OpenRaiser/Intern-Atlas huggingface dataset:
SmartComposition: Enhanced Web Components for a Better Future of Web Development
Authors: Michael Krug, Martin Gaedke
Venue: WWW
Year: 2015

In this paper, we introduce the usage of enhanced Web Components to create web applications with multi-device capabilities by composition. By using the latest developments of the family of W3C standards called "Web Components" that we extent with dedicated communication and synchronization functionality, web developers are enabled to create web applications with ease. We enhance Web Components with an event-based communication channel, which is not limited to a single browser window. With our approach, applications using the extended SmartComponents and an additional synchronization service also support multi-device scenarios. In contrast to other widget-based approaches (W3C Widgets, OpenSocial containers), the usage of SmartComponents does not require a dedicated platform, like Apache Rave. SmartComponents are based on standard web technologies, are natively supported by recent web browsers and loosely coupled using our extension. This ensures a high level of reuse. We show how SmartComponents are structured, can be created and used. Furthermore, we explain how the communication aspect is integrated and multi-device communication is achieved. Finally, we describe our demonstration by outlining two example applications.
Enabling access to Linked Media with SPARQL-MM
Authors: Thomas Kurz, Kai Schlegel, Harald Kosch
Venue: WWW
Year: 2015

The amount of audio, video and image data on the web is immensely growing, which leads to data management problems based on the hidden character of multimedia. Therefore the interlinking of semantic concepts and media data with the aim to bridge the gap between the document web and the Web of Data has become a common practice and is known as Linked Media. However, the value of connecting media to its semantic meta data is limited due to lacking access methods specialized for media assets and fragments as well as to the variety of used description models. With SPARQL-MM we extend SPARQL, the standard query language for the Semantic Web with media specific concepts and functions to unify the access to Linked Media. In this paper we describe the motivation for SPARQL-MM, present the State of the Art of Linked Media description formats and Multimedia query languages, and outline the specification and implementation of the SPARQL-MM function set.
Temporality in Online Food Recipe Consumption and Production
Authors: Tomasz Kusmierczyk, Christoph Trattner, Kjetil Nørvåg
Venue: WWW
Year: 2015

In this paper, we present work-in-progress of a recently started research effort that aims at understanding the hidden temporal dynamics in online food communities. In this context, we have mined and analyzed temporal patterns in terms of recipe production and consumption in a large German community platform. As our preliminary results reveal, there are indeed a range of hidden temporal patterns in terms of food preferences and in particular in consumption and production. We believe that this kind of research can be important for future work in personalized Web-based information access and in particular recommender systems.
Temporal Patterns in Online Food Innovation
Authors: Tomasz Kusmierczyk, Christoph Trattner, Kjetil Nørvåg
Venue: WWW
Year: 2015

Since innovation plays an important role in the context of food, as evident in how successful chefs, restaurants or cuisines in general evolve over time, we were interested in exploring this dimension from a more virtual perspective. In particular, the paper presents results of a study that was conducted in the context of a large-scale German online food community forum to explore another important dimension of online food recipe production, namely known as online food innovation. The study shows interesting findings and temporal patterns in terms of how online food recipe innovation takes place.
Search Query Categorization at Scale
Authors: Michal Laclavik, Marek Ciglan, Sam Steingold, Martin Seleng, Alex Dorman, Stefan Dlugolinský
Venue: WWW
Year: 2015

State of the art query categorization methods usually exploit web search services to retrieve the best matching web documents and map them to a given taxonomy of categories. This is effective but impractical when one does not own a web corpus and has to use a 3rd party web search engine API. The problem lies in performance and in financial costs. In this paper, we present a novel, fast and scalable approach to categorization of search queries based on a limited intermediate corpus: we use Wikipedia as the knowledge base. The presented solution relies on two steps: first a query is mapped to the relevant Wikipedia pages; second, the retrieved documents are categorized into a given taxonomy. We approach the first challenge as an entity search problem and present a new document categorization approach for the second step. On a standard data set, our approach achieves results comparable to the state-of-the-art approaches while maintaining high performance and scalability.
A Graph-Based Recommendation Framework for Price-Comparison Services
Authors: Sang-Chul Lee, Sang-Wook Kim, Sunju Park
Venue: WWW
Year: 2015

In this paper, we propose a set of recommendation strategies and develop a graph-based framework for recommendation in online price-comparison services. We verify the superiority of the proposed framework by comparing it with existing methods using real-world data.
Finding the Differences between the Perceptions of Experts and the Public in the Field of Diabetes
Authors: Dahee Lee, Won Chul Kim, Min Song
Venue: WWW
Year: 2015

