Conferences Review – AAAI and IJCAI

1. Conferences Review– AAAI and IJCAI Sean

2. Outline • Introduction to AAAI • Selected papers from AAAI (3) • Introduction to IJCAI • Selected papers from IJCAI (3) • Summary

3. Introduction to AAAI • Association for the Advancement of Artificial Intelligence conference on Artificial Intelligence (AAAI) • Annual conference in summer (from 1980) • Totally 24 sessions by now • Acceptance rate: 25%~30% • No AAAI 2009 • Related tracks • AI and the Web Track (Special track) • Natural Language Processing • Knowledge-Based Information Systems • Machine Learning • Major groups are from engineering school (algorithms and IS) • Qiang Yang et al., HKUST, Hong Kong • Changshui Zhang et al., Tsinghua University, China • Zhejiang University, China • Zhi-Hua Zhou et al. Nanjing University, China

4. Selected Papers from AAAI • AAAI-10 outstanding paper awards • How Incomplete Is Your Semantic Web Reasoner? Systematic Analysis of the Completeness of Query Answering Systems • AI and the Web Track (Special track, AAAI-10) • Giorgos Stoilos, Bernardo Cuenca Grau, Ian Horrocks (Oxford University, UK) • Other selected papers • Modeling Dynamic Multi-Topic Discussions in Online Forums • AI and the Web Track (Special track, AAAI-10) • Hao Wu, Jiajun Bu, Chun Chen, Can Wang, Guang Qiu, Lijun Zhang and Jianfeng Shen (Zhengjiang University, China) • Learning to Predict Opinion Share in Social Networks • AI and the Web Track (Special track, AAAI-10) • Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda (Osaka University et al., Japan)

5. Selected Papers from AAAI • AAAI-10 outstanding paper awards • How Incomplete Is Your Semantic Web Reasoner? Systematic Analysis of the Completeness of Query Answering Systems • AI and the Web Track (Special track, AAAI-10) • Giorgos Stoilos, Bernardo Cuenca Grau, Ian Horrocks (Oxford University, UK) • Other selected papers • Modeling Dynamic Multi-Topic Discussions in Online Forums • AI and the Web Track (Special track, AAAI-10) • Hao Wu, Jiajun Bu, Chun Chen, Can Wang, Guang Qiu, Lijun Zhang and Jianfeng Shen (Zhengjiang University, China) • Learning to Predict Opinion Share in Social Networks • AI and the Web Track (Special track, AAAI-10) • Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda (Osaka University et al., Japan)

6. Introduction • Introduction • Web Ontology Language (OWL) plays a key role in the Semantic Web Reasoner of a query answering system • For data: describe the meaning of the data • For user: provide answers to query • Completeness vs. efficiency • Completeness: use ontology to provide all possible answers to a query • Efficiency: ignore ontology, just use simple matching • In practical applications, incompleteness is chosen, which lies between completeness and efficiency • Research question and challenges • How to evaluate the completeness of a semantic web reasoner? • Data is not generic and exhaustive (to provide all possible answers to a query) • Answers may not be verifiable

7. Algorithms • Ontology benchmark: Lehigh University Benchmark (LUBM) • An ontology describing an academic domain • Including the ontology, the testing datasets and testing queries • Proposed framework • Step 1: generate an “n-exhaustive” testing datasets based on LUBM ontology using the proposed algorithm (SyGENiA) • The generation of testing datasets in LUBM are hard-coded and is not exhaustive • Exhaustive testing datasets is proved to be impossible to generate due to exponential increase of computing time w.r.t. the scale of the ontology • “n-exhaustive” testing datasets can be used as an approximation to exhaustive testing datasets, which is derived by adding some constraints to the generation of exhaustive testing datasets • Step 2: test the proposed “n-exhaustive” testing datasets generated by SyGENiA using some query answering systems and compare the result to that of the benchmark (LUBM)

8. Results • The results show that • For all 4 systems, the testing datasets generated by SyGENiA indicate more incompleteness that of LUBM • Provide a practical algorithms to generate testing datasets to test the completeness of query answering systems • For AI lab research • Build and test ontology for online text in social media (BI)

9. Selected Papers from AAAI • AAAI-10 outstanding paper awards • How Incomplete Is Your Semantic Web Reasoner? Systematic Analysis of the Completeness of Query Answering Systems • AI and the Web Track (Special track, AAAI-10) • Giorgos Stoilos, Bernardo Cuenca Grau, Ian Horrocks (Oxford University, UK) • Other selected papers • Modeling Dynamic Multi-Topic Discussions in Online Forums • AI and the Web Track (Special track, AAAI-10) • Hao Wu, Jiajun Bu, Chun Chen, Can Wang, Guang Qiu, Lijun Zhang and Jianfeng Shen (Zhengjiang University, China) • Learning to Predict Opinion Share in Social Networks • AI and the Web Track (Special track, AAAI-10) • Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda (Osaka University et al., Japan)

10. Introduction • Introduction • Topics diffuse among online social network by self-preference and peer-influence • Three aspects to consider • The diffusion of generic information (B-TFM) • The diffusion of certain topics (T-TFM) • Fading of interest on topic during diffusion (TT-TFM) • Research questions • How to model the topic diffusion considering both self-preference and peer-influence? • How to analyze the diffusion of specific topics at specific time?

