Social Annotation Research Articles

Swallow: Resource and Tag Recommender System Based on Heat Diffusion Algorithm in Social Annotation Systems

Social annotation systems (SAS) allow users to annotate different online resources with keywords (tags). These systems help users in finding, organizing, and retrieving online resources to significantly provide collaborative semantic data to be potentially applied by recommender systems. Previous studies on SAS had been worked on tag recommendation. Recently, SAS‐based resource recommendation has received more attention by scholars. In the most of such systems, with respect to annotated tags, searched resources are recommended to user, and their recent behavior and click‐through is not taken into account. In the current study, to be able to design and implement a more precise recommender system, because of previous users' tagging data and users' current click‐through, it was attempted to work on the both resource (such as web pages, research papers, etc.) and tag recommendation problem. Moreover, by applying heat diffusion algorithm during the recommendation process, more diverse options would present to the user. After extracting data, such as users, tags, resources, and relations between them, the recommender system so called “Swallow” creates a graph‐based pattern from system log files. Eventually, following the active user path and observing heat conduction on the created pattern, user further goals are anticipated and recommended to him. Test results on SAS data set demonstrate that the proposed algorithm has improved the accuracy of former recommendation algorithms.

Learning Context-Sensitive Domain Ontologies from Folksonomies: A Cognitively Motivated Method

Ontology is the backbone of the Semantic Web, helping users search for relevant resources from the Web of linked data. The existing context-free mapping approach between tags and concepts fails to address the problems of social synonymy and social polysemy when ontologies are induced from folksonomies. The novel contributions of this paper are threefold. First, grounded in the cognitively motivated category utility measure, a novel basic-level concept mining algorithm is developed to construct semantically rich concept vectors to alleviate the problem of social synonymy. Second, contextual aspects of ontology learning are exploited via probabilistic topic modeling to address the problem of social polysemy. Third, a novel context-sensitive domain ontology learning algorithm that combines link- and content-based semantic analysis is developed to identify both taxonomic and associative relations among concepts. To the best of our knowledge, this is the first successful research that exploits a cognitively motivated method to learn context-sensitive domain ontologies from folksonomies. By using the Open Directory Project ontology as a benchmark, we examined the effectiveness of the proposed algorithms based on social annotations crawled from three different folksonomy sites. Our experimental results show that the proposed ontology learning system significantly outperforms the best baseline system by 13.83% in terms of taxonomic F-measure. The practical implication of our research is that high-quality ontologies are constructed with minimal human intervention to facilitate concept-driven retrieval of linked data and the knowledge-based interoperability among enterprises.

Mining Various Semantic Relationships from Unstructured User-Generated Web Data

With the emergence of Web 2.0, the amount of user-generated web data has sharply increased. Thus, many studies have proposed techniques to extract wisdom from these user-generated datasets. Some of these works have focused on extracting semantic relationships through the use of search logs or social annotations, but only hierarchical relationships have been considered. The goal of this paper is to detect various semantic relationships (hierarchical and non-hierarchical) between concepts using search logs and social annotations. The experimental results demonstrate that our proposed approach constructs adequate relationships.

Mining various semantic relationships from unstructured user-generated web data

With the emergence of Web 2.0, the amount of user-generated web data has sharply increased. Thus, many studies have proposed techniques to extract wisdom from these user-generated datasets. Some of these works have focused on extracting semantic relationships through the use of search logs or social annotations, but only hierarchical relationships have been considered. The goal of this paper is to detect various semantic relationships (hierarchical and non-hierarchical) between concepts using search logs and social annotations. The experimental results demonstrate that our proposed approach constructs adequate relationships.

Semantic grounding of social annotations for enhancing resource classification in folksonomies

User-generated annotations in tagging or bookmarking sites such as Flickr or Delicious can provide a promising and interesting source of information for aiding tasks such as Web resource classification. However, the use of tags brings up some challenges. Since there are no constraints on the terms that can be used for tagging, noise and ambiguity are introduced when users annotate resources. Moreover, traditional bag-of-words representations ignore connections between terms and, thus, are affected by synonymity and hyponymia. Althougth tag-based representations are a valuable source for classifying resources, the problems associated with the unsupervised nature of tags may hinder classification results. This paper presents an approach for semantically analysing social annotations in order to attain enriched concept-based representations of Web resources. Representations are enriched with concepts extracted from WordNet and Wikipedia to overcome problems caused by natural language as well as enhancing the quality of information available for performing an effective classification of resources. Several strategies for tag pre-processing, concept disambiguation and incorporation of semantic entities to representations are discussed and evaluated in this paper. Experimental results showed that the strategies proposed to associate tags with conceptual entities allow improving resource classification results, outperforming traditional approaches based on bag-of-words representations.