Automatic information extraction techniques such as named entity recognition and relation extraction have been developed but it is yet rare to apply them to various document types. In this paper, we applied them to academic literature and social media's contents in the field of diabetes to find distinctions between the perceptions of biomedical experts and the public. We analyzed and compared the experts' and the public's networks constituted by the extracted entities and relations. The results confirmed that there are some differences in their views, i.e., biomedical entities that interest them and relations within their knowledge range.
LIKE and Recommendation in Social Media
Authors: Dongwon Lee, Huan Liu
Venue: WWW
Year: 2015

This tutorial covers the state-of-the-art developments in LIKE and recommendation in social media. It is designed for graduate students, practitioners, or IT managers with general understanding on WWW and social media. No prerequisite is expected.
Ajax API Self-adaptive Framework for End-to-end User
Authors: Xiang Li, Zhiyong Feng, Keman Huang, Shizhan Chen
Venue: WWW
Year: 2015

Web developers often use Ajax API to build the rich Internet application (RIA). Due to the uncertainty of the environment, automatically switching among different Ajax APIs with similar functionality is important to guarantee the end-to-end performance. However, it is challenging and time-consuming because it needs to manually modify codes based on the API documentation. In this paper, we propose a framework to address the self-adaption and difficulty in invoking Ajax API. The Ajax API wrapping model, consisting of the specific and abstract components, is proposed to automatically construct the grammatical and functional semantic relations between Ajax APIs. Then switching module is introduced to support the automatic switching among different Ajax APIs, according to the user preference and QoS of Ajax APIs. Taking the map APIs, i.e. Google Map, Baidu Map, Gaode Map, 51 Map and Tencent Map as an example, the demo shows that the framework can facilitate the construction of RIA and improve adaptability of the application. The process of selection and switching in the different Ajax APIs is automatic and transparent to the users.
Geo-Social Media Analytics
Authors: Cheng-Te Li, Hsun-Ping Hsieh
Venue: WWW
Year: 2015

With the maturity of wireless communication techniques, GPS-equipped mobile devices become ubiquitous, and location-acquisition technologies and services are flourishing. These location applications as well as mobile devices, developed and combined with the social networking services, foster the emergence of geo-social media, a novel type of user-generated geo-social data, such as data from Facebook, Twitter, and Foursquare. In geo-social media, social connections and geo-location information of users are the essential elements, which keep track of their user interactions and their spatial-temporal activities. While social interactions are depicted by online network structures, and geographical activities are usually represented as check-in records. Due to the pervasive mobility of users, a huge amount of user-generated geo-social data is rapidly generated. Such big geo-social data not only collectively represents diverse kinds of real-world human activities, but also serves as a handy resource for various geo-social applications. In this tutorial, we aim to present the recent advances on geo-social media analytics in a systematic manner, consisting of five parts: (a) properties of geo-social networks, which unveil the relationships between human mobility and social structures; (b) geo-social link prediction, using geographical, mobility, activity features with various inference models; (c) location recommendation, leveraging personal, social, contextual, geographical, and content information; (d) geo-social influence propagation and maximization; and (e) connecting online and offline social networks for revisiting conventional SNA wisdom and developing applications that bridge virtual and physical social worlds. We also highlight the unsolved problems for each of the aforementioned topics and future directions of geo-social media analytics. We believe this tutorial can benefit the research communities of mobile web, data mining, information retrieval, social network analysis, recommender system, and marketing and advertisement.
Answer Quality Characteristics and Prediction on an Academic Q&A Site: A Case Study on ResearchGate
Authors: Lei Li, Daqing He, Wei Jeng, Spencer Goodwin, Chengzhi Zhang
Venue: WWW
Year: 2015

Despite various studies on examining and predicting answer quality on generic social Q&A sites such as Yahoo! Answers, little is known about why answers on academic Q&A sites are voted on by scholars who follow the discussion threads to be high quality answers. Using 1021 answers obtained from the Q&A part of an academic social network site ResearchGate (RG), we firstly explored whether various web-captured features and human-coded features can be the critical factors that influence the peer-judged answer quality. Then using the identified critical features, we constructed three classification models to predict the peer-judged rating. Our results identify four main findings. Firstly, responders' authority, shorter response time and greater answer length are the critical features that positively associate with the peer-judged answer quality. Secondly, answers containing social elements are very likely to harm the peer-judged answer quality. Thirdly, an optimized SVM algorithm has an overwhelming advantage over other models in terms of accuracy. Finally, the prediction based on web-captured features had better performance when comparing to prediction on human-coded features. We hope that these interesting insights on ResearchGate's answer quality can help the further design of academic Q&A sites.
Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation
Authors: Liangyue Li, Hanghang Tong, Nan Cao, Kate Ehrlich, Yu-Ru Lin, Norbou Buchler
Venue: WWW
Year: 2015

