OntoCS: A Web-Based System for Collaborative Ontology Construction

A number of studies on ontology editing tools and ontology-based applications have been proposed for automatically processing knowledge and information. However, the existing methodologies and tools for dealing with ontologies have assumed that the system is restricted to a single user. Main motivation of this paper is to foster collaborations between users, because ontology building is an expensive task. Thereby, in this paper, we present a web-based ontology construction and integration system, which is called OntoCS, to support collaborative interactions between people during creating ontologies. Particularly, inexpert users can collect available language resources from the web to describe concepts in a (even unfamiliar) domain. We believe that this collaborative process is implementing collective intelligence. In conclusion, we have shown that the proposed OntoCS system can efficiently edit and manage multiple ontologies over time

Social Data Visualization System for Understanding Diffusion Patterns on Twitter: A Case Study on Korean Enterprises

Online social media have been playing an important role of creating and diffusing information to many users. It means the users can get cognitive influence to the other users. Thus, it is important to understand how the information can be diffused by interactions among users through online social media. In this paper, we design a social media monitoring system (called "TweetPulse'') which can analyze and show meaningful diffusion patterns (DP) among the users. Particularly, TweetPulse focuses on visualizing information diffusion in Twitter, given a certain time duration. Also, this work has investigated the relationships 1) between DP and event detecting, 2) between DP and emotional words, and 3) between DP and the number of followers of the users. Thereby, to understand the continuous patterns of the information diffusion, we propose two different types of analytic methods, which are 1) macroscopic approach and 2) microscopic approach. For evaluating the proposed method, we have collected and preprocessed the dataset during about 4 months (14 March 2012 to 12 July 2012). As a conclusion, TweetPulse has helped users to easily understand DP from a large scale dataset streaming through Twitter

Improving Efficiency of Incremental Mining by Trie Structure and Pre-Large Itemsets

Incremental data mining has been discussed widely in recent years, as it has many practical applications, and various incremental mining algorithms have been proposed. Hong et al. proposed an efficient incremental mining algorithm for handling newly inserted transactions by using the concept of pre-large itemsets. The algorithm aimed to reduce the need to rescan the original database and also cut maintenance costs. Recently, Lin et al. proposed the Pre-FUFP algorithm to handle new transactions more efficiently, and make it easier to update the FP-tree. However, frequent itemsets must be mined from the FP-growth algorithm. In this paper, we propose a Pre-FUT algorithm (Fast-Update algorithm using the Trie data structure and the concept of pre-large itemsets), which not only builds and updates the trie structure when new transactions are inserted, but also mines all the frequent itemsets easily from the tree. Experimental results show the good performance of the proposed algorithm

TwiSNER: Semi-supervised Method for Named Entity Recognition from Text Streams on Twitter

The data on Social Network Services (SNSs) has recently become an interesting source for researchers conducting different Natural Language Processing (NLP) experiments, such as sentiment analysis, information extraction, Named Entity Recognition (NER), and so on. The characteristics of SNS data are usually described as short, noisy, with insufficient supplemental information. They often contain grammatical errors, misspellings, and unreliable capitalization. Thus, standard NLP tools (e.g., NER systems) have difficulty obtaining good results when they are applied on these data, even if they perform well on well-formatted texts. Most of the traditional NER methods are based on supervised learning techniques that often require a large amount of standard training data to train a classifier. In this paper, we propose a method called TwiSNER to classify named entities in Twitter data (called tweets) by using a semi-supervised learning approach combined with the conditional random field model, hand-made rules, and the co-occurrence coefficient of the featured words surrounding entities. In the experiments, TwiSNER is applied on a dataset collected from Twitter, which includes 11,425 tweets for training with 4,716 labeled tweets and 1,450 tweets for testing. TwiSNER produces promising results, where the best F-measure is better than the baselines

Beating Social Pulse: Understanding Information Propagation via Online Social Tagging Systems

Social media (e.g., Twitter and FaceBook) have been one of the most popular online communication channels to share information among users. It means the users can give (and have) cognitive influences to (and from) the others. Thus, it is important for many online collaborative applications to understand how the information can be propagated via such social media. In this paper, we focus on a social tagging system where users can easily exchange resources as well as their tags with other users. Given a certain tag from a temporal folksonomy, the social pulse can be established by counting the number of users (or resources). Particularly, we can discover meaningful relationship between tags by computing inducibility. To conduct experimentation, a tag search system has been implemented to collect a dataset from Flickr

Computational Intelligence Tools for Processing Collective Data

Computational Intelligence Tools for Processing Collective Dat

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Similarity-based Complex Publication Network Analytics for Recommending Potential Collaborations

As communities of researchers continue to become quite large and to grow incessantly, collaboration among researchers can be conducive to greater research productivity. Nevertheless, it is difficult for a researcher to find suitable collaborators from all researchers around the world. In this paper, we have used bibliographic DBLP data to extract information of a researcher and to discover the relationship between the co-authors and between authors and conferences. We evaluated some of the similarity measures and developed an innovative random walk model to find potential co-authors for a given researcher. These measures were then used to design a best model to recommend co-authors. We have also applied an HITS algorithm and proposed a ranking algorithm to rank researchers and conferences with the intent of recommending authors or conferences

Retracted: Semantic Information Integration with Linked Data Mashups Approaches

The introduction of semantic web and Linked Data helps facilitate sharing of data on the Internet more easily. Subsequently, the resource description framework (RDF) is the standard in publishing structured data resources on the Internet and is used in interconnecting with other data resources. To remedy the data integration issues of the traditional web mashups, the semantic web technology uses the Linked Data based on RDF data model as the unified data model for combining, aggregating, and transforming data from heterogeneous data resources to build Linked Data mashups. There have been tremendous amounts of efforts of semantic web community to enable Linked Data mashups but there is still lack of a systematic survey on concepts, technologies, applications, and challenges. Therefore, in this paper, we investigate in detail semantic mashups research and application approaches in the information integration. This paper also presents a Linked Data mashup application as an illustration of the proposed approaches

A Semantic Wiki Framework for Reconciling Conflict Collaborations Based on Selecting Consensus Choice

Semantic wikis have been regarded as an important collaboration tool among a number of experts from multiple domains. This wiki platform can play a role of collaborative knowledge management system which can provide an efficient framework to raise social interactions between remote people synchronously. However, as these semantic wiki systems allow users to exploit their own semantics and backgrounds for describing their knowledge and skills, there are often semantic conflicts between knowledge (or information) published and provided by the users. Thereby, the main aims of this work are i) to automatically detect such conflicts by keeping track on the user semantics, and ii) to reasonably select consensus choice by analyzing social collaborations. In this paper, we want to note major patterns of knowledge dynamics through the social interactions on semantic wikis, and the semantic conflicts caused by the knowledge dynamics. The consensus choice has been effectively selected to be recommended for better understandability about the knowledge conflicts