Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence
Boosting Cross-Lingual Knowledge Linking via Concept Annotation
Zhichun Wang†, Juanzi Li‡, Jie Tang‡ †College of Information Science and Technology, Beijing Normal University ‡Department of Computer Science and Technology, Tsinghua University [email protected], [email protected], [email protected]
Abstract However, a large portion of articles are still lack of the CLs to certain languages in Wikipedia. For example, the largest Automatically discovering cross-lingual links version of Wikipedia, English Wikipedia, has over 4 million (CLs) between wikis can largely enrich the English articles in Wikipedia, but only 6% of them are linked cross-lingual knowledge and facilitate knowledge to their corresponding Chinese articles, and only 6% and 17% sharing across different languages. In most existing of them have CLs to Japanese articles and German articles, approaches for cross-lingual knowledge linking, respectively. Recognizing this problem, several cross-lingual the seed CLs and the inner link structures are two knowledge linking approaches have been proposed to find important factors for finding new CLs. When new CLs between wikis. Sorg et al. [Sorg and Cimiano, 2008] there are insufficient seed CLs and inner links, proposed a classification-based approach to infer new CLs be- discovering new CLs becomes a challenging prob- tween German Wikipedia and English Wikipedia. Both link- lem. In this paper, we propose an approach that based features and text-based features are defined for predict- boosts cross-lingual knowledge linking by concept ing new CLs. 5,000 new English-German CLs were reported annotation. Given a small number of seed CLs and in their work. Oh et al. [Oh et al., 2008] defined new link- inner links, our approach first enriches the inner based features and text-based features for discovering CLs links in wikis by using concept annotation method, between Japanese articles and English articles, 100,000 new and then predicts new CLs with a regression-based CLs were established by their approach. Wang et al. [Wang learning model. These two steps mutually reinforce et al., 2012] employed a factor graph model which leverages each other, and are executed iteratively to find as link-based features to find CLs between English Wikipedia many CLs as possible. Experimental results on and a large scale Chinese wiki, Baidu Baike1. Their approach the English and Chinese Wikipedia data show that was able to find about 200,000 CLs. the concept annotation can effectively improve the In most aforementioned approaches, language- quantity and quality of predicted CLs. With 50,000 independent features are mainly defined based on the seed CLs and 30% of the original inner links in link structures in wikis and the already known CLs. Two Wikipedia, our approach discovered 171,393 more most important features are the number of seed CLs between CLs in four runs when using concept annotation. wikis and the number of inner links in each wiki. Large num- ber of known CLs and inner links are required for accurately finding sufficient number of new CLs. However, the number 1 Introduction of seed CLs and inner links in wikis varies across different With the rapid evolving of the Web to be a world-wide knowledge linking tasks. Figure 1 shows the information global information space, sharing knowledge across differ- of wikis that are involved in previous knowledge linking ent languages becomes an important and challenging task. approaches, including the average numbers of outlinks and Wikipedia is a pioneer project that aims to provide knowl- the percentages of articles having CLs to English Wikipedia. edge encoded in various different languages. According to It shows that English Wikipedia has the best connectivity. the statistics in Jun 2012, there are more than 20 million ar- On average each English article has more than 18 outlinks ticles written in 285 languages in Wikipedia. Articles de- on average. The average numbers of outlinks in German scribing the same subjects in different languages are con- Wikipedia, Japanese Wikipedia and Chinese Wikipedia range nected by cross-lingual links (CLs) in Wikipedia. These CLs from 8 to 14. 