In this paper, we study the problem of TEAM MEMBER REPLACEMENT -- given a team of people embedded in a social network working on the same task, find a good candidate to best replace a team member who becomes unavailable to perform the task for certain reason (e.g., conflicts of interests or resource capacity). Prior studies in teamwork have suggested that a good team member replacement should bring synergy to the team in terms of having both skill matching and structure matching. However, existing techniques either do not cover both aspects or consider the two aspects independently. In this work, we propose a novel problem formulation using the concept of graph kernels that takes into account the interaction of both skill and structure matching requirements. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations and user studies on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall and (b) scale sub-linearly.
Understanding Complex Networks Using Graph Spectrum
Authors: Yanhua Li, Zhi-Li Zhang
Venue: WWW
Year: 2015

Complex networks are becoming indispensable parts of our lives. The Internet, wireless (cellular) networks, online social networks, and transportation networks are examples of some well-known complex networks around us. These networks generate an immense range of big data: weblogs, social media, the Internet traffic, which have increasingly drawn attentions from the computer science research community to explore and investigate the fundamental properties of, and improve the user experiences on, these complex networks. This work focuses on understanding complex networks based on the graph spectrum, namely, developing and applying spectral graph theories and models for understanding and employing versatile and oblivious network information -- asymmetrical characteristics of the wireless transmission channels, multiplex social relations, e.g., trust and distrust relations, etc -- in solving various application problems, such as estimating transmission cost in wireless networks, Internet traffic engineering, and social influence analysis in social networks.
Feature Selection for Sentiment Classification Using Matrix Factorization
Authors: Jiguang Liang, Xiaofei Zhou, Li Guo, Shuo Bai
Venue: WWW
Year: 2015

Feature selection is a critical task in both sentiment classification and topical text classification. However, most existing feature selection algorithms ignore a significant contextual difference between them that sentiment classification is commonly depended more on the words conveying sentiments. Based on this observation, a new feature selection method based on matrix factorization is proposed to identify the words with strong inter-sentiment distinguish-ability and intra-sentiment similarity. Furthermore, experiments show that our models require less features while still maintaining reasonable classification accuracy.
A Word Vector and Matrix Factorization Based Method for Opinion Lexicon Extraction
Authors: Zheng Lin, Weiping Wang, Xiaolong Jin, Jiguang Liang, Dan Meng
Venue: WWW
Year: 2015

Automatic opinion lexicon extraction has attracted lots of attention and many methods have thus been proposed. However, most existing methods depend on dictionaries (e.g., WordNet), which confines their applicability. For instance, the dictionary based methods are unable to find domain dependent opinion words, because the entries in a dictionary are usually domain-independent. There also exist corpus-based methods that directly extract opinion lexicons from reviews. However, they heavily rely on sentiment seed words that have limited sentiment information and the context information has not been fully considered. To overcome these problems, this paper presents a word vector and matrix factorization based method for automatically extracting opinion lexicons from reviews of different domains and further identifying the sentiment polarities of the words. Experiments on real datasets demonstrate that the proposed method is effective and performs better than the state-of-the-art methods.
An Approach to Support Data Integrity for Web Services Using Semantic RESTful Interfaces
Authors: Hermano Albuquerque Lira, José Renato Villela Dantas, Bruno de Azevedo Muniz, Tadeu Matos Nunes, Pedro Porfírio Muniz Farias
Venue: WWW
Year: 2015

In the Web, linked data is growing rapidly due to its potential to facilitate data retrieval and data integration. At the same time, relational database systems store a vast amount of data published on the Web. Current linked data in the Web is mainly read only. It allows for integration, navigation, and consultations in large structured datasets, but it still lacks a general concept for reading and writing. This paper proposes a specification, entitled Semantic Data Services (SDS), for RESTful Web services that provide data access. To provide linked data read-write capacity, SDS proposes a mechanism for integrity checking that is analogous to that used in relational databases. SDS implements the Semantic Restful Interface (SERIN) specification. SERIN uses annotations on classes in an ontology, describing the semantic web services that are available to manipulate data. This work extends SERIN specification adding annotations to allow the adoption of data access integrity constraints.
Contextual Query Intent Extraction for Paid Search Selection
Authors: Pengqi Liu, Javad Azimi, Ruofei Zhang
Venue: WWW
Year: 2015

