Bring Semantic Web to Social Communities

Transcription

1 Bring Semantic Web to Social Communities Jie Tang Dept. of Computer Science, Tsinghua University, China April 19, 2010 Abstract Recently, more and more researchers have recognized the importance of knowledge discovery from the data generated in these social sites. Promising results have been presented in work alone this line. The results are mostly useful for analysts, to better describe the characteristics of social communities and better understand user behaviours. The real impact of mining social data and social semantic techniques, however, has not reached into the social communities and social users. People who created the data are not people who benefit from the analysis of the data. How can we leverage the knowledge discovered from social data to enhance experiences of users in social communities? To diminish misbehaviours? To facilitate social learning and social influence? To improve the quality of user-generated data? Search engine has provided a good example in the context of Web 1.0, by analysing the data generated by a search engine (i.e., search logs) to enhance search quality. How can we generalize this success in Web 2.0, by mining user-generated data to influence social behaviour of the same group of users? In this paper, we will discuss some potentially promising directions to bridge the gap between data analysis and user-centered applications, bring together researchers in semantic web and social computing. We will share our experience, success, and lesson learned in developing semantic social systems. 1 Introduction Semantic web aims to bridge computers and human beings by enabling the computers to understand the meaning (semantics) of information and services on the web. In essence, two core tasks to realize semantic web are defining/generating semantics and satisfying users requests based on the semantic information. In the past years, many researches have been conducted to this end. However, a prevalent method is to ask the user to create the semantics on the web and then domain experts define reasoning rules to help locate the information to satisfy users requests. This method has been criticized recently due to its high infeasibility. First, people are not willing to create the semantic content. Although, some tools have been developed to facilitate the semantic annotation task, people who created the semantic data are not people who benefit from the data. There is no motivation for people to create the semantics. Second, the 1

2 complex reasoning rules defined by the domain experts are inapplicable also. The web are evolving rapidly. A rule may be correct at a specific time, but may quickly become out-of-date. On the other hand, with the emergence and rapid proliferation of social applications and media, such as instant messaging (e.g., IRC, AIM, MSN, Jabber, Skype), sharing sites (e.g., Flickr, Picassa, YouTube, Plaxo), blogs (e.g., Blogger, WordPress, LiveJournal), wikis (e.g., Wikipedia, PBWiki), microblogs (e.g., Twitter, Jaiku), social networks (e.g., MySpace, Facebook, Ning), collaboration networks (e.g., DBLP) to mention a few, there is little doubt that social network is becoming a popular research topic, attracting tremendous interest from mathematics, biology, physics, computer science, and sociology. The social web provides an opportunity to obtain users generated data, for example users on Twitter now send out more than 50 million tweets per day; at the same time, it also poses several unique challenges. Most existing researches have focused on finding the macro-level mechanisms of the social influence such as degree distributions, diameter, clustering coefficient, communities, and small world effect [1, 2, 4, 5]. However, these methods provide us limited insight into the micro-level dynamics of the social network such as how an individual user changes his behaviors (actions) and how a user s action influences his friends. In this paper, we discuss the challenges we are facing with and introduce our experiences in developing an academic social network system. 2 Challenges There are still many challenging issues for semantic web in the social communities. 1. Lack of semantic-based information The semantic information obtained from the user generated or extracted by using heuristics is often incomplete or inconsistent. Users do not fill some information merely because they are not willing to fill the information. A challenging problem is how to extract the semantic information from the social web. 2. Integration of semantic data To integrate the semantic data from different sources, one needs not only find the alignment between the heterogeneous schemas of these data, but also solve the object resolution problem (an identical instance has multiple representation forms and a same representation may refer to multiple meanings). This is also a fundamental problem for the Linked data vision. 3. Modeling and search of semantic data The (semantic) web is rather heterogeneous, which gives rise to several challenging issues and make it different from the general search engine. First, the informationseeking practice [3] is not only about documents, but also about other information sources, such as in the academic network it includes authors, papers, conferences and journals, etc. In this spirit, a good search engine should not only provide support for documents, but also for all these information sources. Second, semantic search typically requires much higher retrieval accuracy. Given a query, such as semantic 2

3 address phone fax research_interest affiliation position person_photo homepage title start_page end_page date download_url bsuniv bsdate bsmajor Researcher author Publication cite msmajor msuniv msdate coauthor editor/reviewer Journal published_at phddate publisher phdmajor phduniv member_of is_part_of chair/pc_member Publication Venue homepage Organization Conference host_by location description location date relationship property sub_class Figure 1: The schema of academic network. web, a user does not typically mean to find documents containing these two words. Her/his intention is to find documents on the semantic web topic. These two issues are often intertwined. For instance, we need not only consider the search accuracy of documents, but for other information sources as well. Now, the problem is how to find a principled way to model the heterogeneous semantic data and how to design a ranking method for searching the semantic data with high accuracy. 4. Social influence analysis The social web is not only about data/information, but also about users. It is well known that users actions in a social network are influenced by various factors such as personal interests, social influence, and global trends. However, few work systematically studies how social actions evolve in a dynamic social network and to what extent the different factors affect the user actions. More specifically, how to quantify the strength of social influence between two users? How to estimate the model on real large networks? How to model the social network structure, user attributes and users historical actions so as to predict users behaviors? 3 Our Experiences in Arnetminer As a case study, we have developed Arnetminer, 1 a system aiming to provide comprehensive search and mining services for academic community. As the academic information is located in the distributed web, we need first extract the semantic information from the distributed web. We define the data model of the academic network (as shown in Figure 1). Some of the academic data can be extracted from structured data sources such as the publication information from DBLP; while other data needs to be extracted from 1 3

