OpenRaiser/Intern-Atlas huggingface dataset:
Towards robust service compositions in the context of functionally diverse services
Authors: Florian Wagner, Benjamin Klöpper, Fuyuki Ishikawa, Shinichi Honiden
Venue: WWW
Year: 2012

Web service composition provides a means of customized and flexible integration of service functionalities. Quality-of-Service (QoS) optimization algorithms select services in order to adapt workflows to the non-functional requirements of the user. With increasing number of services in a workflow, previous approaches fail to achieve a sufficient reliability. Moreover, expensive ad-hoc replanning is required to deal with service failures. The major problem with such sequential application of planning and replanning is that it ignores the potential costs during the initial planning and they consequently are hidden from the decision maker. Our basic idea to overcome this substantial problem is to compute a QoS optimized selection of service clusters that includes a sufficient number of backup services for each service employed. To support the human decision maker in the service selection task, our approach considers the possible repair costs directly in the initial composition. On the basis of a multi-objective approach and using a suitable service selection interface, the decision maker can select compositions in line with his/her personal risk preferences.
AMBER: turning annotations into knowledge
Authors: Cheng Wang
Venue: WWW
Year: 2012

Web extraction is the task of turning unstructured HTML into knowledge. Computers are able to generate annotations of unstructured HTML, but it is more important to turn those annotations into structured knowledge. Unfortunately, the current systems extracting knowledge from result pages lack accuracy.
Chinese news event 5W1H semantic elements extraction for event ontology population
Authors: Wei Wang
Venue: WWW
Year: 2012

To relieve "News Information Overload", in this paper, we propose a novel approach of 5W1H (who, what, whom, when, where, how) event semantic elements extraction for Chinese news event knowledge base construction. The approach comprises a key event identification step, an event semantic elements extraction step and an event ontology population step. We first use a machine learning method to identify the key events from Chinese news stories. Then we extract event 5W1H elements by employing the combination of SRL, NER technique and rule-based method. At last we populate the extracted facts of news events to NOEM, an event ontology designed specifically for modeling semantic elements and relations of events. Our experiments on real online news data sets show the reasonability and feasibility of our approach.
Model news relatedness through user comments
Authors: Xuanhui Wang, Jiang Bian, Yi Chang, Belle L. Tseng
Venue: WWW
Year: 2012

Most of previous work on news relatedness focuses on news article texts. In this paper, we study the benefit of user-generated comments on modeling news relatedness. Comments contain rich text information which is provided by commenters and rated by readers with thumb-up or thumb-down, but the quality of individual comments varies widely. We compare different ways of capturing relatedness by leveraging both text and user interaction information in comments. Our evaluation based on an editorial data set demonstrates that the text information in comments is very effective to model relatedness while community rating is quite predictive of the comment quality.
Joint relevance and freshness learning from clickthroughs for news search
Authors: Hongning Wang, Anlei Dong, Lihong Li, Yi Chang, Evgeniy Gabrilovich
Venue: WWW
Year: 2012

In contrast to traditional Web search, where topical relevance is often the main selection criterion, news search is characterized by the increased importance of freshness. However, the estimation of relevance and freshness, and especially the relative importance of these two aspects, are highly specific to the query and the time when the query was issued. In this work, we propose a unified framework for modeling the topical relevance and freshness, as well as their relative importance, based on click logs. We use click statistics and content analysis techniques to define a set of temporal features, which predict the right mix of freshness and relevance for a given query. Experimental results on both historical click data and editorial judgments demonstrate the effectiveness of the proposed approach.
User profile integration made easy: model-driven extraction and transformation of social network schemas
Authors: Martin Wischenbart, Stefan Mitsch, Elisabeth Kapsammer, Angelika Kusel, Birgit Pröll, Werner Retschitzegger, Wieland Schwinger, Johannes Schönböck, Manuel Wimmer, Stephan Lechner
Venue: WWW
Year: 2012

User profile integration from multiple social networks is indispensable for gaining a comprehensive view on users. Although current social networks provide access to user profile data via dedicated APIs, they fail to provide accurate schema information, which aggravates the integration of user profiles, and not least the adaptation of applications in the face of schema evolution. To alleviate these problems, this paper presents, firstly, a semi-automatic approach to extract schema information from instance data. Secondly, transformations of the derived schemas to different technical spaces are utilized, thereby allowing, amongst other benefits, the application of established integration tools and methods. Finally, as a case study, schemas are derived for Facebook, Google+, and LinkedIn. The resulting schemas are analyzed (i) for completeness and correctness according to the documentation, and (ii) for semantic overlaps and heterogeneities amongst each other, building the basis for future user profile integration.
Adapting similarity on the MagnaTagATune database: effects of model and feature choices
Authors: Daniel Wolff, Tillman Weyde
Venue: WWW
Year: 2012

