SADI, SHARE, and the in silico scientific method

<p>Abstract</p> <p>Background</p> <p>The emergence and uptake of Semantic Web technologies by the Life Sciences provides exciting opportunities for exploring novel ways to conduct <it>in silico</it> science. Web Service Workflows are already becoming first-class objects in “the new way”, and serve as explicit, shareable, referenceable representations of how an experiment was done. In turn, Semantic Web Service projects aim to facilitate workflow construction by biological domain-experts such that workflows can be edited, re-purposed, and re-published by non-informaticians. However the aspects of the scientific method relating to explicit discourse, disagreement, and hypothesis generation have remained relatively impervious to new technologies.</p> <p>Results</p> <p>Here we present SADI and SHARE - a novel Semantic Web Service framework, and a reference implementation of its client libraries. Together, SADI and SHARE allow the semi- or fully-automatic discovery and pipelining of Semantic Web Services in response to <it>ad hoc</it> user queries.</p> <p>Conclusions</p> <p>The semantic behaviours exhibited by SADI and SHARE extend the functionalities provided by Description Logic Reasoners such that novel assertions can be automatically added to a data-set without logical reasoning, but rather by analytical or annotative services. This behaviour might be applied to achieve the “semantification” of those aspects of the <it>in silico</it> scientific method that are not yet supported by Semantic Web technologies. We support this suggestion using an example in the clinical research space.</p

A framework for semantic checking of information systems

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresIn this day and age, enterprises often find that their business benefits greatly if they collaborate with others in order to be more competitive and productive. However these collaborations often come with some costs since the worldwide diversity of communities has led to the development of various knowledge representation elements, namely ontologies that, in most cases, are not semantically equivalent. Consequently, even though some enterprises may operate in the same domain, they can have different representations of that same knowledge. However, even after solving this issue and establishing a semantic alignment with other systems, they do not remain unchanged. Subsequently, a regular check of its semantic alignment is needed. To aid in the resolution of this semantic interoperability problem, the author proposes a framework that intends to provide generic solutions and a mean to validate the semantic consistency of ontologies in various scenarios, thus maintaining the interoperability state between the enrolled systems

ChImp:Visualizing Ontology Changes and their Impact in Protégé

Today, ontologies are an established part of many applications and research. However, ontologies evolve over time, and ontology editors---engineers and domain experts---need to be aware of the consequences of changes while editing. Ontology editors might not be fully aware of how they are influencing consistency, quality, or the structure of the ontology, possibly causing applications to fail. To support editors and increase their sensitivity towards the consequences of their actions, we conducted a user survey to elicit preferences for representing changes, e.g., with ontology metrics such as number of classes and properties. Based on the survey, we developed ChImp---a Protégé plug-in to display information about the impact of changes in real-time. During editing of the ontology, ChImp lists the applied changes, checks and displays the consistency status, and reports measures describing the effect on the structure of the ontology. Akin to software IDEs and integrated testing approaches, we hope that displaying such metrics will help to improve ontology evolution processes in the long run

Visualising the effects of ontology changes and studying their understanding with ChImp

Due to the Semantic Web's decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies

Information management in work organization domain in network organizations

Tese de mestrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Spatial Ontology for the Production Domain of Petroleum Geology

ABSTRACT The availability of useful information for research strongly depends on well structured relationships between consistently defined concepts (terms) in that domain. This can be achieved through ontologies. Ontologies are models of the knowledge of specific domain such as petroleum geology, in a computer understandable format. Knowledge is a collection of facts. Facts are represented by RDF triples (subject-predicate-object). A domain ontology is therefore a collection of many RDF triples, which represent facts of that domain. The SWEET ontologies are upper or top-level ontologies (foundation ontologies) consisting of thousands of very general concepts. These concepts are obtained from of Earth System science and include other related concepts. The work in this thesis deals with scientific knowledge representation in which the SWEET ontologies are extended to include wider, more specific and specialized concepts used in Petroleum Geology. Thus Petroleum Geology knowledge modeling is presented in this thesis

Designing novel abstraction networks for ontology summarization and quality assurance

Biomedical ontologies are complex knowledge representation systems. Biomedical ontologies support interdisciplinary research, interoperability of medical systems, and Electronic Healthcare Record (EHR) encoding. Ontologies represent knowledge using concepts (entities) linked by relationships. Ontologies may contain hundreds of thousands of concepts and millions of relationships. For users, the size and complexity of ontologies make it difficult to comprehend “the big picture” of an ontology\u27s content. For ontology editors, size and complexity make it difficult to uncover errors and inconsistencies. Errors in an ontology will ultimately affect applications that utilize the ontology. In prior studies abstraction networks (AbNs) were developed to provide a compact summary of an ontology\u27s content and structure. AbNs have been shown to successfully support ontology summarization and quality assurance (QA), e.g., for SNOMED CT and NCIt. Despite the success of these previous studies, several major, unaddressed issues affect the applicability and usability of AbNs. This thesis is broken into five major parts, each addressing one issue. The first part of this dissertation addresses the scalability of AbN-based QA techniques to large SNOMED CT hierarchies. Previous studies focused on relatively small hierarchies. The QA techniques developed for these small hierarchies do not scale to large hierarchies, e.g., Procedure and Clinical finding. A new type of AbN, called a subtaxonomy, is introduced to address this problem. Subtaxonomies summarize a subset of an ontology\u27s content. Several types of subtaxonomies and subtaxonomy-based QA studies are discussed. The second part of this dissertation addresses the need for summarization and QA methods for the twelve SNOMED CT hierarchies with no lateral relationships. Previously developed SNOMED CT AbN derivation methodologies, which require lateral relationships, cannot be applied to these hierarchies. The Tribal Abstraction Network (TAN) is a new type of AbN derived using only hierarchical relationships. A TAN-based QA methodology is introduced and the results of a QA review of the Observable entity hierarchy are reported. The third part focuses on the development of generic AbN derivation methods that are applicable to groups of structurally similar ontologies, e.g., those developed in the Web Ontology Language (OWL) format. Previously, AbN derivation techniques were applicable to only a single ontology at a time. AbNs that are applicable to many OWL ontologies are introduced, a preliminary study on OWL AbN granularity is reported on, and the results of several QA studies are presented. The fourth part describes Diff Abstraction Networks, which summarize and visualize the structural differences between two ontology releases. Diff Area Taxonomy and Diff Partial-area Taxonomy derivation methodologies are introduced and Diff Partial-area taxonomies are derived for three OWL ontologies. The Diff Abstraction Network approach is compared to the traditional ontology diff approach. Lastly, tools for deriving and visualizing AbNs are described. The Biomedical Layout Utility Framework is introduced to support the automatic creation, visualization, and exploration of abstraction networks for SNOMED CT and OWL ontologies