312 research outputs found
Semantic technologies: from niche to the mainstream of Web 3? A comprehensive framework for web Information modelling and semantic annotation
Context: Web information technologies developed and applied in the last decade
have considerably changed the way web applications operate and have
revolutionised information management and knowledge discovery. Social
technologies, user-generated classification schemes and formal semantics have a
far-reaching sphere of influence. They promote collective intelligence, support
interoperability, enhance sustainability and instigate innovation.
Contribution: The research carried out and consequent publications follow the
various paradigms of semantic technologies, assess each approach, evaluate its
efficiency, identify the challenges involved and propose a comprehensive framework for web information modelling and semantic annotation, which is the thesis’ original contribution to knowledge. The proposed framework assists web information
modelling, facilitates semantic annotation and information retrieval, enables system interoperability and enhances information quality.
Implications: Semantic technologies coupled with social media and end-user
involvement can instigate innovative influence with wide organisational implications that can benefit a considerable range of industries. The scalable and sustainable business models of social computing and the collective intelligence of organisational social media can be resourcefully paired with internal research and knowledge from interoperable information repositories, back-end databases and legacy systems.
Semantified information assets can free human resources so that they can be used to better serve business development, support innovation and increase productivity
Enterprise 2.0: Collaboration and Knowledge Emergence as a Business Web Strategy Enabler
The Web is becoming in many respects a powerful tool for supporting business strategy as companies are quickly becoming more and more reliant on new Web-based technologies to capitalize on new business opportunities. However, this introduces additional managerial problems and risks that have to be taken into consideration, if they are not to be left behind. In this chapter we explore the Web’s present and future potential in relation to information sharing, knowledge management, innovation management, and the automation of cross-organizational business transactions. The suggested approach will provide entrepreneurs, managers, and IT leaders with guidance on how to adopt the latest Web 2.0-based technologies in their everyday work with a view to setting up a business Web strategy. Specifically, Enterprise 2.0 is presented as a key enabler for businesses to expand their ecosystems and partnerships. Enterprise 2.0 also acts as a catalyst for improving innovation processes and knowledge work
Global Diffusion of the Internet XV: Web 2.0 Technologies, Principles, and Applications: A Conceptual Framework from Technology Push and Demand Pull Perspective
Web 2.0, the current Internet evolution, can be described by several key features of an expanded Web that is more interactive; allows easy social interactions through participation and collaboration from a variety of human sectors; responds more immediately to users\u27 queries and needs; is easier to search; and provides a faster, smoother, realistic and engaging user search capability, often with automatic updates to users. The purpose of this study is three-fold. First, the primary goal is to propose a conceptual Web 2.0 framework that provides better understanding of the Web 2.0 concept by classifying current key components in a holistic manner. Second, using several selective key components from the conceptual framework, this study conducts case analyses of Web 2.0 applications to discuss how they have adopted the selective key features (i.e., participation, collaboration, rich user experience, social networking, semantics, and interactivity responsiveness) of the conceptual Web 2.0 framework. Finally, the study provides insightful discussion of some challenges and opportunities provided by Web 2.0 to education, business, and social life
Guided generation of pedagogical concept maps from the Wikipedia
We propose a new method for guided generation of concept maps from open accessonline knowledge resources such as Wikies. Based on this method we have implemented aprototype extracting semantic relations from sentences surrounding hyperlinks in the Wikipedia’sarticles and letting a learner to create customized learning objects in real-time based oncollaborative recommendations considering her earlier knowledge. Open source modules enablepedagogically motivated exploration in Wiki spaces, corresponding to an intelligent tutoringsystem. The method extracted compact noun–verb–noun phrases, suggested for labeling arcsbetween nodes that were labeled with article titles. On average, 80 percent of these phrases wereuseful while their length was only 20 percent of the length of the original sentences. Experimentsindicate that even simple analysis algorithms can well support user-initiated information retrievaland building intuitive learning objects that follow the learner’s needs.Peer reviewe
Text-based Sentiment Analysis and Music Emotion Recognition
Nowadays, with the expansion of social media, large amounts of user-generated
texts like tweets, blog posts or product reviews are shared online. Sentiment polarity
analysis of such texts has become highly attractive and is utilized in recommender
systems, market predictions, business intelligence and more. We also witness deep
learning techniques becoming top performers on those types of tasks. There are
however several problems that need to be solved for efficient use of deep neural
networks on text mining and text polarity analysis.
First of all, deep neural networks are data hungry. They need to be fed with
datasets that are big in size, cleaned and preprocessed as well as properly labeled.
Second, the modern natural language processing concept of word embeddings as a
dense and distributed text feature representation solves sparsity and dimensionality
problems of the traditional bag-of-words model. Still, there are various uncertainties
regarding the use of word vectors: should they be generated from the same dataset
that is used to train the model or it is better to source them from big and popular
collections that work as generic text feature representations? Third, it is not easy for
practitioners to find a simple and highly effective deep learning setup for various
document lengths and types. Recurrent neural networks are weak with longer texts
and optimal convolution-pooling combinations are not easily conceived. It is thus
convenient to have generic neural network architectures that are effective and can
adapt to various texts, encapsulating much of design complexity.
This thesis addresses the above problems to provide methodological and practical
insights for utilizing neural networks on sentiment analysis of texts and achieving
state of the art results. Regarding the first problem, the effectiveness of various
crowdsourcing alternatives is explored and two medium-sized and emotion-labeled
song datasets are created utilizing social tags. One of the research interests of Telecom
Italia was the exploration of relations between music emotional stimulation and
driving style. Consequently, a context-aware music recommender system that aims
to enhance driving comfort and safety was also designed. To address the second
problem, a series of experiments with large text collections of various contents and
domains were conducted. Word embeddings of different parameters were exercised
and results revealed that their quality is influenced (mostly but not only) by the
size of texts they were created from. When working with small text datasets, it is
thus important to source word features from popular and generic word embedding
collections. Regarding the third problem, a series of experiments involving convolutional
and max-pooling neural layers were conducted. Various patterns relating
text properties and network parameters with optimal classification accuracy were
observed. Combining convolutions of words, bigrams, and trigrams with regional
max-pooling layers in a couple of stacks produced the best results. The derived
architecture achieves competitive performance on sentiment polarity analysis of
movie, business and product reviews.
Given that labeled data are becoming the bottleneck of the current deep learning
systems, a future research direction could be the exploration of various data programming
possibilities for constructing even bigger labeled datasets. Investigation
of feature-level or decision-level ensemble techniques in the context of deep neural
networks could also be fruitful. Different feature types do usually represent complementary
characteristics of data. Combining word embedding and traditional text
features or utilizing recurrent networks on document splits and then aggregating the
predictions could further increase prediction accuracy of such models
- …