The Web Ontology Language OWL is based on the highly expressive description logic SROIQ, which allows OWL ontology users to employ out-of-the-box reasoners to compute information that is not only explicitly asserted, but entailed by the ontology. Explanation facilities for entailments of OWL ontologies form an essential part of ontology development tools, as they support users in detecting and repairing errors in potentially large and highly complex ontologies, thus helping to ensure ontology quality. Justifications, minimal subsets of an ontology that are sufficient for an entailment to hold, are currently the prevalent form of explanation in OWL ontology development tools. They have been found to significantly reduce the time and effort required to debug erroneous entailments. A large number of entailments, however, have not only one but many justifications, which can make it considerably more challenging for a user to find a suitable repair for the entailment.In this thesis, we investigate the relationships between multiple justifications for both single and multiple entailments, with the goal of exploiting this justificatory structure in order to devise new coping strategies for multiple justifications. We describe various aspects of the justificatory structure of OWL ontologies, such as shared axiom cores and structural similarities. We introduce a model for measuring user effort in the debugging process and propose debugging strategies that exploit the justificatory structure in order to reduce user effort. Finally, an analysis of a large corpus of ontologies from the biomedical domain reveals that OWL ontologies used in practice frequently exhibit a rich justificatory structure.

Enterprise Information Integration (EII) is rapidly becoming one of the pillars of modern corporate information systems. Given the spread and diversity of information sources in an enterprise, it has become increasingly difficult for decision makers to have access to relevant and accurate information at the opportune time. It has therefore become critical to seamlessly integrate the diverse information stores found in an organization into a single coherent data source. This is the job of EII and one of the key components to making it work is harnessing the implied meaning or semantics hidden within data sources. Modern EII systems are capable of harnessing semantic information and ontologies to make integration across data stores possible. These systems do not, however, allow a consumer of the integration service to build queries with semantic meaning. This is due to the fact that most EII systems make use of XQuery, SQL, or both, as query languages, neither of which has the capability to build semantically rich queries. In this thesis Semantos (from the Greek word sema for “sign or token”) is proposed as a viable alternative: an information query language based in XML, which is capable of exploiting ontologies, enabling consumers to build semantically enriched queries. An exploration is made into the characteristics or requirements that Semantos needs to satisfy as a semantically smart information query language. From these requirements we design and develop a software implementation. The benefit of Semantos is that it possesses a query structure that allows automated processes to decompose and restructure the queries without human intervention. We demonstrate the applicability of Semantos using two realistic examples: a query enhancement- and a query translation service. Both expound the ability of a Semantos query to be manipulated by automated services to achieve Information Integration goals. / Dissertation (MSc)--University of Pretoria, 2009. / Computer Science / unrestricted

Cheap electronic storage and Internet bandwidth has increased the amount of online data. Large quantities of metadata are created to manage this wealth of information. Methods to organize and structure metadata has led to the development of ontologies - data that is organized to describe the relation between elements. The creation of large ontologies has brought forth the need for ontology management strategies. Ontology alignment and merging techniques are standard operations for ontology management. Accurate ontology alignment methods are typically semi-automatic, meaning they require periodic user input. This becomes infeasible on large ontologies and the accuracy and efficiency drops significantly when these algorithms are forced to align without human interaction. Bioinformatics, for example, has seen the influx of large ontologies, such as signal pathway sets with thousands of elements or protein-protein interaction (PPI) databases with hundreds of thousands of elements. This drives the need for a reliable method of large-scale ontology alignment. Many bioinformatics ontologies contain references to domain ontologies - manually curated ontologies describing additional, general information about the terms in the ontologies. For example, more than 2/3 of proteins in PPI data sets contain at least one annotation to the domain ontology the Gene Ontology. We use the domain ontology references as features to compute similarity between elements. However, there are few efficient ways to compute similarity from structured features. We present a novel, automatic method for aligning ontologies based on such domain ontology features. Specifically, we use simulated annealing to reduce the complexity of the domain ontologys structure by finding approximate relevant clusters of elements. An intermediate step performs hierarchical clustering based on the similarity between elements of the ontology. Then the mapping between clusters across aligning ontologies is built. The final step builds an alignment between matched clusters. To evaluate our methods, we perform an alignment between Human (Homo Sapiens) and Yeast (Saccharomyces cerevisiae) signal pathways provided by the Reactome database. The results were compared against reliable homology studies of proteins. The final mapping produces alignments that are significantly more accurate than the traditional ontology alignment methods, without any human involvement. / Science, Faculty of / Computer Science, Department of / Graduate

A reutilização de conhecimento é uma tarefa chave para qualquer sistema computacional. Entretanto, o reúso indiscriminado desse conhecimento pode gerar resultados conflitantes com o objetivo de uso do conhecimento, levando sistemas a se comportarem de maneira imprevisível. Neste trabalho estudamos as consequências do reúso de conhecimento em ontologias baseadas em lógicas de descrição. Focamos principalmente nos problemas que podem ser causados pela fusão de ontologias. Investigamos e comparamos a capacidade das ferramentas de desenvolvimento de ontologias atuais de lidarem com esses problemas e como a teoria se desenvolveu para resolver os mesmos problemas. Realizamos a construção de um arcabouço lógico e de software, organizado na forma de um processo, que tem como objetivo auxiliar o projetista de ontologias a resolver conflitos advindos da fusão. O processo agrupa tarefas descritas normalmente na literatura em separado. Acreditamos que a união dessas abordagens leva a uma melhor solução de conflitos. Durante o desenvolvimento deste trabalho, concentramos nossos esforços principalmente no desenvolvimento de algoritmos para a construção de sub-ontologias maximais, onde os conflitos não ocorram, bem como a ordenação desses conjuntos segundo critérios comuns discutidos na literatura. Tais estratégias foram implementadas em software e testadas utilizando dados gerados automaticamente e dados reais. / Knowledge reuse is a key task during any system development. Nevertheless, careless knowledge reuse may generate conflicting outcomes regarding the system goal, leading such systems to unpredictable behaviour. With that in mind, during this research we studied the consequences of knowledge reuse in ontologies based on description logics. We focused mainly on conflicts arising from ontology merging. We investigated and compared the features developed for this purpose on ontology development tools and how the theory field proposed to deal with the same issues. We developed both a logical and a software framework grouped into a process that aims to help the ontology designer solve conflicts arising from ontology merging. The process groups common tasks that are normally described separately. We believe that the unification of these approaches should result in a better solution for the merging conflicts. We concentrated our efforts during this work on building algorithms for building maximal sub-ontologies where such conflicts are non-existent as well as means for ordering such sets according to a few relevance criteria commonly described at the literature. Such algorithms were implemented and tested against automatically generated and real data.

