1,721,130 research outputs found
Conversational Data Exploration
Full text linkThis paper presents a framework for the design of chatbots for data exploration. With respect to conversational virtual assistants (such as Amazon Alexa or Apple Siri), this class of chatbots exploits structured input to retrieve data from known data sources. The approach is based on a conceptual representation of the available data sources, and on a set of modeling abstractions that allow designers to characterize the role that key data elements play in the user requests to be handled. Starting from the resulting specifications, the framework then generates a conversation for exploring the content exposed by the considered data sources
Conversation Graphs in Online Social Media
Full text linkIn online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication
Designing and developing context-aware mobile mashups: The CAMUS approach
No full textCAMUS (Context-Aware Mobile mashUpS) is a framework for the design of mobile applications that dynamically collect and integrate heterogeneous resources (data sources and services) to offer integrated content and functions to mobile users in a context-aware fashion. CAMUS exploits a set of high-level abstractions for context and mashup modeling that hide the complexity resulting from service selection, invocation and integration. Generative techniques then enable the transformation of models into running code for mobile applications that flexibly respond to actual user needs as they vary in different situations of use
Hera Presentation Generator
No full textSemantic Web Information Systems (SWIS) are Web Information Systems that use Semantic Web technologies. Hera is a modeldriven design methodology for SWIS. In Hera, models are represented in RDFS and model instances in RDF. The Hera Presentation Generator (HPG) is an integrated development environment that supports the presentation generation layer of the Hera methodology. The HPG is based on a pipeline of data transformations driven by different Hera models
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Engineering the Presentation Layer for Semantic Web Information Systems ABSTRACT
No full textThis paper presents a design methodology that deals with the presentation aspects involved in the development of a Semantic Web Information System. The methodology is driven by two main design models: the conceptual model and the application model. The application model extends the conceptual model with presentation abstractions that capture the application logic. During the presentation generation the input data goes through a sequence of transformation steps. Using semantic web technology we chose to represent the models and their instances in RDF(S). The RDF/XML model serialization facilitates the specification of the different transformations in XSLT
A Dependency Graph Isomorphism for News Sentence Searching
Full text linkAbstract. Given that the amount of news being published is only increasing, an effective search tool is invaluable to many Web-based companies. With word-based approaches ignoring much of the information in texts, we propose Destiny, a linguistic approach that leverages the syntactic information in sentences by representing sentences as graphs with disambiguated words as nodes and grammatical relations as edges. Destiny performs approximate sub-graph isomorphism on the query graph and the news sentence graphs, exploiting word synonymy as well as hypernymy. Employing a custom corpus of user-rated queries and sentences, the algorithm is evaluated using the normalized Discounted Cumulative Gain, Spearman's Rho, and Mean Average Precision and it is shown that Destiny performs significantly better than a TF-IDF baseline on the considered measures and corpus
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