9,802 research outputs found
Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies
Combining Clustering techniques and Formal Concept Analysis to characterize Interestingness Measures
Formal Concept Analysis "FCA" is a data analysis method which enables to
discover hidden knowledge existing in data. A kind of hidden knowledge
extracted from data is association rules. Different quality measures were
reported in the literature to extract only relevant association rules. Given a
dataset, the choice of a good quality measure remains a challenging task for a
user. Given a quality measures evaluation matrix according to semantic
properties, this paper describes how FCA can highlight quality measures with
similar behavior in order to help the user during his choice. The aim of this
article is the discovery of Interestingness Measures "IM" clusters, able to
validate those found due to the hierarchical and partitioning clustering
methods "AHC" and "k-means". Then, based on the theoretical study of sixty one
interestingness measures according to nineteen properties, proposed in a recent
study, "FCA" describes several groups of measures.Comment: 13 pages, 2 figure
Machine-assisted Cyber Threat Analysis using Conceptual Knowledge Discovery
Over the last years, computer networks have evolved into highly dynamic and interconnected environments, involving multiple heterogeneous devices and providing a myriad of services on top of them. This complex landscape has made it extremely difficult for security administrators to keep accurate and be effective in protecting their systems against cyber threats. In this paper, we describe our vision and scientific posture on how artificial intelligence techniques and a smart use of security knowledge may assist system administrators in better defending their networks. To that end, we put forward a research roadmap involving three complimentary axes, namely, (I) the use of FCA-based mechanisms for managing configuration vulnerabilities, (II) the exploitation of knowledge representation techniques for automated security reasoning, and (III) the design of a cyber threat intelligence mechanism as a CKDD process. Then, we describe a machine-assisted process for cyber threat analysis which provides a holistic perspective of how these three research axes are integrated together
Time Aware Knowledge Extraction for Microblog Summarization on Twitter
Microblogging services like Twitter and Facebook collect millions of user
generated content every moment about trending news, occurring events, and so
on. Nevertheless, it is really a nightmare to find information of interest
through the huge amount of available posts that are often noise and redundant.
In general, social media analytics services have caught increasing attention
from both side research and industry. Specifically, the dynamic context of
microblogging requires to manage not only meaning of information but also the
evolution of knowledge over the timeline. This work defines Time Aware
Knowledge Extraction (briefly TAKE) methodology that relies on temporal
extension of Fuzzy Formal Concept Analysis. In particular, a microblog
summarization algorithm has been defined filtering the concepts organized by
TAKE in a time-dependent hierarchy. The algorithm addresses topic-based
summarization on Twitter. Besides considering the timing of the concepts,
another distinguish feature of the proposed microblog summarization framework
is the possibility to have more or less detailed summary, according to the
user's needs, with good levels of quality and completeness as highlighted in
the experimental results.Comment: 33 pages, 10 figure
Cost-of-illness of rheumatoid arthritis and ankylosing spondylitis
OBJECTIVES:\ud
To assess, quantify and summarise the cost of illness of rheumatoid arthritis (RA) and ankylosing spondylitis (AS) from the societal perspective.\ud
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METHODS:\ud
Original studies reporting costs of RA or AS were searched systematically. Both cost-of-illness studies and economic evaluations of therapies were included. Studies were appraised for patient and study characteristics, type of costs and actual costs. Reported costs were aggregated by cost categories and overall mean costs were summarised by cost domain (healthcare, patient and family, and productivity costs).\ud
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RESULTS:\ud
Overall mean costs of RA (€14,906 per year) were above that of AS (€9,374 per year), while the relative distribution of costs over cost domains was approximately similar. For both diseases, productivity costs based on the human cost approach were 3 to 10 times higher than the friction costs and accounted for more than half the total costs of both diseases.\ud
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CONCLUSIONS:\ud
Productivity costs constitute the largest part of the total cost-off-illness of RA and AS reflecting the high burden of the disease on work participation. Although total and direct costs of illness in RA were higher than in AS, the average age of AS patients was 10 years lower and therefore, lifetime costs associated with AS may actually be equal or higher
Recovering Architectural Variability of a Family of Product Variants
A Software Product Line (SPL) aims at applying a pre-planned systematic reuse
of large-grained software artifacts to increase the software productivity and
reduce the development cost. The idea of SPL is to analyze the business domain
of a family of products to identify the common and the variable parts between
the products. However, it is common for companies to develop, in an ad-hoc
manner (e.g. clone and own), a set of products that share common
functionalities and differ in terms of others. Thus, many recent research
contributions are proposed to re-engineer existing product variants to a SPL.
Nevertheless, these contributions are mostly focused on managing the
variability at the requirement level. Very few contributions address the
variability at the architectural level despite its major importance. Starting
from this observation, we propose, in this paper, an approach to reverse
engineer the architecture of a set of product variants. Our goal is to identify
the variability and dependencies among architectural-element variants at the
architectural level. Our work relies on Formal Concept Analysis (FCA) to
analyze the variability. To validate the proposed approach, we experimented on
two families of open-source product variants; Mobile Media and Health Watcher.
The results show that our approach is able to identify the architectural
variability and the dependencies
Revisiting Numerical Pattern Mining with Formal Concept Analysis
In this paper, we investigate the problem of mining numerical data in the
framework of Formal Concept Analysis. The usual way is to use a scaling
procedure --transforming numerical attributes into binary ones-- leading either
to a loss of information or of efficiency, in particular w.r.t. the volume of
extracted patterns. By contrast, we propose to directly work on numerical data
in a more precise and efficient way, and we prove it. For that, the notions of
closed patterns, generators and equivalent classes are revisited in the
numerical context. Moreover, two original algorithms are proposed and used in
an evaluation involving real-world data, showing the predominance of the
present approach
Behavioural economics in competition policy enforcement for financial product markets
Behavioural Economics (BE) acknowledges that individuals often make choices that are not entirely rational. The UK Competition and Markets Authority and Financial Conduct Authority have recently highlighted the relevance of BE. This article explains the difference it makes to the economic analysis of competition and why it is seen as particularly relevant to financial product markets. BE is already being used to frame and test theories of harm. It also brings experimental techniques to the analytical toolkit. The current approach is illustrated with examples from recent and ongoing cases. Finally, the risks of over-intervention and unintended harm from inappropriate remedies are highlighted
Characterizing the Landscape of Musical Data on the Web: State of the Art and Challenges
Musical data can be analysed, combined, transformed and exploited for diverse purposes. However, despite the proliferation of digital libraries and repositories for music, infrastructures and tools, such uses of musical data remain scarce. As an initial step to help fill this gap, we present a survey of the landscape of musical data on the Web, available as a Linked Open Dataset: the musoW dataset of catalogued musical resources. We present the dataset and the methodology and criteria for its creation and assessment. We map the identified dimensions and parameters to existing Linked Data vocabularies, present insights gained from SPARQL queries, and identify significant relations between resource features. We present a thematic analysis of the original research questions associated with surveyed resources and identify the extent to which the collected resources are Linked Data-ready
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