Automatic Wikipedia Link Generation Based On Interlanguage Links

Lecture Notes in Computer Science, 2012

In this paper we examine automated Chinese to English link discovery in Wikipedia and the effects of Chinese segmentation and Chinese to English translation on the hyperlink recommendation. Our experimental results show that the implemented link discovery framework can effectively recommend Chinese-to-English cross-lingual links. The techniques described here can assist bilingual users where a particular topic is not covered in Chinese, is not equally covered in both languages, or is biased in one language; as well as for language learning.

Held in Singapore, 24 July 2008., 2008

Proc. of AMTA …, 2010

This position paper outlines our project–WikiBABEL–which will be released as an open source project for the creation of multilingual Wikipedia content, and has potential to produce parallel data as a by-product for Machine Translation systems research. We ...

2016

Given the enormous amount of unstructured texts available on the Web, there has been an emerging need to increase discoverability of and accessibility to these texts. One of the proposed solutions is to annotate texts with information extracted from background knowledge. Wikipedia, the free encyclopedia, has been recently exploited as a background knowledge to dynamically annotate text with complementary information. Given any piece of text the main challenge is how to determine the most relevant information from Wikipedia with the least effort and time. While Wikipedia-based annotation has mainly targeted the English and Latin versions of Wikipedia, little effort has been devoted to annotate Arabic text using the Arabic version of Wikipedia. This work proposes an approach for dynamic linking of Arabic short texts to Wikipedia articles. It reports on the several challenges associated with the design and implementation of the linking approach including the processing and setting up of the Wikipedia's enormous content, the mapping of texts to Wikipedia articles, the problem of article disambiguation, and time efficiency. The proposed approach focuses on short texts because they are generally more difficult to process and annotate than long texts. The proposed approach was assessed over a dataset of 100 short texts gathered from online Arabic articles and then work on it offline. Hyperlinks generated by the approach were compared with the hyperlinks generated by two human raters. The dynamic linking approach achieved 71.79% accuracy, 74.70% average precision, and 82.63 % average recall. A thorough analysis and discussion of the evaluation results are also presented to address the limitations and strengths as well as the recommendations for future improvements. The source code, dataset, and complete experimental results are made available online on:

This paper describes the methods used in the submission of Knowledge Media institute (KMI), The Open University to the NTCIR-9 Cross-Lingual Link Discovery (CLLD) task entitled CrossLink. KMI submitted four runs for link discovery from English to Chinese; however, the developed methods, which utilise Explicit Semantic Analysis (ESA), are applicable also to other language combinations. Three of the runs are based on exploiting the existing cross-lingual mapping between different versions of Wikipedia articles. In the fourth run, we assume information about the mapping is not available. Our methods achieved encouraging results and we describe in detail how their performance can be further improved. Finally, we discuss two important issues in link discovery: the evaluation methodology and the applicability of the developed methods across different textual collections.

IEEE Transactions on Knowledge and Data Engineering, 2009

Collaborative online encyclopedias or knowledge bases such as Wikipedia and PlanetMath are becoming increasingly popular. We demonstrate NNexus, a generalization of the automatic linking engine of PlanetMath.org and the first system that automates the process of linking disparate "encyclopedia" entries into a fully-connected conceptual network. The main challenges of this problem space include: 1) linking quality (correctly identifying which terms to link and which entry to link to with minimal effort on the part of users), 2) efficiency and scalability, and 3) generalization to multiple knowledge bases and web-based information environment. We present NNexus that utilizes subject classification and other metadata to address these challenges and demonstrate its effectiveness and efficiency through multiple real world corpora.

2011

yukawa@vos.nagaokaut.ac.jp Abstract This paper describes our method for the Cross-Lingual Link Discovery (CLLD). We used English-Japanese document collections in CLLD subtask of NTCIR-9. The topics in our method are translated by Wikipedia. Wikipedia is written by multi-language. In our method, the page written by the target language is retrieved for each topic written in the source language. The topic written in the target language is made from Wikipedia concept part of this page. Cross-language link is retrieved by a TF-IDF model.We use nouns, nouns phrase and adjective to make concept base. Re-ranked result retrieved by TF-IDF model. TFIDF and concept base are made from the outline part of Wikipidia pages, which are written in the target language extracted in Wikipidia pages collection. Crosslink Evaluation Tool of NTCIR9 Crosslink Task is utilized for performance evaluation.

TKE 2012

Abstract. In this article we present Termontospider, a wiki crawler that optimally traverses Wikipedia in search of domain-specific texts for extracting terminological and ontological information. The crawler is part of a tool suite for automatically developing multilingual termontological databases, ie ontologically-underpinned multilingual terminological databases. The focus is on analyzing the best value for internal links, categories and other metadata to assign weights and search mechanisms in network traversal. Keywords: data ...