35% to 50% of these articles have CLs to serve as a valuable resource for sharing knowledge across English Wikipedia in these three wikis. While we also note languages, which have been widely used in many applica- that in Baidu Baike, both the average number of outlinks and tions, including machine translation [Wentland et al., 2008], the percentage of articles linked to English Wikipedia are cross-lingual information retrieval [Potthast et al., 2008], and much smaller than that in Wikipedia. Therefore, discovering multilingual semantic data extraction [Bizer et al., 2009; de Melo and Weikum, 2010], etc. 1http://baike.baidu.com/
2733 all the possible missing CLs between wikis (such as Baidu Concept Annotator Baike) that have sparse connectivity and limited seed CLs CL Predictor is a challenging problem. As mentioned above, more than Concept Extraction 200,000 Chinese articles in Baidu Baike are linked to English Input Wikis Wikipedia, but they only account for a small portion of more Training than 4,000,000 Chinese articles in Baidu Baike. How to find Annotation large scale CLs between wikis based on small number of CLs Wiki 1 and inner links? To the best of our knowledge, this problem Prediction has not been studied yet. Wiki 2
%!# )!"# Cross-lingual $*# (!"# Links $)# $'# '!"# $%# Figure 2: Framework of the proposed approach. $!# &!"# *# %!"# )# forcement manner. The concept annotation method con- '# sumes seed CLs and inner links to produce new inner $!"# %# links. Then the seed CLs and the enriched inner links !# !"# are used in the CL prediction method to find new CLs. +,#-./.012.3# 4+#-./.012.3# 56#-./.012.3# 78#-./.012.3# 93.2:#93./1# The new CLs are again used to help concept annotation. 6;1<3=1#>:?@1<#AB#A:CD.>/# E1
2734 2.2 Concept Annotation Definition 2 Semantic Relatedness. Given a concept c, let Important concepts in a wiki article are usually annotated by Nc be the existing annotations in the same article with c, the editors with hyperlinks to their corresponding articles in the Semantic Relatedness between a candidate destination article same wiki database. These inner links guide readers to arti- a and the context annotations of c is computed as: cles that provide related information about the subject of cur- 1 X rent article. The linkage structure was often used to estimate SR(a, c) = r(a, b) (2) |Nc| similarity between articles in traditional knowledge linking b∈Nc approaches, e.g., [Wang et al., 2012]. However, there might be missing inner links in the wiki articles, especially when the log(max(|Ia|, |Ib|)) − log(|Ia ∩ Ib|) articles are newly created or have fewer editors. In this cir- r(a, b) = 1 − (3) cumstance, link-based features cannot accurately assess the log(|W |) − log(min(|Ia|, |Ib|)) structure-based similarity between articles. Therefore, our where Ia and Ib are the sets of inlinks of article a and article basic idea is to first uses a concept annotator to enrich the b, respectively; and W is the set of all articles in the input inner links in wikis before finding new CLs. wiki. Recently, several approaches have been proposed to anno- tating documents with concepts in Wikipedia [Mihalcea and The Semantic Relatedness metric has been used in several Csomai, 2007; Milne and Witten, 2008; Kulkarni et al., 2009; other approaches for linking documents to Wikipedia. The Shen et al., 2012]. These approaches aim to identify impor- inner link structures in the wiki are used to compute the Se- tant concepts in the given documents and link them to the mantic Relatedness. One thing worth mentioning is that when corresponding articles in Wikipedia. The concept annotator there are insufficient inner links, this metric might be not ac- in our approach provides the similar function as