153,891 research outputs found
Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context
Mathematical formulae represent complex semantic information in a concise
form. Especially in Science, Technology, Engineering, and Mathematics,
mathematical formulae are crucial to communicate information, e.g., in
scientific papers, and to perform computations using computer algebra systems.
Enabling computers to access the information encoded in mathematical formulae
requires machine-readable formats that can represent both the presentation and
content, i.e., the semantics, of formulae. Exchanging such information between
systems additionally requires conversion methods for mathematical
representation formats. We analyze how the semantic enrichment of formulae
improves the format conversion process and show that considering the textual
context of formulae reduces the error rate of such conversions. Our main
contributions are: (1) providing an openly available benchmark dataset for the
mathematical format conversion task consisting of a newly created test
collection, an extensive, manually curated gold standard and task-specific
evaluation metrics; (2) performing a quantitative evaluation of
state-of-the-art tools for mathematical format conversions; (3) presenting a
new approach that considers the textual context of formulae to reduce the error
rate for mathematical format conversions. Our benchmark dataset facilitates
future research on mathematical format conversions as well as research on many
problems in mathematical information retrieval. Because we annotated and linked
all components of formulae, e.g., identifiers, operators and other entities, to
Wikidata entries, the gold standard can, for instance, be used to train methods
for formula concept discovery and recognition. Such methods can then be applied
to improve mathematical information retrieval systems, e.g., for semantic
formula search, recommendation of mathematical content, or detection of
mathematical plagiarism.Comment: 10 pages, 4 figure
Substructure Discovery Using Minimum Description Length and Background Knowledge
The ability to identify interesting and repetitive substructures is an
essential component to discovering knowledge in structural data. We describe a
new version of our SUBDUE substructure discovery system based on the minimum
description length principle. The SUBDUE system discovers substructures that
compress the original data and represent structural concepts in the data. By
replacing previously-discovered substructures in the data, multiple passes of
SUBDUE produce a hierarchical description of the structural regularities in the
data. SUBDUE uses a computationally-bounded inexact graph match that identifies
similar, but not identical, instances of a substructure and finds an
approximate measure of closeness of two substructures when under computational
constraints. In addition to the minimum description length principle, other
background knowledge can be used by SUBDUE to guide the search towards more
appropriate substructures. Experiments in a variety of domains demonstrate
SUBDUE's ability to find substructures capable of compressing the original data
and to discover structural concepts important to the domain. Description of
Online Appendix: This is a compressed tar file containing the SUBDUE discovery
system, written in C. The program accepts as input databases represented in
graph form, and will output discovered substructures with their corresponding
value.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
Towards automatic construction of domain ontologies: Application to ISA88 and assessment
Process Systems Engineering has shown a growing interest on ontologies to develop knowledge models, organize information, and produce software accordingly. Although software tools supporting the structure of ontologies exist, developing a PSE ontology is a creative procedure to be performed by human experts from each specific domain. This work explores the opportunities for automatic construction of domain ontologies. Specialised documentation can be selected and automatically parsed; next pattern recognition methods can be used to extract concepts and relations; finally, supervision is required to validate the automatic outcome, as well as to complete the task. The bulk of the development of an ontology is expected to result from the application of systematic procedures, thus the development time will be significantly reduced. Automatic methods were prepared and applied to the development of an ontology for batch processing based on the ISA88 standard. Methods are described and commented, and results are discussed from the comparison with a previous ontology for the same domain manually developed.Postprint (published version
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