Predicting user's tastes on music has become crucial for a competitive music recommendation systems, and perceived similarity plays an influential role in this. MIR currently turns towards making recommendation systems adaptive to user preferences and context. Here, we consider the particular task of adapting music similarity measures to user voting data. This work builds on and responds to previous publications based on the MagnaTagATune dataset. We have reproduced the similarity dataset presented by Stober and Nürnberger at AMR 2011 to enable a comparison of approaches. On this dataset, we compare their two-level approach, defining similarity measures on individual facets and combining them in a linear model, to the Metric Learning to Rank (MLR) algorithm. MLR adapts a similarity measure that operates directly on low-level features to the user data. We compare the different algorithms, features and parameter spaces with regards to minimising constraint violations. Furthermore, the effectiveness of the MLR algorithm in generalising to unknown data is evaluated on this dataset. We also explore the effects of feature choice. Here, we find that the binary genre data shows little correlation with the similarity data, but combined with audio features it clearly improves generalisation.
Titan: a system for effective web service discovery
Authors: Jian Wu, Liang Chen, Yanan Xie, Zibin Zheng
Venue: WWW
Year: 2012

With the increase of web services and user demand's diversity, effective web service discovery is becoming a big challenge. Clustering web services would greatly boost the ability of web service search engine to retrieve relevant ones. In this paper, we propose a web service search engine Titan which contains 15,969 web services crawled from the Internet. In Titan, two main technologies, i.e., web service clustering and tag recommendation, are employed to improve the effectiveness of web service discovery. Specifically, both WSDL (Web Service Description Language) documents and tags of web services are utilized for clustering, while tag recommendation is adopted to handle some inherent problems of tagging data, e.g., uneven tag distribution and noise tags.
A framework to represent and mine knowledge evolution from Wikipedia revisions
Authors: Xian Wu, Wei Fan, Meilun Sheng, Li Zhang, Xiaoxiao Shi, Zhong Su, Yong Yu
Venue: WWW
Year: 2012

State-of-the-art knowledge representation in semantic web employs a triple format (subject-relation-object). The limitation is that it can only represent static information, but cannot easily encode revisions of semantic web and knowledge evolution. In reality, knowledge does not stay still but evolves over time. In this paper, we first introduce the concept of "quintuple representation" by adding two new fields, state and time, where state has two values, either in or out, to denote that the referred knowledge takes effective or becomes expired at the given time. We then discuss a two-step statistical framework to mine knowledge evolution into the proposed quintuple representation. Utilizing extracted quintuple properly, it not only can reveal knowledge changing history but also detect expired information. We evaluate the proposed framework on Wikipedia revisions, as well as, common web pages currently not in semantic web format.
An exploration of improving collaborative recommender systems via user-item subgroups
Authors: Bin Xu, Jiajun Bu, Chun Chen, Deng Cai
Venue: WWW
Year: 2012

Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets have demonstrated the effectiveness of our proposed approach.
Combining classification with clustering for web person disambiguation
Authors: Jian Xu, Qin Lu, Zhengzhong Liu
Venue: WWW
Year: 2012

Web Person Disambiguation is often conducted through clustering web documents to identify different namesakes for a given name. This paper presents a new key-phrased clustering method combined with a second step re-classification to identify outliers to improve cluster performance. For document clustering, the hierarchical agglomerative approach is conducted based on the vector space model which uses key phrases as the main feature. Outliers of cluster results are then identified through a centroids-based method. The outliers are then reclassified by the SVM classifier into the more appropriate clusters using a key phrase-based string kernel model as its feature space. The re-classification uses the clustering result in the first step as its training data so as to avoid the use of separate training data required by most classification algorithms. Experiments conducted on the WePS-2 dataset show that the algorithm based on key phrases is effective in improving the WPD performance.
Semi-structured semantic overlay for information retrieval in self-organizing networks
Authors: Yulian Yang
Venue: WWW
Year: 2012