Opening the Early Modern ToolBox: The Digital Interleaf and Digital Commonplace Book

This paper considers how early modern note-taking practices can inform the design of digital reading environments. In particular, it argues that proximate, handwritten note-taking is essential for both memory retention and archiving, and that digital readers should work within structures that allow for such practices. The Digital Interleaf, the first of two conceptual prototypes introduced, offers one response to that need: a multi-layered page designed for individual and social annotation. The Digital Commonplace Book, the second of the prototypes discussed, provides a method for indexing notes from the Digital Interleaf. These two interoperable concepts are the first in a suite called the Early Modern Toolbox.

Social video annotation by combining features with a tri-adaptation approach

Online social video websites such as YouTube allow users to manually annotate their video documents with textual labels. These labels can be used as indexing keywords to facilitate search and organization of video data. However, manual video annotation is usually a labor-intensive and time-consuming process. In this work, we propose a novel social video annotation approach that combines multiple feature sets based on a tri-adaptation approach. For the shots in each video, they are annotated by aggregating models that are learned from three complementary feature sets. Meanwhile, the models are collaboratively adapted by exploring unlabeled shots. In this sense, the method can be viewed as a novel semi-supervised algorithm that explores three complementary views. Our approach also exploits the temporal smoothness of video labels by applying a label correction strategy. Experiments on a web video dataset demonstrate the effectiveness of the proposed approach.

A delineation of the cognitive processes manifested in a social annotation environment

AbstractThis study aims to examine how students' learning trajectories progress in an online social annotation environment, and how their cognitive processes and levels of interaction correlate with their learning outcomes. Three different types of activities (cognitive, metacognitive and social) were identified in the online environment. The time series of different levels of cognitive and metacognitive activities correlate with one another, indicating that a kind of symbiotic or ecological relationship exists among the cognitive and metacognitive processes. The multiple regression results show that low‐cognitive level activities mediate the effect of high‐cognitive level activities on student's performance in the group inquiry learning project. The results of multilevel analysis indicate that the average number of highlighted texts posted per person in a group accounted for over 50% of the variance of the group inquiry learning project scores. Further, the results also indicate that the level of collaboration within a group explains over 70% of the variance of the high‐level cognitive and metacognitive activities across different groups. In sum, group collaboration is found to be conducive to fostering high‐level cognitive and metacognitive activities.

A demonstration of socrates: Streamlining the collection, analysis and exploration of social media data

While the diversity in social media platforms is a primary motivation for people to participate in different social media services and utilize them in appropriately different manners, the heterogeneity in formats and platforms can also encumber information gathering and investigation, especially for a researcher who might have to resort to time consuming and rudimentary cut-and-paste tasks. In this demonstration we introduce the Social and Crowdsourced Activity Extraction System (SOCRATES), a tool developed to help researchers utilize a single, modularly architected, open-sourced, and standardized platform for collecting social media data from application programming interfaces (APIs), analyzing it with a variety of methodologies, and exploring it via multiple visualizations. SOCRATES has been developed as a highly usable social-computational platform that allows researchers from any background to track, capture, visualize, explore, and analyze large or small amounts of social media data and annotations.

An Efficient Method for Noisy Annotation Data Modeling

Probabilistic topic models are used for analyzing and extracting content-related annotations from noisy annotated discrete data like WebPages on WWW and these WebPages are stored using social bookmarking services with the help of social bookmarking services, reason behind this process most of time users can attach annotations freely, some annotations do not describe the semantics of the content, therefore they are noisy, simply they are not content related. The extraction of content-related annotations can be used as a prepossessing step in machine learning. Prepossessing step in machine learning is like text classification and image recognition, and can improve information retrieval performance. The proposed model is a generative model for content and annotations, where annotations are assumed to be originated either from topics that generated the content or from a general distribution unrelated to the content. We demonstrate the effectiveness of the proposed method with the help of synthetic data and real social annotation data for text and images.

