A Virtual Evaluation Track for Cross Language Link Discovery

The Wikipedia has become the most popular online source of encyclopedic information. The English Wikipedia collection, as well as some other languages collections, is extensively linked. However, as a multilingual collection the Wikipedia is only very weakly linked. There are few cross-language links or cross-dialect links (see, for example, Chinese dialects). In order to link the multilingual-Wikipedia as a single collection, automated cross language link discovery systems are needed – systems that identify anchor-texts in one language and targets in another. The evaluation of Link Discovery approaches within the English version of the Wikipedia has been examined in the INEX Link the-Wiki track since 2007, whilst both CLEF and NTCIR emphasized the investigation and the evaluation of cross-language information retrieval. In this position paper we propose a new virtual evaluation track: Cross Language Link Discovery (CLLD). The track will initially examine cross language linking of Wikipedia articles. This virtual track will not be tied to any one forum; instead we hope it can be connected to each of (at least): CLEF, NTCIR, and INEX as it will cover ground currently studied by each. The aim is to establish a virtual evaluation environment supporting continuous assessment and evaluation, and a forum for the exchange of research ideas. It will be free from the difficulties of scheduling and synchronizing groups of collaborating researchers and alleviate the necessity to travel across the globe in order to share knowledge. We aim to electronically publish peer-reviewed publications arising from CLLD in a similar fashion: online, with open access, and without fixed submission deadlines.

Evaluation framework for focused link discovery

Information has no value unless it is accessible. Information must be connected together so a knowledge network can then be built. Such a knowledge base is a key resource for Internet users to interlink information from documents. Information retrieval, a key technology for knowledge management, guarantees access to large corpora of unstructured text. Collaborative knowledge management systems such as Wikipedia are becoming more popular than ever; however, their link creation function is not optimized for discovering possible links in the collection and the quality of automatically generated links has never been quantified. This research begins with an evaluation forum which is intended to cope with the experiments of focused link discovery in a collaborative way as well as with the investigation of the link discovery application. The research focus was on the evaluation strategy: the evaluation framework proposal, including rules, formats, pooling, validation, assessment and evaluation has proved to be efficient, reusable for further extension and efficient for conducting evaluation. The collection-split approach is used to re-construct the Wikipedia collection into a split collection comprising single passage files. This split collection is proved to be feasible for improving relevant passages discovery and is devoted to being a corpus for focused link discovery. Following these experiments, a mobile client-side prototype built on iPhone is developed to resolve the mobile Search issue by using focused link discovery technology. According to the interview survey, the proposed mobile interactive UI does improve the experience of mobile information seeking. Based on this evaluation framework, a novel cross-language link discovery proposal using multiple text collections is developed. A dynamic evaluation approach is proposed to enhance both the collaborative effort and the interacting experience between submission and evaluation. A realistic evaluation scheme has been implemented at NTCIR for cross-language link discovery tasks.

Mobile applications of focused link discovery

Interaction with a mobile device remains difficult due to inherent physical limitations. This dif-ficulty is particularly evident for search, which re-quires typing. We extend the One-Search-Only search paradigm by adding a novel link-browsing scheme built on top of automatic link discovery. A prototype was built for iPhone and tested with 12 subjects. A post-use interview survey suggests that the extended paradigm improves the mobile information seeking experience.

Automated cross-lingual link discovery in Wikipedia

At NTCIR-9, we participated in the cross-lingual link discovery (Crosslink) task. In this paper we describe our approaches to discovering Chinese, Japanese, and Korean (CJK) cross-lingual links for English documents in Wikipedia. Our experimental results show that a link mining approach that mines the existing link structure for anchor probabilities and relies on the “translation” using cross-lingual document name triangulation performs very well. The evaluation shows encouraging results for our system.

Overview of the NTCIR-9 crosslink task : cross-lingual link discovery

This paper presents an overview of NTCIR-9 Cross-lingual Link Discovery (Crosslink) task. The overview includes: the motivation of cross-lingual link discovery; the Crosslink task definition; the run submission specification; the assessment and evaluation framework; the evaluation metrics; and the evaluation results of submitted runs. Cross-lingual link discovery (CLLD) is a way of automatically finding potential links between documents in different languages. The goal of this task is to create a reusable resource for evaluating automated CLLD approaches. The results of this research can be used in building and refining systems for automated link discovery. The task is focused on linking between English source documents and Chinese, Korean, and Japanese target documents.

The effectiveness of cross-lingual link discovery

This paper describes the evaluation in benchmarking the effectiveness of cross-lingual link discovery (CLLD). Cross lingual link discovery is a way of automatically finding prospective links between documents in different languages, which is particularly helpful for knowledge discovery of different language domains. A CLLD evaluation framework is proposed for system performance benchmarking. The framework includes standard document collections, evaluation metrics, and link assessment and evaluation tools. The evaluation methods described in this paper have been utilised to quantify the system performance at NTCIR-9 Crosslink task. It is shown that using the manual assessment for generating gold standard can deliver a more reliable evaluation result.