As scalability and flexibility have become the critical concerns in information management systems, self-organizing networks attract attentions from both research and industrial communities. This work proposes a semi-structured semantic overlay for information retrieval in large-scale self-organizing networks. With the autonomy to their own resources, the nodes are organized into a semantic overlay hosting topically discriminative communities. For information retrieval within a community, unstructured routing approach is employed for the sake of flexibility; While for joining new nodes and routing queries to a distant community, a structured mechanism is designed to save the traffic and time cost. Different from the semantic overlay in the literature, our proposal has three contributions: 1. we design topic-based indexing to form and maintain the semantic overlay, to guarantee both scalability and efficiency; 2. We introduce unstructured routing approach within the community, to allow flexible node joining and leaving; 3. We take advantage of the interaction among nodes to capture the overlay changes and make corresponding adaption in topic-based indexing.
Music retagging using label propagation and robust principal component analysis
Authors: Yi-Hsuan Yang, Dmitry Bogdanov, Perfecto Herrera, Mohamed Sordo
Venue: WWW
Year: 2012

Comunicació presentada a: WWW'12 Companion: The 21st International Conference on World Wide Web celebrat del 16 al 20 d'abril de 2012 a Lió, França.
Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter
Authors: Chao Yang, Robert Chandler Harkreader, Jialong Zhang, Seungwon Shin, Guofei Gu
Venue: WWW
Year: 2012

In this paper, we perform an empirical analysis of the cyber criminal ecosystem on Twitter. Essentially, through analyzing inner social relationships in the criminal account community, we find that criminal accounts tend to be socially connected, forming a small-world network. We also find that criminal hubs, sitting in the center of the social graph, are more inclined to follow criminal accounts. Through analyzing outer social relationships between criminal accounts and their social friends outside the criminal account community, we reveal three categories of accounts that have close friendships with criminal accounts. Through these analyses, we provide a novel and effective criminal account inference algorithm by exploiting criminal accounts' social relationships and semantic coordinations.
Exploiting various implicit feedback for collaborative filtering
Authors: Byoungju Yang, Sangkeun Lee, Sungchan Park, Sang-goo Lee
Venue: WWW
Year: 2012

So far, many researchers have worked on recommender systems using users' implicit feedback, since it is difficult to collect explicit item preferences in most applications. Existing researches generally use a pseudo-rating matrix by adding up the number of item consumption; however, this naive approach may not capture user preferences correctly in that many other important user activities are ignored. In this paper, we show that users' diverse implicit feedbacks can be significantly used to improve recommendation accuracy. We classify various users' behaviors (e.g., search item, skip, add to playlist, etc.) into positive or negative feedback groups and construct more accurate pseudo-rating matrix. Our preliminary experimental result shows significant potential of our approach. Also, we bring out a question to the previous approaches, aggregating item usage count into ratings.
MECA: mobile edge capture and analysis middleware for social sensing applications
Authors: Fan Ye, Raghu K. Ganti, Raheleh Dimaghani, Keith Grueneberg, Seraphin B. Calo
Venue: WWW
Year: 2012

In this paper, we propose and develop MECA, a common middleware infrastructure for data collection from mobile devices in an efficient, flexible, and scalable manner. It provides a high level abstraction of phenomenon such that applications can express diverse data needs in a declarative fashion. MECA coordinates the data collection and primitive processing activities, so that data can be shared among applications. It addresses the inefficiency issues in the current vertical integration approach. We showcase the benefits of MECA by means of a disaster management application.
Towards expressive exploratory search over entity-relationship data
Authors: Sivan Yogev, Haggai Roitman, David Carmel, Naama Zwerdling
Venue: WWW
Year: 2012

In this paper we describe a novel approach for exploratory search over rich entity-relationship data that utilizes a unique combination of expressive, yet intuitive, query language, faceted search, and graph navigation. We describe an extended faceted search solution which allows to index, search, and browse rich entity-relationship data. We report experimental results of an evaluation study, using a benchmark of several of entity-relationship datasets, demonstrating that our exploratory approach is both effective and efficient compared to other existing approaches.
The effect of links on networked user engagement
Authors: Elad Yom-Tov, Mounia Lalmas, Georges Dupret, Ricardo Baeza-Yates, Pinar Donmez, Janette Lehmann
Venue: WWW
Year: 2012

In the online world, user engagement refers to the phenomena associated with being captivated by a web application and wanting to use it longer and frequently. Nowadays, many providers operate multiple content sites, very different from each other. Due to their extremely varied content, these are usually studied and optimized separately. However, user engagement should be examined not only within individual sites, but also across sites, that is the entire content provider network. In previous work, we investigated networked user engagement, by defining a global measure of engagement that captures the effect that sites have on the engagement on other sites within the same browsing session. Here, we look at the effect of links on networked user engagement, as these are commonly used by online content providers to increase user engagement.
Investigating bias in traditional media through social media
Authors: Arjumand Younus, Muhammad Atif Qureshi, Suneel Kumar Kingrani, Muhammad Saeed, Nasir Touheed, Colm O&apos, Riordan, Gabriella Pasi
Venue: WWW
Year: 2012

