5,399 research outputs found
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
We evaluate a version of the recently-proposed classification system named
Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space
of sequences of generic objects. The ODSE system has been originally presented
as a classification system for patterns represented as labeled graphs. However,
since ODSE is founded on the dissimilarity space representation of the input
data, the classifier can be easily adapted to any input domain where it is
possible to define a meaningful dissimilarity measure. Here we demonstrate the
effectiveness of the ODSE classifier for sequences by considering an
application dealing with the recognition of the solubility degree of the
Escherichia coli proteome. Solubility, or analogously aggregation propensity,
is an important property of protein molecules, which is intimately related to
the mechanisms underlying the chemico-physical process of folding. Each protein
of our dataset is initially associated with a solubility degree and it is
represented as a sequence of symbols, denoting the 20 amino acid residues. The
herein obtained computational results, which we stress that have been achieved
with no context-dependent tuning of the ODSE system, confirm the validity and
generality of the ODSE-based approach for structured data classification.Comment: 10 pages, 49 reference
Combining machine learning and semantic web: A systematic mapping study
In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining Machine Learning components with techniques developed by the Semantic Web community - Semantic Web Machine Learning (SWeML). Due to its rapid growth and impact on several communities in thepast two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this article is a classification system for SWeML Systems that we publish as ontology.</p
Describing and Organizing Semantic Web and Machine Learning Systems in the SWeMLS-KG
In line with the general trend in artificial intelligence research to create
intelligent systems that combine learning and symbolic components, a new
sub-area has emerged that focuses on combining machine learning (ML) components
with techniques developed by the Semantic Web (SW) community - Semantic Web
Machine Learning (SWeML for short). Due to its rapid growth and impact on
several communities in the last two decades, there is a need to better
understand the space of these SWeML Systems, their characteristics, and trends.
Yet, surveys that adopt principled and unbiased approaches are missing. To fill
this gap, we performed a systematic study and analyzed nearly 500 papers
published in the last decade in this area, where we focused on evaluating
architectural, and application-specific features. Our analysis identified a
rapidly growing interest in SWeML Systems, with a high impact on several
application domains and tasks. Catalysts for this rapid growth are the
increased application of deep learning and knowledge graph technologies. By
leveraging the in-depth understanding of this area acquired through this study,
a further key contribution of this paper is a classification system for SWeML
Systems which we publish as ontology.Comment: Preprint of a paper in the resource track of the 20th Extended
Semantic Web Conference (ESWC'23
Neurosymbolic AI for Reasoning on Graph Structures: A Survey
Neurosymbolic AI is an increasingly active area of research which aims to
combine symbolic reasoning methods with deep learning to generate models with
both high predictive performance and some degree of human-level
comprehensibility. As knowledge graphs are becoming a popular way to represent
heterogeneous and multi-relational data, methods for reasoning on graph
structures have attempted to follow this neurosymbolic paradigm. Traditionally,
such approaches have utilized either rule-based inference or generated
representative numerical embeddings from which patterns could be extracted.
However, several recent studies have attempted to bridge this dichotomy in ways
that facilitate interpretability, maintain performance, and integrate expert
knowledge. Within this article, we survey a breadth of methods that perform
neurosymbolic reasoning tasks on graph structures. To better compare the
various methods, we propose a novel taxonomy by which we can classify them.
Specifically, we propose three major categories: (1) logically-informed
embedding approaches, (2) embedding approaches with logical constraints, and
(3) rule-learning approaches. Alongside the taxonomy, we provide a tabular
overview of the approaches and links to their source code, if available, for
more direct comparison. Finally, we discuss the applications on which these
methods were primarily used and propose several prospective directions toward
which this new field of research could evolve.Comment: 21 pages, 8 figures, 1 table, currently under review. Corresponding
GitHub page here: https://github.com/NeSymGraph
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