Jangwhan Han

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Jangwhan Han is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Heterogeneous graph-based intent learning with queries, web pages and Wikipedia concepts Heterogeneous graph clustering
Search intent
WSDM 2014 - Proceedings of the 7th ACM International Conference on Web Search and Data Mining English 2014 The problem of learning user search intents has attracted intensive attention from both industry and academia. However, state-of-the-art intent learning algorithms suffer from different drawbacks when only using a single type of data source. For example, query text has difficulty in distinguishing ambiguous queries; search log is bias to the order of search results and users' noisy click behaviors. In this work, we for the first time leverage three types of objects, namely queries, web pages and Wikipedia concepts collaboratively for learning generic search intents and construct a heterogeneous graph to represent multiple types of relationships between them. A novel unsupervised method called heterogeneous graph-based soft-clustering is developed to derive an intent indicator for each object based on the constructed heterogeneous graph. With the proposed co-clustering method, one can enhance the quality of intent understanding by taking advantage of different types of data, which complement each other, and make the implicit intents easier to interpret with explicit knowledge from Wikipedia concepts. Experiments on two real-world datasets demonstrate the power of the proposed method where it achieves a 9.25% improvement in terms of NDCG on search ranking task and a 4.67% enhancement in terms of Rand index on object co-clustering task compared to the best state-of-the-art method. 0 0
Ranking Wikipedia article's data quality by learning dimension distributions Data quality
Ensemble learning
Multivariate Gaussian distribution
Quality dimensions
International Journal of Information Quality English 2014 As the largest free user-generated knowledge repository, data quality of Wikipedia has attracted great attention these years. Automatic assessment of Wikipedia article's data quality is a pressing concern. We observe that every Wikipedia quality class exhibits its specific characteristic along different first-class quality dimensions including accuracy, completeness, consistency and minimality. We propose to extract quality dimension values from article's content and editing history using dynamic Bayesian network (DBN) and information extraction techniques. Next, we employ multivariate Gaussian distributions to model quality dimension distributions for each quality class, and combine multiple trained classifiers to predict an article's quality class, which can distinguish different quality classes effectively and robustly. Experiments demonstrate that our approach generates a good performance. Copyright 0 0
An exploration of discussion threads in social news sites: A case study of the Reddit community Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 English 2013 Social news and content aggregation Web sites have become massive repositories of valuable knowledge on a diverse range of topics. Millions of Web-users are able to leverage these platforms to submit, view and discuss nearly anything. The users themselves exclusively curate the content with an intricate system of submissions, voting and discussion. Furthermore, the data on social news Web sites is extremely well organized by its user-base, which opens the door for opportunities to leverage this data for other purposes just like Wikipedia data has been used for many other purposes. In this paper we study a popular social news Web site called Reddit. Our investigation looks at the dynamics of its discussion threads, and asks two main questions: (1) to what extent do discussion threads resemble a topical hierarchy? and (2) Can discussion threads be used to enhance Web search? We show interesting results for these questions on a very large snapshot several sub-communities of the Reddit Web site. Finally, we discuss the implications of these results and suggest ways by which social news Web site's can be used to perform other tasks. Copyright 2013 ACM. 0 0
On detecting Association-Based Clique Outliers in heterogeneous information networks Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 English 2013 In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. People like to discover groups (or cliques) of entities linked to each other with rare and surprising associations from such networks. We define such anomalous cliques as Association-Based Clique Outliers (ABCOutliers) for heterogeneous information networks, and design effective approaches to detect them. The need to find such outlier cliques from networks can be formulated as a conjunctive select query consisting of a set of (type, predicate) pairs. Answering such conjunctive queries efficiently involves two main challenges: (1) computing all matching cliques which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the cliques. In this paper, we address these two challenges as follows. First, we introduce a new low-cost graph index to assist clique matching. Second, we define the outlierness of an association between two entities based on their attribute values and provide a methodology to efficiently compute such outliers given a conjunctive select query. Experimental results on several synthetic datasets and the Wikipedia dataset containing thousands of entities show the effectiveness of the proposed approach in computing interesting ABCOutliers. Copyright 2013 ACM. 0 0