Social semantic query expansion

Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon . The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.

KIPTC: a kernel information propagation tag clustering algorithm

In the social annotation systems, users annotate digital data sources by using tags which are freely chosen textual descriptions. Tags are used to index, annotate and retrieve resource as an additional metadata of resource. Poor retrieval performance remains a major challenge of most social annotation systems resulting from several problems of ambiguity, redundancy and less semantic nature of tags. Clustering is a useful tool to handle these problems in social annotation systems. In this paper, we propose a novel tag clustering algorithm based on kernel information propagation. This approach makes use of the kernel density estimation of the kNN neighborhood directed graph as a start to reveal the prestige rank of tags in tagging data. The random walk with restart algorithm is then employed to determine the center points of tag clusters. The main strength of the proposed approach is the capability of partitioning tags from the perspective of tag prestige rank rather than the intuitive similarity calculation itself. Experimental studies on the six real world data sets demonstrate the effectiveness and superiority of the proposed method against other state-of-the-art clustering approaches in terms of various evaluation metrics.

Modeling Noisy Annotated Data with Application to Social Annotation

We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as webpages stored using social bookmarking services. With these services, because users can attach annotations freely, some annotations do not describe the semantics of the content, thus they are noisy, i.e., not content related. The extraction of content-related annotations can be used as a prepossessing step in machine learning tasks such as text classification and image recognition, or can improve information retrieval performance. The proposed model is a generative model for content and annotations, in which the annotations are assumed to originate either from topics that generated the content or from a general distribution unrelated to the content. We demonstrate the effectiveness of the proposed method by using synthetic data and real social annotation data for text and images.

Examining Student Participation in Three Learning Activities Supported by Social Annotation Tools

ocial annotation (SA) allows learners to highlight and comment on Web pages and share annotations with each other online. Despite its potential in promoting collaborative learning, examining how to integrate it into educational settings has not been fully studied. The purpose of the study is to examine student participation in three different SA-based online activities: (1) peer review, (2) annotated discussion, and (3) collaborative reading. Students participated in all three SA-based activities and took a survey at the end reporting the effectiveness of these activities. The analysis of students’ annotations and their survey responses suggested that although participants perceived the use of SA tools in all the three activities to be relatively effective, their levels of participation in the three activities varied greatly. The authors discuss the pros and cons of using SA in each of the activities based on the findings. Suggestions for future use and development of SA tools are also provided.

Text classification model framework based on social annotation quality

Social annotation is a form of folksonomy,which allows Web users to categorize Web resource with text tags freely.It usually implicates fundamental and valuable semantic information of Web resources.Consequently,social annotation is helpful to improve the quality of information retrieval when applied to information retrieval system.This paper investigated and proposed an improved text classification algorithm based on social annotation.Because social annotation is a kind of folksonomy and social tags are usually generated arbitrarily without any control or expertise knowledge,there has been significant variance in the quality of social tags.Under this consideration,the paper firstly proposed a quantitative approach to measure the quality of social tags by utilizing the semantic similarity between Web pages and social tags.After that,the social tags with relatively low quality were filtered out based on the quality measurement and the remained social tags with high quality were applied to extend traditional vector space model.In the extended vector space model,a Web page was represented by a vector in which the components were the words in the Web page and tags tagged to the Web page.At last,the support vector machine algorithm was employed to perform the classification task.The experimental results show that the classification result can be improved after filtering out the social tags with low quality and embedding those high quality social tags into the traditional vector space model.Compared with other classification approaches,the classification result of F1 measurement has increased by 6.2% on average when using the proposed algorithm.