existing ap- curate enough to reflect the relatedness between articles. To proaches. However, instead of discovering links from an ex- this end, our approach first utilizes the known CLs to merge ternal document to a wiki, concept annotator focuses on en- the link structures of two wikis, which results in a Integrated riching the inner links in a wiki. Therefore, there will be Concept Network. The Semantic Relatedness is computed some existing links in the articles before annotating. In our based on the links in the Integrated Concept Network, which approach, the inputs of concept annotator are two wikis and is formally defined as follows. a set of CLs between them. The concept annotation process Definition 3 Integrated Concept Network. Given two wikis consists of two basic steps: (1) concept extraction, and (2) W1 = (A1,E1), W2 = (A2,E2), where A1 and A2 are sets annotation. of articles, E1 and E2 are sets of links in W1 and W2, respec- k Concept Extraction. In this step, possible concepts that tively. Let CL = {(ai, bi)|ai ∈ B1, bi ∈ B2}i=1 be the set of might refer to other articles in the same wiki are identified in cross-lingual links between W1 and W2, where B1 ⊆ A1 and an input article. The concepts are extracted by matching all B2 ⊆ A2. The Integrated Concept Network of W1 and W2 is the n-grams in the input article with the elements in a con- ICN(W1,W2) = (V,L), where V = A1 ∪ A2; L is a set of trolled vocabulary. The vocabulary contains the titles and the links which are established as follows: anchor texts of all the articles in the input wiki. For an in- 0 0 0 (v, v ) ∈ E1 ∨ (v, v ) ∈ E2 → (v, v ) ∈ L put article a, the results of concept extraction contain a set 0 0 0 0 k (v, v ) ∈ E1 ∧ (v, b) ∈ CL ∧ (v , b ) ∈ CL → (b, b ) ∈ L of concepts C = {ci}i=1 and the sets of candidate articles 0 0 0 0 (v, v ) ∈ E2 ∧ (v, a) ∈ CL ∧ (a , v ) ∈ CL → (a, a ) ∈ L {Tc1 ,Tc2 , ..., Tck } that each concept might link to. Annotation. In the annotation step, links from the con- In the Integrated Concept Network, the link structures of cepts to the destination articles are identified. Here we use two wikis are merged by using CLs. The Semantic Related- two metrics, the Link Probability and the Semantic Related- ness computed based on Integrated Concept Network is more ness, to decide the correct links for each concept. The Link reliable than on a single wiki. In order to balance the Link Probability measures the possibilities that an article is the Probability and the Semantic Relatedness, our approach uses destination of a given concept, which is approximated based an algorithm that greedily selects the articles that maximize on the already known annotations in a wiki. The Semantic the product of the two metrics. Algorithm 1 outlines the al- Relatedness measures relatedness between the candidate des- gorithm in the concept annotator. tination article and the surrounding context of the concept. Formally, these two metrics are defined as follows. 2.3 Cross-lingual Link Prediction Definition 1 Link Probability. Given a concept c identified As shown in Figure 2, the CL predictor takes two wikis and a in an article, the Link Probability from this concept c to an- set of seed CLs between them as inputs, then it predicts a set other article a is defined by the approximated conditional of new CLs. This subsection first introduces the definitions of probability of a given c: different similarity features, and then describes the learning method for predicting new CLs. count(a, c) LP (a|c) = (1) count(c) Feature Definition Six features are defined to assess the similarities between ar- where count(a, c) denotes the number of times that c links to ticles by using different kinds of information, all of these a and count(c) denotes the number of times that c appears in features are based on the link structures and therefore are the whole wiki. language-independent.