11. Algorithms • B-TFM • Use reply-to relationship to build adjacent matrix of social network for random walk (peer-influence) • Use the number of replies of each user to measure the intensity of participation (self-preference) • Combine peer-influence and self-preference into a single measure called “ParticipationRank”, updated at each time point • T-TFM • Use LDA for topic analysis of each thread • Build separate social networks for each topic, and use the topic strength to adjust the transition probabilities in adjacent matrices • TT-TFM • Add time lapse factor such that the transition probabilities in the adjacent matrix of each topic social network fade with time

12. Results • Dataset: Drag Racing, Honda/Acura (Honda-tech forum) • Task: to predict if a user will participate in the discussion of a specific topic at a certain time point by ParticipationRank • Results show that TT-TFM performs the best • For AI lab research • Study viral marketing in social media (BI)

13. Selected Papers from AAAI • AAAI-10 outstanding paper awards • How Incomplete Is Your Semantic Web Reasoner? Systematic Analysis of the Completeness of Query Answering Systems • AI and the Web Track (Special track, AAAI-10) • Giorgos Stoilos, Bernardo Cuenca Grau, Ian Horrocks (Oxford University, UK) • Other selected papers • Modeling Dynamic Multi-Topic Discussions in Online Forums • AI and the Web Track (Special track, AAAI-10) • Hao Wu, Jiajun Bu, Chun Chen, Can Wang, Guang Qiu, Lijun Zhang and Jianfeng Shen (Zhengjiang University, China) • Learning to Predict Opinion Share in Social Networks • AI and the Web Track (Special track, AAAI-10) • Masahiro Kimura, Kazumi Saito, Kouzou Ohara, Hiroshi Motoda (Osaka University et al., Japan)

14. Introduction • Introduction • Multiple opinions diffusion in social network • Voter model • Value-weighted voter model • Property of value-weighted voter model • Eventually one opinion will win and others will die out • Share of opinion • The percentage of population that hold a certain opinion • Research questions • How to predict the share of opinions at a future time point in social networks?

15. Algorithms • The algorithm aims to estimate the weight value of each opinion by maximizing the log-likelihood function of the vector of weight values • Algorithm • Step 1: initialize all weight value to 0 • Step 2: calculate the first order derivative of the log-likelihood function • Step 3: if the first order derivative is sufficiently small (below a given threshold), terminate. Otherwise, go to step 4 • Step 4: calculate the Hessian matrix (second order derivative) and update the vector of weight values by multiplying the inverted Hessian matrix, return to step 2 • Benchmark • Naïve linear method: simple linear regression • Datasets (social networks) • Japanese blog networks, list of people in Japanese Wikipedia

16. Results • Results show that performance of predicting opinion shares with the proposed learning method is better • For AI lab research • Topic/information diffusion in social media (BI/GeoPolitical)

17. Introduction to IJCAI • International Joint Conferences on Artificial Intelligence (IJCAI) • Biennial conference in summer (from 1969) • Totally 20 sessions by now • Acceptance rate: 20%~25% • Related tracks • Web and Knowledge-based Information Systems • Natural Language Processing • Machine Learning • Major groups are from engineering school (algorithms) • Changshui Zhang et al., Tsinghua University, China • Jieping Ye et al., Arizona State University, Arizona • Qiang Yang et al., HKUST, Hong Kong • Zhengjiang University, China • Zhi-Hua Zhou et al. Nanjing University, China • University of Illinois at Chicago, Illinois

18. Selected Papers from IJCAI • IJCAI-09 distinguished paper awards • Learning Conditional Preference Networks with Queries • Uncertainty in AI • Frédéric Koriche, Bruno Zanuttini (Université de Caen Basse-Normandie, France) • Other selected papers • Efficient Estimation of Influence Functions for SIS Model on Social Networks • Web and Knowledge-based Information Systems • Masahiro Kimura, Kazumi Saito, Hiroshi Motoda (Osaka University et al., Japan) • Incorporating User Behaviors in New Word Detection • Web and Knowledge-based Information Systems • Yabin Zheng, Zhiyuan Liu, Maosong Sun, Liyun Ru, Yang Zhang (Tsinghua University, China)

