Web Intelligence - Volume 14, issue 1

Authors: Lim, Kwan Hui | Datta, Amitava

Article Type: Research Article

Abstract: The immense popularity and rapid growth of Online Social Networks (OSN) have attracted the interest of researchers and companies, particularly in how users group together to form communities online. While many community detection algorithms have been developed to detect communities on such OSNs, most of these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing, which require the community to be …highly active so as to facilitate the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. Our experimental results show that communities detected by our proposed approach are more cohesive and connected within different interest groups, based on topological measures. We also show that the detected communities actively interact about the specific interests, based on the high frequency of #hashtags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users, and observe that our approach detects communities comprising users whose links are more persistent compared to those in other groups of users. Show more

Keywords: Twitter, tweets, social network analysis, community detection, like-minded communities, interaction links, common interests

DOI: 10.3233/WEB-160328

Citation: Web Intelligence, vol. 14, no. 1, pp. 1-15, 2016

Authors: Subercaze, Julien | Gravier, Christophe | Laforest, Frédérique

Article Type: Research Article

Abstract: Real-time recommendation of Twitter users based on the content of their profiles is a very challenging task. Traditional IR methods such as TF-IDF fail to handle efficiently large datasets. In this paper we present a scalable approach that allows real time recommendation of users based on their tweets. Our model builds a graph of terms, driven by the fact that users sharing similar interests will share similar terms. We show how this model can be encoded as a compact binary footprint, that allows very fast comparison and ranking, taking full advantage of modern CPU architectures. We validate our approach through …an empirical evaluation against the Apache Lucene’s implementation of TF-IDF. We show that our approach is in average two hundred times faster than standard optimized implementation of TF-IDF with a precision of 58%. Show more

Keywords: Twitter recommendation, binary footprint, large scale approach, information retrieval, real-time recommendation

DOI: 10.3233/WEB-160329

Citation: Web Intelligence, vol. 14, no. 1, pp. 17-29, 2016

Authors: Fukazawa, Yusuke | Kröll, Mark | Strohmaier, Markus | Ota, Jun

Article Type: Research Article

Abstract: Task-models concretize general requests to support users in real-world scenarios. In this paper, we present an IR based algorithm (IRTML) to automate the construction of hierarchically structured task-models. In contrast to other approaches, our algorithm is capable of assigning general tasks closer to the top and specific tasks closer to the bottom. Connections between tasks are established by extending Turney’s PMI-IR measure. To evaluate our algorithm, we manually created a ground truth in the health-care domain consisting of 14 domains. We compared the IRTML algorithm to three state-of-the-art algorithms to generate hierarchical structures, i.e. BiSection K-means, Formal Concept Analysis and …Bottom-Up Clustering. Our results show that IRTML achieves a 25.9% taxonomic overlap with the ground truth, a 32.0% improvement over the compared algorithms. Show more

Keywords: Human task, task model, taxonomic structure, pointwise mutual information, health care domain

DOI: 10.3233/WEB-160330

Citation: Web Intelligence, vol. 14, no. 1, pp. 31-41, 2016

Authors: Dunin-Kęplicz, Barbara | Strachocka, Alina

Article Type: Research Article

Abstract: Various types of everyday arguments are represented as argumentation schemes , originating from the Legal Argumentation literature. The recent achievements in this domain can be applied to multi-agent settings to enrich the paradigmatic aspects of communication and reasoning. Agents typically populate complex environments where incompleteness and inconsistency of information is rather a rule than exception. Although the problem how to tackle inconsistencies is already present in argumentation, a paraconsistent (that is, tolerating inconsistency) approach is still missing from the literature. The contribution of this research is a computationally-friendly framework for formalizing paraconsistent argumentation schemes . This is achieved by …extending agent’s reasoning capabilities with non-deductive methods rooted in argumentation. To this end we provide a generic paraconsistent program template for implementation of various argumentation schemes. Our methodology is strongly influenced by ideas underlying 4QL: a four-valued, rule-based, Datalog ¬¬ -like query language. Thanks to its properties, the tractability of the solution (so hardly obtainable in logical modeling) has been reached. The paper concludes with examples of several paraconsistent argumentation schemes implemented in 4QL. Show more

Keywords: Multi-agent systems, communication, nonmonotonic reasoning, paraconsistent argumentation schemes

DOI: 10.3233/WEB-160331

Citation: Web Intelligence, vol. 14, no. 1, pp. 43-65, 2016

Authors: Dutta, Ayan | Dasgupta, Prithviraj | Nelson, Carl

Article Type: Research Article

Abstract: We consider the problem of partitioning a set of modules for efficient and dynamic shape or configuration generation in modular robotic systems, where the size of any configuration formed by the modules is constrained by a maximum, user-provided value denoted by n max . The objective is to determine the best partition of the module set that gives the highest utility to the modules. This problem is non-trivial as the set of partitions that needs to be explored grows exponentially with the number of modules and an exhaustive search in the space of partitions makes …the problem intractable. To address this problem, we propose a branch and bound based search algorithm, called bottomUpCSGSearch , which is able to intelligently prune the unpromising search space and find the best partition of the modules in a reasonable amount of time. We have provided analytical results related to the completeness, anytime nature and time complexity of our proposed algorithm. Our experimental results show that our proposed algorithm performs better in terms of number of nodes explored (up to 2 8 times fewer nodes explored) and the time required to find the best partition (improvement up to the order of 10 4   ms ) than existing comparable algorithms. We have simulated with different number of modules and different values of maximum coalition size of a modular self-reconfigurable robot (MSR) called ModRED, and, shown that our algorithm takes nominal time to find the best solution. Show more

Keywords: Modular robots, configuration generation, coalition structure formation, algorithm

DOI: 10.3233/WEB-160332

Citation: Web Intelligence, vol. 14, no. 1, pp. 67-82, 2016