Semantic science and machine-accessible scientific theories

2008

At present, oil companies acquire data by measurement surveys during field operation. In order to reduce exploration and production risks, they use various software tools for ensuring the quality of the collected information, carrying out human interpretations and creating new data and new knowledge. However, the additional knowledge corresponding to data processing and interpretation is generally embedded in technical data stores and files or remains hidden in reports or in experts’ brains. Moreover, while there exist rough communication standards for exchanging measured data representation, none is available for exchanging and sharing interpretations. The consequence is that interpretation remains in most cases inaccessible for queries or updating and that it is never used in core business knowledge communication. Thus it generally appears impossible to characterize the content of a given interpretation and to determine when, how, why and by whom it was created. This lack of recor...

2012

The term 'hypothesis' is part of the Linked Science Core Vocabulary (LSC) as one of the core elements for making scientific assets explicit and linked in the web of data. Hypotheses are generally understood as propositions for explaining observed phenomena, but eliciting and linking hypotheses can be a challenge. In this paper, we elaborate on a semantic view on hypotheses and their linkage, by striving for minimal ontological commitments. We address the engineering of hypotheses as linked data, and build upon LSC by extending it in order to accommodate terms necessary in model-based sciences such as Computational Science. Then we instantiate the extended LSC by eliciting and linking hypotheses from a published research in Computational Hemodynamics.

Science-and biology in particular-has a rich tradition in categorical knowledge management. This continues today in the generation and use of formal ontologies.

AI*IA 2017 Advances in Artificial Intelligence

The geologic mapping process requires the organization of data according tothe general knowledge about the objects in the map, namely the geologic units, and tothe objectives of a graphic representation of such objects in a map, following some established model of geotectonic evolution. Semantics can greatly help such a process in providing a terminological base to name and classify the objects of the map and supporting the application of reasoning mechanisms for the derivation of novel properties and relations about the objects of the map. The OntoGeonous initiative has built a terminological base of geological knowledge in a machine-readable format, following the Semantic Web tenets and the Linked Data paradigm, with the construction of an appropriate data base schema that can be then filled with the objects of the map. The paper will present the conceptual model of the geologic system and how the elements of the cartographic database are classified from general definitions. Also, the paper addresses the setup of web-based services that respond to queries concerning the properties of the map elements that are not explicitly asserted in the underlying data base, but are inferred through a reasoning process.

Proceedings of the 2nd International Conference on Geographical Information Systems Theory, Applications and Management, 2016

Early mineral exploration activities motivates innovative research into cost-effective methods for automating the process of mineral deposits' prospectivity analysis. At the heart that process is the development of a knowledge base that is not only capable of consuming geodata originating from multiple sources with different representation format and data veracity, but also provides for the reasoning capabilities required by the prospectivity analysis. In this paper, we present an integrative semantic-driven approach that reconciles the representation format of sourced geodata using a unifying metadata model, and encodes the prospectivity analysis of geological knowledge both at the schemata modelling level and through more explicit reasoning rules operating on the semantically tagged geodata. The paper provides valuable insights into the challenges of representation, inference, and query of geospatially-tagged geological data and analyses our initial results into the prospectivity analysis of mineral deposits.

Conceptual representations are being developed within many geoscience domains to aid semantic-enabled scientific computing, e.g. geology, geophysics, hydrology, marine science, planetary science. These representations often make different ontological assumptions, causing their integration to be impeded. This is particularly problematic for science issues that involve crossdomain knowledge, such as those related to climate change, biodiversity, and hazards risk. Emerging foundational ontologies have the potential to alleviate this problem by providing a coherent and complete conceptual basis for domain representations. In this paper we present work-in-progress in which the DOLCE foundational ontology is used to integrate two geoscience knowledge representations, the GeoSciML schema and SWEET ontology, to enable cross-domain scientific computing. The preliminary results indicate good conceptual coverage by the foundational ontology, minimal conflict and overlap between the domain ontologies, alternative approaches for aligning with the foundational ontology, and several issues with the domain and foundational ontologies. An OWL-DL encoding is in progress and testing via data integration is planned.

2011

Interoperability of geological data receives considerable attention in recent years and different types of ontologies (i.e., an ontology spectrum) have been studied and applied. There are several key challenges faced by current and potential practices of an ontology spectrum in the field of geology: (1) Modeling and encoding of ontologies; (2) Multilinguality of geological data and ontologies; (3) Flexibility and usefulness of ontology-based applications; (4) Mediation and evolution of geological data and ontologies. This paper reviews our works addressing these challenges in the past three years, concludes findings and proposes directions for future works.

We present results of a research effort into the application of semantic web methods and technologies to address the challenging problem of integrating data from heterogeneous sources-in particular from volcanic and atmospheric chemistry data in support of assessing a particular science question: what are measureable atmospheric effects of of a volcanic eruption. The introduction of formal semantics in our methods and into the implemented technical infrastructure allows scientists to ask measurement based ...

Abstract. Semantic technologies and ontologies play an increasing role in scientific workflow systems and knowledge infrastructures. While ontologies are mostly used for the semantic annotation of metadata, semantic technologies enable searching metadata catalogs beyond simple keywords, with some early evidence of semantics used for data translation. However, the next generation of distributed and interdisciplinary knowledge infrastructures will require capabilities beyond simple subsumption reasoning over subclass relations.

2016

This paper presents our work on development of OWL-driven systems for formal representation and reasoning about terminological knowledge and facts in petrology. The long-term aim of our project is to provide solid foundations for a large-scale integration of various kinds of knowledge, including basic terms, rock classification algorithms, findings and reports. We describe three steps we have taken towards that goal here. First, we develop a semi-automated procedure for transforming a database of igneous rock samples to texts in a controlled natural language (CNL), and then a collection of OWL ontologies. Second, we create an OWL ontology of important petrology terms currently described in natural language thesauri. We describe a prototype of a tool for collecting definitions from domain experts. Third, we present an approach to formalization of current industrial standards for classification of rock samples, which requires linear equations in OWL 2. In conclusion, we discuss a range of opportunities arising from the use of semantic technologies in petrology and outline the future work in this area.