chinese articles is still less than half million and there are only 217 thousand cross-lingual links between articles of the two languages. On the other hand, there are more than 3.9 million Chinese Wi-ki articles on Baidu Baike and Hudong.com, two popular encyclopedias in Chinese. One important question is how to link the knowledge entries distributed in different knowledge bases. This will immensely enrich the information in the on-line knowledge bases and benefit many applications. In this paper, we study the problem of cross-lingual knowledge link-ing and present a linkage factor graph model. Features are defined according to some interesting observations. Exper-iments on the Wikipedia data set show that our approach can achieve a high precision of 85.8 % with a recall of 88.1%. The approach found 202,141 new cross-lingual links between English Wikipedia and Baidu Baike
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
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
MAG: A Multilingual, Knowledge-base Agnostic and Deterministic Entity Linking Approach
Entity linking has recently been the subject of a significant body of
research. Currently, the best performing approaches rely on trained
mono-lingual models. Porting these approaches to other languages is
consequently a difficult endeavor as it requires corresponding training data
and retraining of the models. We address this drawback by presenting a novel
multilingual, knowledge-based agnostic and deterministic approach to entity
linking, dubbed MAG. MAG is based on a combination of context-based retrieval
on structured knowledge bases and graph algorithms. We evaluate MAG on 23 data
sets and in 7 languages. Our results show that the best approach trained on
English datasets (PBOH) achieves a micro F-measure that is up to 4 times worse
on datasets in other languages. MAG, on the other hand, achieves
state-of-the-art performance on English datasets and reaches a micro F-measure
that is up to 0.6 higher than that of PBOH on non-English languages.Comment: Accepted in K-CAP 2017: Knowledge Capture Conferenc
Joint Representation Learning of Cross-lingual Words and Entities via Attentive Distant Supervision
Joint representation learning of words and entities benefits many NLP tasks,
but has not been well explored in cross-lingual settings. In this paper, we
propose a novel method for joint representation learning of cross-lingual words
and entities. It captures mutually complementary knowledge, and enables
cross-lingual inferences among knowledge bases and texts. Our method does not
require parallel corpora, and automatically generates comparable data via
distant supervision using multi-lingual knowledge bases. We utilize two types
of regularizers to align cross-lingual words and entities, and design knowledge
attention and cross-lingual attention to further reduce noises. We conducted a
series of experiments on three tasks: word translation, entity relatedness, and
cross-lingual entity linking. The results, both qualitatively and
quantitatively, demonstrate the significance of our method.Comment: 11 pages, EMNLP201
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