81,907 research outputs found
Semantic Variation in Online Communities of Practice
We introduce a framework for quantifying semantic variation of common words
in Communities of Practice and in sets of topic-related communities. We show
that while some meaning shifts are shared across related communities, others
are community-specific, and therefore independent from the discussed topic. We
propose such findings as evidence in favour of sociolinguistic theories of
socially-driven semantic variation. Results are evaluated using an independent
language modelling task. Furthermore, we investigate extralinguistic features
and show that factors such as prominence and dissemination of words are related
to semantic variation.Comment: 13 pages, Proceedings of the 12th International Conference on
Computational Semantics (IWCS 2017
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
The topology of a discussion: the #occupy case
We analyse a large sample of the Twitter activity developed around the social
movement 'Occupy Wall Street' to study the complex interactions between the
human communication activity and the semantic content of a discussion. We use a
network approach based on the analysis of the bipartite graph @Users-#Hashtags
and of its projections: the 'semantic network', whose nodes are hashtags, and
the 'users interest network', whose nodes are users In the first instance, we
find out that discussion topics (#hashtags) present a high heterogeneity, with
the distinct role of the communication hubs where most the 'opinion traffic'
passes through. In the second case, the self-organization process of users
activity leads to the emergence of two classes of communicators: the
'professionals' and the 'amateurs'. Moreover the network presents a strong
community structure, based on the differentiation of the semantic topics, and a
high level of structural robustness when a certain set of topics are censored
and/or accounts are removed. Analysing the characteristics the @Users-#Hashtags
network we can distinguish three phases of the discussion about the movement.
Each phase corresponds to specific moment of the movement: from declaration of
intent, organisation and development and the final phase of political
reactions. Each phase is characterised by the presence of specific #hashtags in
the discussion. Keywords: Twitter, Network analysisComment: 13 pages, 9 figure
Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction
The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
Because word semantics can substantially change across communities and
contexts, capturing domain-specific word semantics is an important challenge.
Here, we propose SEMAXIS, a simple yet powerful framework to characterize word
semantics using many semantic axes in word- vector spaces beyond sentiment. We
demonstrate that SEMAXIS can capture nuanced semantic representations in
multiple online communities. We also show that, when the sentiment axis is
examined, SEMAXIS outperforms the state-of-the-art approaches in building
domain-specific sentiment lexicons.Comment: Accepted in ACL 2018 as a full pape
Rexplore: unveiling the dynamics of scholarly data
Rexplore is a novel system that integrates semantic technologies, data mining techniques, and visual analytics to provide an innovative environment for making sense of scholarly data. Its functionalities include: i) a variety of views to make sense of important trends in research; ii) a novel semantic approach for characterising research topics; iii) a very fine-grained expert search with detailed multi-dimensional parameters; iv) an innovative graph view to relate a variety of academic entities; iv) the ability to detect and explore the main communities within a research topic; v) the ability to analyse research performance at different levels of abstraction, including individual researchers, organizations, countries, and research communities
Interests Diffusion in Social Networks
Understanding cultural phenomena on Social Networks (SNs) and exploiting the
implicit knowledge about their members is attracting the interest of different
research communities both from the academic and the business side. The
community of complexity science is devoting significant efforts to define laws,
models, and theories, which, based on acquired knowledge, are able to predict
future observations (e.g. success of a product). In the mean time, the semantic
web community aims at engineering a new generation of advanced services by
defining constructs, models and methods, adding a semantic layer to SNs. In
this context, a leapfrog is expected to come from a hybrid approach merging the
disciplines above. Along this line, this work focuses on the propagation of
individual interests in social networks. The proposed framework consists of the
following main components: a method to gather information about the members of
the social networks; methods to perform some semantic analysis of the Domain of
Interest; a procedure to infer members' interests; and an interests evolution
theory to predict how the interests propagate in the network. As a result, one
achieves an analytic tool to measure individual features, such as members'
susceptibilities and authorities. Although the approach applies to any type of
social network, here it is has been tested against the computer science
research community.
The DBLP (Digital Bibliography and Library Project) database has been elected
as test-case since it provides the most comprehensive list of scientific
production in this field.Comment: 30 pages 13 figs 4 table
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