The amount of available protein sequences is rapidly increasing, mainly as a consequence of the development and application of high throughput sequencing technologies in the life sciences. It is a key question in the life sciences to identify the functions of proteins, and furthermore to identify the phenotypes that may be associated with a loss (or gain) of function in these proteins. Protein functions are generally determined experimentally, and it is clear that experimental determination of protein functions will not scale to the current { and rapidly increasing { amount of available protein sequences (over 300 million). Furthermore, identifying phenotypes resulting from loss of function is even more challenging as the phenotype is modi ed by whole organism interactions and environmental variables. It is clear that accurate computational prediction of protein functions and loss of function phenotypes would be of signi cant value both to academic research and to the biotechnology industry. We developed and expanded novel methods for representation learning, predicting protein functions and their loss of function phenotypes. We use deep neural network algorithm and combine them with symbolic inference into neural-symbolic algorithms. Our work signi cantly improves previously developed methods for predicting protein functions through methodological advances in machine learning, incorporation of broader data types that may be predictive of functions, and improved systems for neural-symbolic integration. The methods we developed are generic and can be applied to other domains in which similar types of structured and unstructured information exist. In future, our methods can be applied to prediction of protein function for metagenomic samples in order to evaluate the potential for discovery of novel proteins of industrial value. Also our methods can be applied to the prediction of loss of function phenotypes in human genetics and incorporate the results in a variant prioritization tool that can be applied to diagnose patients with Mendelian disorders.

The engineering of laminated composite structures is a complex task for design engineers and manufacturers, requiring significant management of manufacturing process and materials information. Ontologies are becoming increasingly commonplace for semantically representing knowledge in a formal manner that facilitates sharing of rich information between people and applications. Moreover, ontologies can support first-order logic and reasoning by rule engines that enhance automation. To support the engineering of laminated composite structures, this work developed a novel Semantic LAminated Composites Knowledge management System (SLACKS) that is based on a suite of ontologies for laminated composites materials and design for manufacturing (DFM) and their integration into a previously developed engineering design framework. By leveraging information from CAD/FEA tools and materials data from online public databases, SLACKS uniquely enables software tools and people to interoperate, to improve communication and automate reasoning during the design process. With SLACKS, this research shows the power of integrating relevant domains of the product lifecycle, such as design, analysis, manufacturing and materials selection through the engineering case study of a wind turbine blade. The integration reveals a usable product lifecycle knowledge tool that can facilitate efficient knowledge creation, retrieval and reuse, from design inception to manufacturing of the product.

The rapid production of digital information makes the task of locating relevant information increasingly difficult. Keyword search alleviates this difficulty by retrieving documents containing keywords of interest. However, keyword search suffers from a number of issues such ambiguity, synonymy, and the inability to handle semantic constraints. Semantic search helps resolve these issues but is limited by the quality of annotations which are likely to be incomplete or imprecise. Hybrid search, a search technique that combines the merits of both keyword and semantic search, appears to be a promising solution. In this work we introduce HyKSS, a hybrid search system driven by extraction ontologies for both annotation creation and query interpretation. HyKSS is not limited to a single domain, but rather allows queries to cross ontological boundaries. We show that our hybrid search system, which uses a query driven dynamic ranking mechanism, outperforms keyword and semantic search in isolation, as well as a number of other non-HyKSS hybrid ranking approaches, over data sets of short topical documents. We also find that there is not a statistically significant difference between using multiple ontologies for query generation and simply selecting and using the best matching ontology.

To understand the process of life, it is crucial for us to study proteins and their functions. Proteins execute (almost) all cellular activities, and their functions are standardized by Gene Ontology (GO). The amount of discovered protein sequences grows rapidly as a consequence of the fast rate of development of technologies in gene sequencing. In UniProtKB, there are more than 200 million proteins. Still, less than 1% of the proteins in the UniProtKB database are experimentally GO-annotated, which is the result of the exorbitant cost of biological experiments. To minimize the large gap, developing an efficient and effective method for automatic protein function prediction (AFP) is essential. Many approaches have been proposed to solve the AFP problem. Still, these methods suffer from limitations in the way the knowledge of the domain is presented and what type of knowledge is included. In this work, we formulate the task of AFP as an entailment problem and exploit the structure of the related knowledge in a set and reusable framework. To achieve this goal, we construct a knowledge base of formal GO axioms and protein-protein interactions to use as background knowledge for AFP. Our experiments show that the approach proposed here, which allows for ontology awareness, improves results for AFP of proteins; they also show the importance of including protein-protein interactions for predicting the functions of proteins.

No description available.

No description available.