Probabilistically ranking web article quality based on evolution patterns Lecture Notes in Computer Science English 2012 User-generated content (UGC) is created, updated, and maintained by various web users, and its data quality is a major concern to all users. We observe that each Wikipedia page usually goes through a series of revision stages, gradually approaching a relatively steady quality state and that articles of different quality classes exhibit specific evolution patterns. We propose to assess the quality of a number of web articles using Learning Evolution Patterns (LEP). First, each article's revision history is mapped into a state sequence using the Hidden Markov Model (HMM). Second, evolution patterns are mined for each quality class, and each quality class is characterized by a set of quality corpora. Finally, an article's quality is determined probabilistically by comparing the article with the quality corpora. Our experimental results demonstrate that the LEP approach can capture a web article's quality precisely. 0 0
Probabilistic quality assessment based on article's revision history Lecture Notes in Computer Science English 2011 The collaborative efforts of users in social media services such as Wikipedia have led to an explosion in user-generated content and how to automatically tag the quality of the content is an eminent concern now. Actually each article is usually undergoing a series of revision phases and the articles of different quality classes exhibit specific revision cycle patterns. We propose to Assess Quality based on Revision History (AQRH) for a specific domain as follows. First, we borrow Hidden Markov Model (HMM) to turn each article's revision history into a revision state sequence. Then, for each quality class its revision cycle patterns are extracted and are clustered into quality corpora. Finally, article's quality is thereby gauged by comparing the article's state sequence with the patterns of pre-classified documents in probabilistic sense. We conduct experiments on a set of Wikipedia articles and the results demonstrate that our method can accurately and objectively capture web article's quality. 0 0
Probabilistic quality assessment of articles based on learning editing patterns Data quality
Quality assessment
Web article
2011 International Conference on Computer Science and Service System, CSSS 2011 - Proceedings English 2011 As a new model of distributed, collaborative information source, such as Wikipedia, is emerging, its content is constantly being generated, updated and maintained by various users and its data quality varies from time to time. Thus the quality assessment of the content is a pressing concern now. We observe that each article usually goes through a series of editing phases such as building structure, contributing text, discussing text, etc., gradually getting into the final quality state and that the articles of different quality classes exhibit specific edit cycle patterns. We propose a new approach to Assess Quality based on article's Editing History (AQEH) for a specific domain as follows. First, each article's editing history is transformed into a state sequence borrowing HiddenMarkov Model(HMM). Second, edit cycle patterns are first extracted for each quality class and then each quality class is further refined into quality corpora by clustering. Now, each quality class is clearly represented by a series of quality corpora and each quality corpus is described by a group of frequently co-occurring edit cycle patterns. Finally, article quality can be determined in probabilistic sense by comparing the article with the quality corpora. Experimental results demonstrate that our method can capture and predict web article's quality accurately and objectively. 0 0
Web article quality assessment in multi-dimensional space Lecture Notes in Computer Science English 2011 Nowadays user-generated content (UGC) such as Wikipedia, is emerging on the web at an explosive rate, but its data quality varies dramatically. How to effectively rate the article's quality is the focus of research and industry communities. Considering that each quality class demonstrates its specific characteristics on different quality dimensions, we propose to learn the web quality corpus by taking different quality dimensions into consideration. Each article is regarded as an aggregation of sections and each section's quality is modelled using Dynamic Bayesian Network(DBN) with reference to accuracy, completeness and consistency. Each quality class is represented by three dimension corpora, namely accuracy corpus, completeness corpus and consistency corpus. Finally we propose two schemes to compute quality ranking. Experiments show our approach performs well. 0 0
Using Wikipedia technology for topic maps design Authoring
Digital repositories
Topic map
English 2007 0 0