 Introduction Creating KBs based on the crowd-sourced wikis has attracted significant research interest in the field of intelligent Web. However, the user-generated subsumption relations in the wikis and the semantic taxonomic relations in the KBs are not exactly the same. Current taxonomy derivation approaches include:  The heuristic-based methods  The corpus-based methods Here, we systematically study the problem of cross-lingual knowledge validation based taxonomy derivation from heterogeneous online wikis. The problem of cross-lingual taxonomic relation prediction is at the heart of our work.

Example of Mistaken Derived Facts  Approach Given two wikis 𝑊1 , 𝑊2 in different languages (English and Chinese here) and the set of cross- lingual links 𝐶𝐿, Cross-lingual Taxonomy Derivation is a cross-lingual knowledge validation based boosting process, by simultane- ously learning four taxonomic prediction 𝑒𝑛𝑧ℎ𝑒𝑛𝑧ℎfunctions 𝑓 , 𝑓 , 𝑔 and 𝑔 in 𝑇 iterations.

Framework where 𝑓 𝑒𝑛 , 𝑓 𝑧ℎ , 𝑔𝑒𝑛 and 𝑔 𝑧ℎ denote the English subClassOf, the Chinese subClassOf, the English instanceOf, and the Chinese instanceOf prediction functions respectively. Dynamic Adaptive Boosting (DAB) model is to maintain a dynamic changed training set to achieve a better generalization ability via knowledge validation with cross-lingual links. 1. Weak Classifier We utilize the binary classifier for the basic learner and use the Decision Tree as our implementation. Linguistic Heuristic Features Feature 1: English Features. Whether the head words of label are plural or singular. Feature 2: Chinese Features. Whether the super-category’s label is the prefix/suffix of the sub-category’s label. Or, whether the category’s label is the prefix/suffix of the article’s label. Feature 3: Common Features for instanceOf. Whether the comment contains the label or not. Structural Features Six Normalized Google Distance based structural features are defined on articles, properties and categories. 2. Boosting Model Active Set A: the set of training data. Pool P: the set of all labeled data. Unknown Data Set U: the set of unlabeled data.

Learning Process.  Train a hypothesis on current active set.  Re-weight the weight vector.  Predict U using current classifier and validate the results using CL.  Expand P and update U.  Resample A with the constant size.  Experiments Comparison Methods  Heuristic Linking (HL): only uses the linguistic heuristic features, and trains the taxonomic relation prediction functions separately using the decision tree model.  Decision Tree (DT): uses both the linguistic heuristic features and the structural features, and trains the taxonomic relation prediction functions separately using the decision tree model.  Adaptive Boosting (AdaBoost): uses the same basic learner, and iteratively trains the taxonomic relation prediction functions using the real AdaBoost model. Performance of Cross-lingual Taxonomy Derivation with Different Methods (%)

Boosting Contribution Comparison  Conclusion and Future Work DAB gives a new way for language processing tasks using cross-language resources. The future work contains automatically learning more cross-lingual validation rules and conducting more experiments in other languages.  References de Melo, G., and Weikum, G. 2010. Menta: Inducing multilingual taxonomies from Wikipedia. In CIKM’10. Potthast, M., Stein, B., and Anderka, M. 2008. A Wikipedia-based multilingual retrieval model. In ECIR’08. Wang, Z.; Li, J.; Wang, Z.; and Tang, J. 2012. Cross-lingual knowledge linking across wiki knowledge bases. In WWW’12.