2735 Algorithm 1: Concept annotation algorithm. categories of two articles, the category similarity is computed as Input: A wiki W = (A, E) 0 Output: Annotated wiki W = (A, E ) 2 · |φ (C(a)) ∩ C(b)| f (a, b)(= 1→2 (8) 5 |φ (C(a))| + |C(b)| for each article a ∈ A do 1→2 Extract a set of concepts Ca in a; Here φ1→2(·) maps categories from one wiki to another Get the existing annotations Na in a; wiki by using the CLs between categories, which can be ob- for each ci ∈ Ca do tained from Wikipedia. + Find the set of candidate articles Tci of ci; Feature 6 Category similarity Get b∗ = arg max LP (b|c ) × SR(b, N ); b∈Tci i a Given two articles a and b, let C(a) and C(b) be the cat- ∗ + Na = Na ∪ {b }; egories of a and b respectively. Category similarity com- end putes similarities between categories by using CLs between for each bj ∈ Na do articles. Let E(c1) and E(c2) be the set of articles belonging if < a, bj >/∈ E then to category c1 and c2 respectively, the similarity between two E = E ∪ {< a, bj >} categories is computed as end n m end 1 X X f (a, b) = λ(ca, cb) (9) end 6 nm i j i=1 j=1 return Nd
2|φ1→2(E(c1)) ∩ E(c2)| λ(c1, c2) = (10) Feature 1: Outlink similarity |φ1→2(E(c1))| + |E(c2)| Outlinks of an article correspond to a set of other articles a b where ci ∈ C(a), cj ∈ C(b), n = |C(a)|, and m = |C(b)|. that it links to. The outlink similarity computes the similari- ties between articles by comparing elements in their outlinks. Regression-based Model for Predicting new CLs Given two articles a ∈ W1 and b ∈ W2, let O(a) and O(b) The CL predictor in our approach computes the weighted sum be the outlinks of a and b respectively. The outlink similarity of different similarities between articles, and applies a thresh- is computed as old ω0 to decide whether an article pair should have a CL. For this purpose, a scoring function is defined: 2 · |φ (O(a)) ∩ O(b)| f (a, b) = 1→2 (4) 1 |φ (O(a))| + |O(b)| 1→2 ~ s(a, b) = ω0 + ~ω · fa,b where φ1→2(·) is a function to maps articles in W1 to their (11) = ω + ω × f (a, b) + ... + ω × f (a, b) corresponding articles in W2 if there are CLs between them. 0 1 1 6 6 Feature 2: Outlink+ similarity For each article a in W , the article b∗ in W that maxi- Given two articles a ∈ W and b ∈ W , let O+(a) and 1 2 1 2 mizes the score function s(a, b∗) and satisfies s(a, b∗) > 0 O+(b) be the outlinks in the abstracts and infoboxes of a and is predicted as the corresponding article of a. The idea of b respectively. The outlink+ similarity is computed as CL prediction is simple and straightforward, but how to ap- + + propriately set the weights of different similarity features is a 2 · |φ1→2(O (a)) ∩ O (b)| f2(a, b) = (5) challenging problem. |φ (O+(a))| + |O+(b)| 1→2 Here, we propose a regression-based model to learn the Feature 3: Inlink similarity weights of features based on a set of known CLs. Given a set Inlinks of an article correspond to a set of other articles n n of CLs {(ai, bi)}i=1 as training data, let A = {(ai)}i=1 and linking to it, let I(a) and I(b) denote inlinks of two articles, n B = {(bi)}i=1, our model tries to find the optimal weights to the inlink similarity is computed as ensure:
2 · |φ1→2(I(a)) ∩ I(b)| f3(a, b) = (6) 0 0 |φ1→2(I(a))| + |I(b)| ∀ai ∈ A, ∀b ∈ (B − {bi}), s(ai, bi) − s(ai, b ) > 0 + Feature 4: Inlink similarity which also means Let I+(a) and I+(b) denote two articles’ inlinks that are from other articles’ abstracts and infoboxes, the inlink+ sim- ~ω · (f~ − f~ ) > 0 ilarity is computed as ai,bi ai,b‘ D = {(x , y )}n + + Therefore, we generate a new dataset i i i=1, 2 · |φ1→2(I (a)) ∩ I (b)| ~ ~ f (a, b) = (7) where the input vector xi = (fa ,b −fa ,b ), the target out- 4 |φ (I+(a))| + |I+(b)| i i i j6=i 1→2 put yi is always set to 1. Then we train a linear regression Feature 5 Category similarity model on the dataset D to get the weights of different fea- Categories are tags attached to articles, which represent the tures. The threshold ω0 is set to the value that maximizes the topics of the articles’ subjects. Let C(a) and C(b) denote F1-measure on the training CLs.
