Conferences review aaai and ijcai
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Conferences Review – AAAI and IJCAI. Sean. Outline. Introduction to AAAI Selected papers from AAAI (3) Introduction to IJCAI Selected papers from IJCAI (3) Summary. Introduction to AAAI.

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Conferences review aaai and ijcai

Conferences Review– AAAI and IJCAI

Sean


Outline

Outline

  • Introduction to AAAI

  • Selected papers from AAAI (3)

  • Introduction to IJCAI

  • Selected papers from IJCAI (3)

  • Summary


Introduction to aaai

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


Selected papers from aaai

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)


Selected papers from aaai1

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)


Introduction

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


Algorithms

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)


Results

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)


Selected papers from aaai2

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)


Introduction1

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?


Algorithms1

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


Results1

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)


Selected papers from aaai3

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)


Introduction2

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?


Algorithms2

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


Results2

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)


Introduction to ijcai

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


Selected papers from ijcai

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)


Selected papers from ijcai1

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)


Introduction3

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?


Conditional preference networks

Conditional Preference Networks


Algorithms3

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)


Selected papers from ijcai2

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)


Introduction4

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


Algorithms4

Algorithms


Results3

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)


Selected papers from ijcai3

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)


Introduction5

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?


Algorithms5

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


Results4

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

    • [email protected]: precision at n

  • For AI lab research

    • Features selection for text mining in social media (BI)


Summary

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


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