Top-k publish-subscribe for social annotation of news

Social content, such as Twitter updates, often have the quickest first-hand reports of news events, as well as numerous commentaries that are indicative of public view of such events. As such, social updates provide a good complement to professionally written news articles. In this paper we consider the problem of automatically annotating news stories with social updates (tweets), at a news website serving high volume of pageviews. The high rate of both the pageviews (millions to billions a day) and of the incoming tweets (more than 100 millions a day) make real-time indexing of tweets ineffective, as this requires an index that is both queried and updated extremely frequently. The rate of tweet updates makes caching techniques almost unusable since the cache would become stale very quickly.We propose a novel architecture where each story is treated as a subscription for tweets relevant to the story's content, and new algorithms that efficiently match tweets to stories, proactively maintaining the top-k tweets for each story. Such top-k pub-sub consumes only a small fraction of the resource cost of alternative solutions, and can be applicable to other large scale content-based publish-subscribe problems. We demonstrate the effectiveness of our approach on realworld data: a corpus of news stories from Yahoo! News and a log of Twitter updates.

Using group supervision and social annotation systems to support students’ academic writing

In this best practice paper, we present how we have used a Social annotation system (SAS) in a bachelor’s thesis course in media technology to support students’ academic writing. In the paper, we reflect on both technical and social practices with using SAS. Despite limited instructional support and despite the fact that different groups used SAS in different ways, there have been a high completion rate, good quality of the theses and satisfied students. The combination of group supervision and the use of SAS has been successful, especially when taking into consideration that this was the first year we broadly introduced SAS in the bachelor’s thesis course.

A case study of using a social annotation tool to support collaboratively learning

The purpose of the study was to understand student interaction and learning supported by a collaboratively social annotation tool — Diigo. The researcher examined through a case study how students participated and interacted when learning an online text with the social annotation tool — Diigo, and how they perceived their experience. The findings suggested that students participated actively in the collaborative learning activity and were engaged in a variety of behaviors including self-reflection, elaboration, internalization, and showing support. Although students generally had a moderately positive attitude toward using the social annotation tool for collaborative learning, a few problems were identified. In particular, students found it distracting to navigate through a large amount of annotation while reading the text. The study has implications for future research on using or developing social annotation tools for educational purposes.

A probabilistic approach to mining geospatial knowledge from social annotations

Knowledge produced online often comes in the form of free-text labels, known as tags, with which users annotate the content they create, such as photos and videos. Increasingly, such content is also georeferenced, i.e., it is associated with geographic coordinates. The implicit relationships between tags and their locations can tell us much about how people conceptualize places and relations between them. However, extracting such knowledge from social annotations presents many challenges, since annotations are often ambiguous, noisy, uncertain and spatially inhomogeneous. We introduce a probabilistic framework for modeling georeferenced annotations and a method for learning model parameters from data. The framework is flexible and general, and can be used in a variety of applications that mine geospatial knowledge from user generated content. Specifically, we study two problems --- extracting place semantics and predicting locations of photos from tags --- and show that performance of our method is comparable to that of state-of-the-art approaches. Moreover, we show that combining the two problems leads to a better performance on the location prediction task than baseline.

Revolutionary Reading, Evolutionary Toolmaking: (Re)development of Scholarly Reading and Annotation Tools in Response to an Ever Changing Scholarly Climate

As the online scholarly landscape changes, so too must the tools used to traverse it. The Public Knowledge Project (PKP) Reading Tools provides readers a bridge from online scholarly content to a host of contextual information, to a number of discipline-specific search engines and databases, and to other tools. A lot has changed since it was originally released, such as the rise of Google Scholar as the de facto starting point for many novice (and not-so novice) researchers; the blurring line between desktop and web applications; and the increased professional use of social networking tools and websites. Recently, the University of Victoria's Electronic Textual Cultures Lab (ETCL), in cooperation with the PKP, undertook a study to determine the role and value of the existing Reading Tools, particularly in the context of Humanities Computing. The ETCL has also developed a prototype Professional Reading Environment which has been the basis for substantial analysis. Rick Kopak and Chia-Ning Chiang at the University of British Columbia (UBC) have undertaken a broad survey of the online annotation landscape, and have written a proposal for developing an annotation system for PKP software. This paper discusses how, using this research as a base and in cooperation with UBC and the PKP, the ETCL has begun a large-scale redevelopment of the PKP Reading Tools, extending the current toolset to include new social networking and research tools, as well as a robust personal annotation system, making social annotation possible between small groups and the public.