2736 Table 1: Results of cross-lingual link prediction (%). Before Annotation After Annotation #Seed CLs Model Precision Reccall F1-measure Precision Recall F1-measure 0.05 Mil. CLs SVM 92.1 35.0 50.7 78.5 37.2 50.5 RM 93.3 36.0 52.0 92.4 38.6 54.5 0.10 Mil. CLs SVM 79.7 35.0 48.6 86.9 50.4 63.8 RM 84.6 37.4 51.9 96.6 49.3 65.3 0.15 Mil. CLs SVM 80.9 35.9 49.7 88.1 57.3 69.5 RM 93.5 38.2 54.2 93.7 56.2 70.2 0.20 Mil. CLs SVM 84.7 37.3 51.8 88.8 68.1 77.1 RM 94.5 37.9 54.1 95.9 67.2 79.0
3 Experiments .::8) <:8) The data of English Wikipedia and Chinese Wikipedia 5:8) (archived in August 2012) has been used to evaluate our pro- 9:8) posed approach. The English Wikipedia contains 4 million ?:8) articles, and the Chinese Wikipedia contains 499 thousand ;:8) articles, and there are already 239,309 cross-lingual links @:8) between English Wikipedia and Chinese Wikipedia. Using >:8) these data, we first evaluate the effectiveness of concept an- 6:8) notation and CL prediction methods separately, and then eval- .:8) uate the final result obtained by the proposed approach. :8) !"#$%&%'() *#$+,,) -./0#+&1"#) 3.1 Concept Annotation 2')34&)) 567.8) 9:7;8) 9;7<8) :7.)=%,7)34&) 5>7?8) 9.7@8) 997:8) For the evaluation of the Concept Annotation method, we ran- :76)=%,7)34&) 5;7?8) 9>798) 9<768) domly selected 1000 articles from Chinese Wikipedia. There are 12,723 manually created inner links in these selected ar- ticles. 70% of the existing inner links in the selected articles Figure 3: Results of concept annotation. were randomly chosen as the ground truth for the evaluation, which were removed from the articles before the articles are were extracted from the rest of 236,309 existing CLs between fed to the annotation algorithm. After the annotation, we col- English Wikipedia and Chinese Wikipedia. In the experi- lected the new discovered inner links, and computed the Pre- ments, we aimed to investigate how the CL prediction method cision, Recall and F1-measure of the new discovered inner performed before and after the concept annotation, and how links based on the ground truth links. the CL prediction method performed with different numbers One advantage of our concept annotation method is to of seeding CLs. Therefore, we did three groups of exper- leverage the cross-lingual information to enhance the con- iments, with each group using different number of seeding cept annotation. Therefore, we did experiments with differ- cross-lingual links. In each group of experiments, we also ent number of CLs used in the concept annotation. Figure 3 compared the performance of our method with the SVM clas- shows the experimental results of concept annotation. In the sification model, which was used in previous knowledge link- experiments, we respectively used 0, 0.1million and 0.2 mil- ing approaches. Table 1 shows the experimental results. lion CLs, respectively. As shown in Figure 3, our approach In each group of the experiment, our regression-based can obtain a precision of 82.1% and a recall of 70.5% without learning model (RM) always performed better than the SVM using any CLs. The performance can be significantly boosted model in terms of F1-measure. It shows that our RM model when using some seed CLs in our approach. For example, is more suitable for the knowledge linking problems than the when 0.2 million CLs are used, the obtained precision is in- traditional classification model SVM. creased by 3.5% and the recall is increased by 3.2%. More- Before the concept annotation being performed, the perfor- over, a large part of missing inner links can be discovered by mance (e.g., by F1-measure) of our approach does not always our concept annotation method. The newly discovered CLs increase with more seed CLs being used. On the other hand, can be again used to improve the quality of the discovered after the concept annotation process, the F1-score increases inner links. clearly as the number of seed CLs increases. For example, there is a 24.5% increment of the F1-measure when the num- 3.2 Cross-lingual Links Prediction ber of seed CLs increases from 0.05 million to 0.20 million. To evaluate the CL prediction method, we randomly selected This might be because the equivalent relations of seed CLs 3,000 articles having CLs to English articles from Chinese cannot be propagated to other article pairs when there are few Wikipedia. The existing CLs between these articles were inner links in wikis. Therefore, enriching the inner links is used as the ground truth for the evaluation. The seed CLs important for knowledge linking and can effectively improve