Paid Search algorithms play an important role in online advertising where a set of related ads is returned based on a searched query. The Paid Search algorithms mostly consist of two main steps. First, a given searched query is converted to different sub-queries or similar phrases which preserve the core intent of the query. Second, the generated sub-queries are matched to the ads bidded keywords in the data set, and a set of ads with highest utility measuring relevance to the original query are returned. The focus of this paper is optimizing the first step by proposing a contextual query intent extraction algorithm to generate sub-queries online which preserve the intent of the original query the best. Experimental results over a very large real-world data set demonstrate the superb performance of proposed approach in optimizing both relevance and monetization metrics compared with one of the existing successful algorithms in our system.
Science Navigation Map: an Interactive Data Mining Tool for Literature Analysis
Authors: Yu Liu, Zhen Huang, Yizhou Yan, Yufeng Chen
Venue: WWW
Year: 2015

With the advances of all research fields and web 2.0, scientific literature has been widely observed in digital libraries, citation databases, and social media. Its new properties, such as large volume, wide exhibition, and the complicated citation relationship in papers bring challenges to the management, analysis and exploring knowledge of scientific literature. In addition, although data mining techniques have been imported to scientific literature analysis tasks, they typically requires expert input and guidance, and returns static results to users after process, which makes them inflexible and not smart. Therefore, there is the need of a tool, which highly reflects article-level-metrics and combines human users and computer systems for analysis and exploring knowledge of scientific literature, as well as discovering and visualizing underlying interesting research topics. We design an online tool for literature navigation, filtering, and interactive data mining, named Science Navigation Map (SNM), which integrates information from online paper repositories, citation databases, etc. SNM provides visualization of article level metrics and interactive data mining which takes advantage of effective interaction between human users and computer systems to explore and extract knowledge from scientific literature and discover underlying interesting research topics. We also propose a multi-view non-negative matrix factorization and apply it to SNM as an interactive data mining tool, which can make better use of complicated multi-wise relationships in papers. In experiments, we visualize all the papers published at the journal of PLOS Biology from 2003 to 2012 in the navigation map and explore six relationship in papers for data mining. From this map, one can easily filter, analyse and explore knowledge of the papers through an interactive way.
GalaxyExplorer: Influence-Driven Visual Exploration of Context-Specific Social Media Interactions
Authors: Xiaotong Liu, Srinivasan Parthasarathy, Han-Wei Shen, Yifan Hu
Venue: WWW
Year: 2015

The ever-increasing size and complexity of social networks place a fundamental challenge to visual exploration and analysis tasks. In this paper, we present \textit{GalaxyExplorer}, an influence-driven visual analysis system for exploring users of various influence and analyzing how they influence others in a social network. GalaxyExplorer reduces the size and complexity of a social network by dynamically retrieving theme-based graphs, and analyzing users' influence and passivity regarding specific themes and dynamics in response to disaster events. In GalaxyExplorer, a galaxy-based visual metaphor is introduced to simplify the visual complexity of a large graph with a focus+context view. Various interactions are supported for visual exploration. We present experimental results on real-world datasets that show the effectiveness of GalaxyExplorer in theme-aware influence analysis.
Collaborative Datasets Retrieval for Interlinking on Web of Data
Authors: Haichi Liu, Jintao Tang, Dengping Wei, Peilei Liu, Hong Ning, Ting Wang
Venue: WWW
Year: 2015

Dataset interlinking is a great important problem in Linked Data. We consider this problem from the perspective of information retrieval in this paper, thus propose a learning to rank based framework, which combines various similarity measures to retrieve the relevant datasets for a given dataset. Specifically, inspired by the idea of collaborative filtering, an effective similarity measure called collaborative similarity is proposed. Experimental results show that the collaborative similarity measure is effective for dataset interlinking, and the learning to rank based framework can significantly increase the performance.
Scalable Parallel EM Algorithms for Latent Dirichlet Allocation in Multi-Core Systems
Authors: Xiaosheng Liu, Jia Zeng, Xi Yang, Jianfeng Yan, Qiang Yang
Venue: WWW
Year: 2015

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling tool for content analysis such as web mining. To handle web-scale content analysis on just a single PC, we propose multi-core parallel expectation-maximization (PEM) algorithms to infer and estimate LDA parameters in shared memory systems. By avoiding memory access conflicts, reducing the locking time among multiple threads and residual-based dynamic scheduling, we show that PEM algorithms are more scalable and accurate than the current state-of-the-art parallel LDA algorithms on a commodity PC. This parallel LDA toolbox is made publicly available as open source software at mloss.org.