Ontologies and languages for representing mathematical knowledge on the Semantic Web

Lange, Christoph

doi:10.3233/sw-2012-0059

Cited by 65 publications

(27 citation statements)

References 79 publications

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“…To put our research into the context, we summarize the most relevant previous works for representing mathematical knowledge in this section. For a more comprehensive overview of services, ontological models and languages for mathematical knowledge management on the Semantic Web and beyond, we refer the interested reader to C. Lange's survey [19].…”

Section: Related Workmentioning

confidence: 99%

OntoMath PRO Ontology: A Linked Data Hub for Mathematics

Nevzorova

Zhiltsov

Kirillovich

et al. 2014

Knowledge Engineering and the Semantic Web

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Abstract. In this paper, we present an ontology of mathematical knowledge concepts that covers a wide range of the fields of mathematics and introduces a balanced representation between comprehensive and sensible models. We demonstrate the applications of this representation in information extraction, semantic search, and education. We argue that the ontology can be a core of future integration of math-aware data sets in the Web of Data and, therefore, provide mappings onto relevant datasets, such as DBpedia and ScienceWISE.

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Section: Related Workmentioning

confidence: 99%

OntoMath PRO Ontology: A Linked Data Hub for Mathematics

Nevzorova

Zhiltsov

Kirillovich

et al. 2014

Knowledge Engineering and the Semantic Web

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“…Although Resource Description Framework -RDF and OWL were not developed to given support for numeric concepts, because they depend on schema definition and are based on the eXtensible Markup Language -XML, the set of upper level ontologies Semantic Web for Earth and Environmental Terminology -SWEET <http://sweet.jpl.nasa.gov/ontology/> is a good example to integrate mathematical knowledge with scientific application domains (Lange, 2013). In this work some statements from SWEET are reused and extended.…”

Section: Ontologiesmentioning

confidence: 99%

Domain Ontology for Time Series Provenance

Souza

Vaz

Sunyé

2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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Time series data are generated all the time with a volume without precedent, constituting themselves of a points sequence spread out over time, usually at time regular intervals. Time series analysis is different from data analysis, given its intrinsic nature, where observations are dependent and the observations order is important for analysis. The knowledge about the data which will be analyzed is relevant in an analysis process, but this knowledge is not always explicit and easy to interpret in many information resources. Time series can be semantically enriched where provenance information using ontologies allows to representing and inferring knowledge. The main contribution of this paper is to present a domain ontology developed by modular design for time series provenance, which adds semantic knowledge and contributes to the choice of appropriate statistical methods for an important step of time series analysis that is the trend extraction (detrending). Trend is a time series component that needs be extracted because it can hide other phenomena, as well as the most statistical methods are developed for stationary time series. With this work, is intended to contribute for semantically improving the decision making about trend extraction step, facilitating the preprocessing phase of time series analysis.

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“…The Mathematical Markup Language (MathML) may provide a starting point; however, the representation of mathematical knowledge on the Semantic Web is still an open research question. An overview of the state of the art and a roadmap for future work have recently been presented by Lange (2012). In terms of provenance, the core challenge will be to represent the complex workflows used to learn the primitives.…”

Section: Provenancementioning

confidence: 99%

Observation‐Driven Geo‐Ontology Engineering

Janowicz

2012

Transactions in GIS

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Big Data, Linked Data, Smart Dust, Digital Earth, and e-Science are just some of the names for research trends that surfaced over the last years. While all of them address different visions and needs, they share a common theme: How do we manage massive amounts of heterogeneous data, derive knowledge out of them instead of drowning in information, and how do we make our findings reproducible and reusable by others? In a network of knowledge, topics span across scientific disciplines and the idea of domain ontologies as common agreements seems like an illusion. In this work, we argue that these trends require a radical paradigm shift in ontology engineering away from a small number of authoritative, global ontologies developed top-down, to a high number of local ontologies that are driven by application needs and developed bottom-up out of observation data. Similarly as the early Web was replaced by a social Web in which volunteers produce data instead of purely consuming it, the next generation of knowledge infrastructures has to enable users to become knowledge engineers themselves. Surprisingly, existing ontology engineering frameworks are not well suited for this new perspective. Hence, we propose an observation-driven ontology engineering framework, show how its layers can be realized using specific methodologies, and relate the framework to existing work on geo-ontologies.

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Ontologies and languages for representing mathematical knowledge on the Semantic Web

Cited by 65 publications

References 79 publications

OntoMath PRO Ontology: A Linked Data Hub for Mathematics

OntoMath PRO Ontology: A Linked Data Hub for Mathematics

Domain Ontology for Time Series Provenance

Observation‐Driven Geo‐Ontology Engineering

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