Link discovery for Chinese/English cross-language web information retrieval

Nowadays people heavily rely on the Internet for information and knowledge. Wikipedia is an online multilingual encyclopaedia that contains a very large number of detailed articles covering most written languages. It is often considered to be a treasury of human knowledge. It includes extensive hypertext links between documents of the same language for easy navigation. However, the pages in different languages are rarely cross-linked except for direct equivalent pages on the same subject in different languages. This could pose serious difficulties to users seeking information or knowledge from different lingual sources, or where there is no equivalent page in one language or another. In this thesis, a new information retrieval task—cross-lingual link discovery (CLLD) is proposed to tackle the problem of the lack of cross-lingual anchored links in a knowledge base such as Wikipedia. In contrast to traditional information retrieval tasks, cross language link discovery algorithms actively recommend a set of meaningful anchors in a source document and establish links to documents in an alternative language. In other words, cross-lingual link discovery is a way of automatically finding hypertext links between documents in different languages, which is particularly helpful for knowledge discovery in different language domains. This study is specifically focused on Chinese / English link discovery (C/ELD). Chinese / English link discovery is a special case of cross-lingual link discovery task. It involves tasks including natural language processing (NLP), cross-lingual information retrieval (CLIR) and cross-lingual link discovery. To justify the effectiveness of CLLD, a standard evaluation framework is also proposed. The evaluation framework includes topics, document collections, a gold standard dataset, evaluation metrics, and toolkits for run pooling, link assessment and system evaluation. With the evaluation framework, performance of CLLD approaches and systems can be quantified. This thesis contributes to the research on natural language processing and cross-lingual information retrieval in CLLD: 1) a new simple, but effective Chinese segmentation method, n-gram mutual information, is presented for determining the boundaries of Chinese text; 2) a voting mechanism of name entity translation is demonstrated for achieving a high precision of English / Chinese machine translation; 3) a link mining approach that mines the existing link structure for anchor probabilities achieves encouraging results in suggesting cross-lingual Chinese / English links in Wikipedia. This approach was examined in the experiments for better, automatic generation of cross-lingual links that were carried out as part of the study. The overall major contribution of this thesis is the provision of a standard evaluation framework for cross-lingual link discovery research. It is important in CLLD evaluation to have this framework which helps in benchmarking the performance of various CLLD systems and in identifying good CLLD realisation approaches. The evaluation methods and the evaluation framework described in this thesis have been utilised to quantify the system performance in the NTCIR-9 Crosslink task which is the first information retrieval track of this kind.

An evaluation framework for cross-lingual link discovery

Cross-Lingual Link Discovery (CLLD) is a new problem in Information Retrieval. The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages. This is particularly helpful in knowledge discovery if a multi-lingual knowledge base is sparse in one language or another, or the topical coverage in each language is different; such is the case with Wikipedia. Techniques for identifying new and topically relevant cross-lingual links are a current topic of interest at NTCIR where the CrossLink task has been running since the 2011 NTCIR-9. This paper presents the evaluation framework for benchmarking algorithms for cross-lingual link discovery evaluated in the context of NTCIR-9. This framework includes topics, document collections, assessments, metrics, and a toolkit for pooling, assessment, and evaluation. The assessments are further divided into two separate sets: manual assessments performed by human assessors; and automatic assessments based on links extracted from Wikipedia itself. Using this framework we show that manual assessment is more robust than automatic assessment in the context of cross-lingual link discovery.

Overview of the NTCIR-10 cross-lingual link discovery task

This paper presents an overview of NTCIR-10 Cross-lingual Link Discovery (CrossLink-2) task. For the task, we continued using the evaluation framework developed for the NTCIR-9 CrossLink-1 task. Overall, recommended links were evaluated at two levels (file-to-file and anchor-to-file); and system performance was evaluated with metrics: LMAP, R-Prec and P@N.

Analisi e progettazione di un sistema di link discovery semi-automatico nell'ambito dei Linked Open Data

Lo scopo di questo elaborato è di analizzare e progettare un sistema in grado di supportare la definizione dei dati nel formato utilizzato per definire in modo formale la semantica dei dati, ma soprattutto nella complessa e innovativa attività di link discovery. Una attività molto potente che, tramite gli strumenti e le regole del Web Semantico (chiamato anche Web of Data), permette data una base di conoscenza sorgente ed altre basi di conoscenza esterne e distribuite nel Web, di interconnettere i dati della base di conoscenza sorgente a quelli esterni sulla base di complessi algoritmi di interlinking. Questi algoritmi fanno si che i concetti espressi sulla base di dati sorgente ed esterne vengano interconnessi esprimendo la semantica del collegamento ed in base a dei complessi criteri di confronto definiti nel suddetto algoritmo. Tramite questa attività si è in grado quindi di aumentare notevolmente la conoscenza della base di conoscenza sorgente, se poi tutte le basi di conoscenza presenti nel Web of Data seguissero questo procedimento, la conoscenza definita aumenterebbe fino a livelli che sono limitati solo dalla immensa vastità del Web, dando una potenza di elaborazione dei dati senza eguali. Per mezzo di questo sistema si ha l’ambizioso obiettivo di fornire uno strumento che permetta di aumentare sensibilmente la presenza dei Linked Open Data principalmente sul territorio nazionale ma anche su quello internazionale, a supporto di enti pubblici e privati che tramite questo sistema hanno la possibilità di aprire nuovi scenari di business e di utilizzo dei dati, dando una potenza al dato che attualmente è solo immaginabile.

Cross‐lingual link discovery in the Web of Data

Cross‐lingual link discovery in the Web of Data

Overview of the INEX 2009 Link the Wiki Track

In the third year of the Link the Wiki track, the focus has been shifted to anchor-to-bep link discovery. The participants were encouraged to utilize different technologies to resolve the issue of focused link discovery. Apart from the 2009 Wikipedia collection, the Te Ara collection was introduced for the first time in INEX. For the link the wiki tasks, 5000 file-to-file topics were randomly selected and 33 anchor-to-bep topics were nominated by the participants. The Te Ara collection does not contain hyperlinks and the task was to cross link the entire collection. A GUI tool for self-verification of the linking results was distributed. This helps participants verify the location of the anchor and bep. The assessment tool and the evaluation tool were revised to improve efficiency. Submission runs were evaluated against Wikipedia ground-truth and manual result set respectively. Focus-based evaluation was undertaken using a new metric. Evaluation results are presented and link discovery approaches are described