2737 the performance of CL prediction. YAGO ontology to predict correct links from documents to Wikipedia. Kaulkarni et al. [Kulkarni et al., 2009] and Han 3.3 Incremental Knowledge Linking et al. [Han et al., 2011] proposed collective approaches that Finally, we evaluated our approach as a whole. Since the pro- aim to take the relations between annotation results into ac- posed approach aims to discover CLs with a small set of seed- count. Different from these approaches, our concept annota- ing links, we did not use all the available CLs as seed links. tion method uses the cross-lingual links to improve the anno- Instead, we randomly selected 50 thousand of them as the tation results. Only two measures, the link probability and the seed links. We also randomly removed 70% inner links in semantic relatedness, are used to decide the links from con- the Chinese Wikipedia (for both articles having cross-lingual cepts to their corresponding wiki articles, which guarantees links and having no cross-lingual links). We fed the seed links the efficiency of annotation process. and two wikis to our approach and run the proposed approach Cross-lingual Knowledge Linking. There have been sev- iteratively to incrementally find new CLs. Figure 4 shows eral approaches for cross-lingual knowledge linking. Sorg the number of newly discovered CLs in each run. The gray and Cimiano [Sorg and Cimiano, 2008] proposed a method bars plot the results without concept annotation and the black to find missing CLs between English and German. Their bars indicate the results when the concept annotation was per- method uses the link structure of articles to find candidates of formed. missing links. A classifier is trained based on several graph- According to the results, new CLs can be discovered in based features and text-based features to predict CLs. Oh et each run no matter whether the concept annotation was per- al. [Oh et al., 2008] proposed a method for discovering miss- formed or not. However, there would be more CLs discov- ing cross-lingual links between English and Japanese. Wang ered when the concept annotation was first performed. In the et al. [Wang et al., 2012] employed a factor graph model fourth run, more CLs were discovered than in the third run which only used link-based features to find CLs between a when using concept annotation, but less CLs were found in Chinese wiki and English Wikipedia. In our approach, inner the fourth run than in the third run without concept annota- links appearing in different parts of articles are used sepa- tion. In summary, 171,393 more CLs were discovered when rately in different similarity features, which is different from the concept annotation were performed. the existing approaches. And our approach uses a regression- based learning model to learn weights to aggregate similari- ties. From a broad viewpoint, ontology matching is also rel- !03-333" evant. Approaches such as RiMOM [Li et al., 2009; Tang !%3-333" et al., 2006] have been proposed. Cross-lingual ontology !#3-333" matching is a more relevant problem to cross-lingual knowl- edge linking. However, existing approaches, e.g., [Spohr et !33-333" al., 2011; Fu et al., 2011], mainly use the machine translation .3-333" tools to bridge the gap between languages, which makes the
03-333" approaches heavily dependent on the language itself. In our approach, all the defined features are language-independent. %3-333" !"#$%&'()'*+,-(.%&%*'/0, #3-333" 5 Conclusions and Future Work 3" !" #" $" %" In this paper, we propose an approach that boosts cross- &'"())'*+,')" $%-./0" 1/-!#." /0-2$#" 0/-%%3" lingual knowledge linking by concept annotation. New inner ())'*+,')" %1-33#" /.-$$%" !$#-#!3" !1#-##$" links are first found in wikis by a concept annotation method, which are then used for predicting new CLs. The concept annotation and the CL prediction are designed to mutually Figure 4: Results of incremental knowledge linking. reinforce each other, which allows new CLs to be incremen- tally discovered. Experiments show that the concept annota- tion can effectively improve the results of CL prediction. The 4 Related Work proposed approach can also effectively find new CLs based on a small set of seed CLs and inner links. Concept Annotation. Recently, several approaches have The knowledge linking is a core problem in knowledge been proposed to link documents to Wikipedia. Wikify! [Mi- base and represents a new research direction. There are a halcea and Csomai, 2007] is a system which automatically number of potential future directions of this work, for exam- performs the annotation task following the Wikipedia guide- ple, to design an online algorithm for knowledge linking, or lines. Wikify! first extracts keywords in documents, then to design a scalable algorithm to handle large knowledge base find the links from these keywords to their corresponding arti- with billions of entities. cles by combining both knowledge-based approach and data- driven method. Milne et al. [Milne and Witten, 2008] pro- Acknowledgements. This work is supported by NSFC posed a learning based approach for linking entities in text to (No. 61202246, 61035004, 61222212, 61073073), NSFC- Wikipedia. A classification algorithm is also used in the can- ANR (No. 61261130588), 863 High Technology Program didate link detection. Shen et al. [Shen et al., 2012] proposed (2011AA01A207), FP7-288342, and THU-NUS NExT Co- a system LINDEN, which explores semantic information in Lab.
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