It is often the case that traditional media provide coverage of a news event on the basis of journalists' viewpoints -- a problem termed in the literature as media bias. On the other hand social media have given birth to an alternative paradigm of journalism known as "citizen journalism". We take advantage of citizen journalism to detect the bias in traditional media and propose a simple model for empirical measurement of media bias.
Filtering and ranking schemes for finding inclusion dependencies on the web
Authors: Erika Yumiya, Atsuyuki Morishima, Masami Takahashi, Shigeo Sugimoto, Hiroyuki Kitagawa
Venue: WWW
Year: 2012

This paper addresses the problem of finding inclusion dependencies on the Web. In our approach, we enumerate pairs of HTML/XML elements that possibly represent inclusion dependencies and then rank the results for verification. This paper focuses on the challenges in the finding and ranking processes.
Contextual trace-based video recommendations
Authors: Raafat Zarka, Amélie Cordier, Elöd Egyed-Zsigmond, Alain Mille
Venue: WWW
Year: 2012

People like creating their own videos by mixing various contents. Many applications allow us to generate video clips by merging different media like videos clips, photos, text and sounds. Some of these applications enable us to combine online content with our own resources. Given the large amount of content available, the problem is to quickly find content that truly meet our needs. This is when recommender systems come in. In this paper, we propose an approach for contextual video recommendations based on a Trace-Based Reasoning approach.
User interests driven web personalization based on multiple social networks
Authors: Yi Zeng, Ning Zhong, Xu Ren, Yan Wang
Venue: WWW
Year: 2012

User related data indicate user interests in a certain environment. In the context of massive data from the Web, if an application wants to provide more personalized service (e.g. search) for users, an investigation on user interests is needed. User interests are usually distributed in different sources. In order to provide a more comprehensive understanding, user related data from multiple sources need to be integrated together for deeper analysis. Web based social networks have become typical platforms for extracting user interests. In addition, there are various types of interests from these social networks. In this paper, we provide an algorithmic framework for retrieving semantic data based on user interests from multiple sources (such as multiple social networking sites). We design several algorithms to deal with interests based retrieval based on single and multiple types of interests. We utilize publication data from Semantic Web Dog Food (which can be considered as an academic collaboration based social network), and microblogging data from Twitter to validate our framework. The Active Academic Visit Recommendation Application (AAVRA) is developed as a concrete usecase to show the potential effectiveness of the proposed framework for user interests driven Web personalization based on multiple social networks.
Community: issues, definitions, and operationalization on the web
Authors: Guo Zhang, Elin K. Jacob
Venue: WWW
Year: 2012

This paper addresses the concepts of community and online community and discusses the physical, functional, and symbolic characteristics of a community that have formed the basis for traditional definitions. It applies a four-dimensional perspective of space and place (i.e., shape, structure, context, and experience) as a framework for refining the definition of traditional offline communities and for developing a definition of online communities that can be effectively operationalized. The methods and quantitative measures of social network analysis are proposed as appropriate tools for investigating the nature and function of communities because they can be used to quantify the typically subjective social phenomena generally associated with communities.
Mining photo-sharing websites to study ecological phenomena
Authors: Haipeng Zhang, Mohammed Korayem, David J. Crandall, Gretchen LeBuhn
Venue: WWW
Year: 2012

The popularity of social media websites like Flickr and Twitter has created enormous collections of user-generated content online. Latent in these content collections are observations of the world: each photo is a visual snapshot of what the world looked like at a particular point in time and space, for example, while each tweet is a textual expression of the state of a person and his or her environment. Aggregating these observations across millions of social sharing users could lead to new techniques for large-scale monitoring of the state of the world and how it is changing over time. In this paper we step towards that goal, showing that by analyzing the tags and image features of geo-tagged, time-stamped photos we can measure and quantify the occurrence of ecological phenomena including ground snow cover, snow fall and vegetation density. We compare several techniques for dealing with the large degree of noise in the dataset, and show how machine learning can be used to reduce errors caused by misleading tags and ambiguous visual content. We evaluate the accuracy of these techniques by comparing to ground truth data collected both by surface stations and by Earth-observing satellites. Besides the immediate application to ecology, our study gives insight into how to accurately crowd-source other types of information from large, noisy social sharing datasets.