19. Selected Papers from IJCAI • IJCAI-09 distinguished paper awards • Learning Conditional Preference Networks with Queries • Uncertainty in AI • Frédéric Koriche, Bruno Zanuttini (Université de Caen Basse-Normandie, France) • Other selected papers • Efficient Estimation of Influence Functions for SIS Model on Social Networks • Web and Knowledge-based Information Systems • Masahiro Kimura, Kazumi Saito, Hiroshi Motoda (Osaka University et al., Japan) • Incorporating User Behaviors in New Word Detection • Web and Knowledge-based Information Systems • Yabin Zheng, Zhiyuan Liu, Maosong Sun, Liyun Ru, Yang Zhang (Tsinghua University, China)

20. Introduction • Introduction • Conditional Preference Networks (CP-nets) • A graph where each node (attribute) is labelled with a table describing the user’s preference over alternative values of this node given different values of the parent nodes • Traditional way of building CP-nets • Select possible attributes • Asking a user for the preference of each attribute • Build the CP-net by the collected information • Challenges • A minimal set of attributes must be selected to build the CP-net • Too many irrelevant attributes will lead to low efficiency • Research question • How to design an efficient algorithm to build CP-net by actively feeding queries (preference relationships) to the algorithm?

21. Conditional Preference Networks

22. Algorithms Test if a preference relationship is consistent in N (current CP-net) If false, take the counter example | If there are rules (of a node) that involve the counter example | | Find the parent nodes of the node | | Expand the conditions of the rules using parent nodes | Else | | Add the node and the rules to N Return N • Advantages of the proposed algorithm • Integrates the learning and preference testing together, which are separated in traditional way • The computational complexity is proved to be linear in the size of CP-net and logarithmic in the number of attributes • For AI lab research • Recommendation systems in social media (BI)

23. Selected Papers from IJCAI • IJCAI-09 distinguished paper awards • Learning Conditional Preference Networks with Queries • Uncertainty in AI • Frédéric Koriche, Bruno Zanuttini (Université de Caen Basse-Normandie, France) • Other selected papers • Efficient Estimation of Influence Functions for SIS Model on Social Networks • Web and Knowledge-based Information Systems • Masahiro Kimura, Kazumi Saito, Hiroshi Motoda (Osaka Universityet al., Japan) • Incorporating User Behaviors in New Word Detection • Web and Knowledge-based Information Systems • Yabin Zheng, Zhiyuan Liu, Maosong Sun, Liyun Ru, Yang Zhang (Tsinghua University, China)

24. Introduction • Introduction • SIS (Susceptible-Infected-Susceptible) model describes the repeated diffusion of a topic in social network • Influence function • σ(v,t): expected number of nodes infected by v at time t when v was infected at t=0 • Research question • How to estimate the influence function of each node by effective (in terms of computational time) simulation? • Layered graph method • All vertices are presented • Only edges through which topic diffused are added at time t • The graph (edges) evolve with time • Proposed technique and algorithms • Bond percolation (BP) • Bond percolation with pruning method: retain only one node when many nodes have exactly the same influence path at time t

25. Algorithms

26. Results • Advantages • The influence function of all nodes are estimated simultaneously • The number of edges in the graph are significantly reduced when propagation probability is small • For AI lab research • SIS/SIR model simulation in social media (BI/GeoPolitical)

27. Selected Papers from IJCAI • IJCAI-09 distinguished paper awards • Learning Conditional Preference Networks with Queries • Uncertainty in AI • Frédéric Koriche, Bruno Zanuttini (Université de Caen Basse-Normandie, France) • Other selected papers • Efficient Estimation of Influence Functions for SIS Model on Social Networks • Web and Knowledge-based Information Systems • Masahiro Kimura, Kazumi Saito, Hiroshi Motoda (Osaka University et al., Japan) • Incorporating User Behaviors in New Word Detection • Web and Knowledge-based Information Systems • Yabin Zheng, Zhiyuan Liu, Maosong Sun, Liyun Ru, Yang Zhang (Tsinghua University, China)

28. Introduction • Introduction • Why study new word detection • Try to identify out-of-vocabulary words • Useful for language with no natural word boundaries (e.g. Chinese) • Lexicons • Cell dictionary: domain specific lexicons • User dictionary: user specific lexicons • Word features • Coverage: how many users have used a word (popularity) • Discriminability: the ratio of popularity of a word among users from a specific domain and users outside that specific domain • Research question • How to detect new words in domain-specific fields based on user behavior?

29. Algorithms • Step 1: • Identify top n representative words from every domain using the combination of coverage and discriminability • Step 2: • Identify users who use the representative words very frequently as potential experts • Step 3: • Identify new words by their popularity among potential experts and other users

30. Results • Dataset: generated from Sogou (搜狗) Chinese input method • Benchmarks: Google Sets, Bayesian Sets • Evaluation metrics (relevant documents) • Bpref: binary preference measure • MRR: mean reciprocal rank • P@n: precision at n • For AI lab research • Features selection for text mining in social media (BI)

31. Summary • All papers focused on algorithms development • Possible take-away for AI lab • Topic diffusion analysis in social media for both empirical analysis and simulation • Feature selection using collaborative filtering