# Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection
## 1 Introduction  
Knowledge Bases (KBs) play an important role in many natural-language processing (NLP) tasks such as question answering, web searching and dialog tasks [1; 2]. KBs describe the knowledge about entities, relations, and their attributes. In a KB, each fact is a triple of the form (\(h\), \(r\), \(t\)) that indicates the head entity \(h\) and tail entity \(t\) are connected with a relationship named \(r\), e.g., (_Bat_, _CapableOf_, _Fly_). As a part of KB, commonsense knowledge (CSK) is mainly referred to as background knowledge and used in natural-language processing tasks that require reasoning based on implicit knowledge [3; 4; 5; 6]. However, many languages, especially low-resource languages including Uyghur, have no existing commonsense knowledge base (CKB) to use [7; 8; 9]. Constructing CKBs from scratch is very time-consuming and labor-intensive. Instead, there are many available CKB resources in other rich-resource languages such as English and Chinese. A straightforward way to construct an Uyghur CKB is to directly translate Chinese KB to Uyghur based on the surface texts of a fact with the existing machine translation (MT) system or bilingual dictionary. However, we find that this method suffers from the problem of ambiguity. For example, consider translating Chinese fact (\(\mathbb{H}\)\(\downarrow\)\(\
eg\)host computer\(>\), _CapableOf_, \(\mathbb{H}\)\(\
eg\)cheat\(>\)) shown in Figure 1. The head entity \"\(\mathbb{H}\)\(\vdash\)\" (host computer) has six Uyghur translation candidates, including \(\mathbb{H}\)\(\vdash\)\(\There are two main challenges to effectively disambiguate these translation triples. The first challenge is how to remove the semantic unrelated translation candidates for a single entity. The second challenge is how to effectively model the semantics of the Chinese fact and Uyghur fact in common semantic space. In this paper, we address these two challenges by presenting a cross-lingual knowledge-projection method to translate the Chinese CKB into Uyghur. Given a Chinese fact, first, the method uses a Chinese-Uyghur bilingual dictionary to get entity translations in fact and use back-translation to remove the semantically unrelated translation candidates. Then, the method converts each Chinese and Uyghur fact to a parallel sentence using a hand-crafted rule template and achieves their sentence representation by a recursive autoencoder. Finally, the method encodes the source and target sentence in the same semantic space using the bidimensional attention network, and calculates the distance between them to get the semantic similarity score. Being the largest multilingual CKB, ConceptNet [10] connects words and phrases of natural language with labeled edges and maintains knowledge as a triple of two concepts and relations between them. The relations come from a fixed set. The latest release (v5.6.0) of ConceptNet has 369,687 unique Chinese facts (both head and tail nodes are Chinese) while the number of Uyghur facts is only 3872 (\(\approx\)1.05%). We focus on ConceptNet in this paper. We project ConceptNet's Chinese facts to Uyghur by using a Chinese-Uyghur bilingual dictionary and a bilingual semantic similarity scoring model, automatically building an Uyghur CKB from existing Chinese CKBs, taking the advantages of the cross-lingual knowledge projection. Suppose we project a Chinese fact \(f^{s}\) into a target-side Uyghur fact, and obtain \(n\) candidate translations by using dictionary translation. We denote these candidates as \(f_{1}^{t},f_{2}^{t},\ldots,f_{n}^{t}\). Our goal is to estimate a projection score \(h(f_{i}^{t}|f^{s})\) and find the most appropriate Uyghur fact that maximizes the score, which can be formulated in Equation (1). \[\hat{f}=\underset{f_{i}^{t}}{argmaxh(f_{i}^{t}|f^{s})} \tag{1}\]  
The structure of this paper is organized as follows: in Section 2 we discuss the related works; in Section 3 we introduce our cross-lingual knowledge-projection method; in Section 4 we present the experiments and analysis of the results.