4 unstructured Web pages such as researchers homepages. We propose a unified approach to extract researcher profiles from the researchers homepages. We integrate the publication data from online databases. We extract the organization information from Wikipedia using regular expressions. Our technique contribution includes the unified approach for researcher profiling [10] and the approach for dealing with the disambiguation problem in the integration [14]. The unified approach for research profiling explored in this paper is based on a new Condition Random Field model called Tree-structured Conditional Random Fields(TCRFs) [6] [9]. Researcher profiling Specifically, the researcher profiling approach consists of three steps: relevant page identification, preprocessing, and tagging. In relevant page identification, given a researcher, we first get a list of web pages by a search engine (we use Google API) and then identify the homepage/introducing page using a classifier. The performance of the classifier is 92.39% in terms of F1-measure. In preprocessing, we separate the text into tokens and assign possible tags to each token. The tokens form the basic units in the following tagging step and the pages form the sequences of units in it. In tagging, given a sequence of units, we determine the most likely corresponding sequence of tags by using a trained tagging model. The type of the tags corresponds to the profile property (as shown in Figure 1). As the tagging model, we use Treestructured Conditional Random Fields (TCRFs) [6]. TCRFs can model dependencies across hierarchically laid-out information. In researcher profile extraction, an identified homepage can be represented as an DOM tree. The root node corresponds to the Web page, a leaf node denotes a word token, and an inner node denotes a coarse information block (e.g., a block containing contact information). For parameter estimation, as the graphical structure in TCRFs can be a tree with cycles, exact inference will be expensive. We propose using the Tree-based Reparameterization (TRP) algorithm [13] to compute the approximate inference. We evaluate the performance of the proposed approach on 2, 000 randomly chosen researchers homepages. Our approach can reach 86.70% (in terms of F1-measure) on average. We compare our method with several state-of-the-art methods, i.e., rule learning based method (Amilcare), classification based method (SVM-based method), and linear-chain CRFs. Our approach significantly outperforms (+3.4%-33.2%) the baseline methods for profile extraction. Integration We collect the publication data from online databases including DBLP, ACM Digital library, Citeseer, and others. For integrating researcher profiles and the publications data, we use the author as identifier. Thus we need to deal with the ambiguity problem. The task of disambiguation is defined as follows: Given a person a, we denote all papers having the author d a as P = {p 1, p 2,, p n }. Suppose there existing k actual researchers {y 1, y 2,, y k } having the a, our task is to assign each of these n papers to its real researcher y i. We propose a probabilistic framework for disambiguation based on Hidden Markov Random Fields (HMRF) [14]. The method effectively improve (+8%) the performance of disambiguation, by comparing with the baseline methods on two real-world data sets. Heterogeneous academic network The extracted/integrated data is stored into an academic network base. With the profiling and integration methods, we have already 4

5 collected 548,504 researcher profiles, 2,858,504 publications, 5,042 conferences, and 32,215,473 paper-paper citation relationships, 47,443,857 coauthor relationships, and 14,720,130 paper-published-at relationships. Based on the academic network, services such as expertise search [7], citation network analysis [12], influence analysis [8], topical graph search, and topic browser [11] have been provided. The system is in operation on the internet for nearly two years and receives a large amount of accesses from 180 countries. Feedbacks from users and system logs indicate that users consider the system really help people to find and share information in the academic community. 4 Conclusion In this paper, we discuss the opportunities and the challenges of semantic web in the social web era. We briefly introduce our work on Arnetminer, an academic social networking system and share our experiences when developing this system. The general problem of semantic web meeting social communities presents an new and interesting research direction in web science. References [1] R. Albert and A. L. Barabasi. Reviews of Modern Physics, 74(1), [2] M. Faloutsos, P. Faloutsos, and C. Faloutsos. On power-law relationships of the internet topology. In SIGCOMM 99, pages , [3] M. Hertzum and A. M. Pejtersen. The information-seeking practices of engineers: Searching for documents as well as for people. Information Processing & Management, 36(5): , [4] M. E. J. Newman. The structure and function of complex networks. SIAM Reviews, 45, [5] S. H. Strogatz. Exploring complex networks. Nature, 410: , [6] J. Tang, M. Hong, J. Li, and B. Liang. Tree-structured conditional random fields for semantic annotation. In ISWC 06, pages , [7] J. Tang, R. Jin, and J. Zhang. A topic modeling approach and its integration into the random walk framework for academic search. In ICDM 08, pages , [8] J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In KDD 09, pages , [9] J. Tang, L. Yao, D. Zhang, and J. Zhang. A combination approach to web user profiling. ACM Transactions on Knowledge Discovery from Data, 2010 (to appear). [10] J. Tang, D. Zhang, and L. Yao. Social network extraction of academic researchers. In ICDM 07, pages , [11] J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su. Arnetminer: Extraction and mining of academic social networks. In KDD 08, pages , [12] J. Tang, J. Zhang, J. X. Yu, Z. Yang, K. Cai, R. Ma, L. Zhang, and Z. Su. Topic distributions over links on web. In ICDM 09, [13] M. J. Wainwright, T. Jaakkola, and A. S. Willsky. Tree-based reparameterization for approximate estimation on loopy graphs. In Proceedings of the 13th Neural Information Processing Systems (NIPS 01), pages , [14] D. Zhang, J. Tang, and J. Li. A constraint-based probabilistic framework for disambiguation. In CIKM 07, pages ,