Evaluating Ontology-Alignment Techniques

Schreiber, A.T. [Promotor

On adopting Ontology Alignment techniques within the Phenotype Acquisition Process

The work presented in this paper is framed within the context of the BigMed project, aproject funded by the Norwegian Research Council. One of the objectives of BigMed isto enhance the phenotype acquisition process in newborns with a monogenetic disorder,one of the four patient groups studied in the project. The use of the Human PhenotypeOntology (HPO) [1] to tag phenotypes and systems like PhenoTips have substantiallycontributed to the overall phenotype acquisition workflow. PhenoTips [2] is a systemfor the acquisition of phenotypic information in patients with a genetic disease. Phe-noTips also suggests, given a selected set of HPO terms, candidate diagnoses usingOMIM (Online Mendelian Inheritance in Man) codes, and related genes for a subse-quent genetic test. Although PhenoTips represents a fantastic effort, we believe it couldbe extended with suitable Semantic Web solutions. In this paper, we present the firststeps to adopt ontology alignment techniques to contribute to the diagnostic process

GéOnto : Enrichissement d'une taxonomie de concepts topographiques

National audienceIn this paper we present the GéOnto project, aiming in particular to build an ontology of topographic concepts. This ontology is made by enrichment of a first taxonomy developed beforehand, through the analysis of two types of textual documents: technical database specifications and description of journeys. This work relies on natural language processing and ontology alignment techniques, as well as external knowledge resources such as dictionaries and gazetteers

Towards information profiling: data lake content metadata management

There is currently a burst of Big Data (BD) processed and stored in huge raw data repositories, commonly called Data Lakes (DL). These BD require new techniques of data integration and schema alignment in order to make the data usable by its consumers and to discover the relationships linking their content. This can be provided by metadata services which discover and describe their content. However, there is currently a lack of a systematic approach for such kind of metadata discovery and management. Thus, we propose a framework for the profiling of informational content stored in the DL, which we call information profiling. The profiles are stored as metadata to support data analysis. We formally define a metadata management process which identifies the key activities required to effectively handle this.We demonstrate the alternative techniques and performance of our process using a prototype implementation handling a real-life case-study from the OpenML DL, which showcases the value and feasibility of our approach.Peer ReviewedPostprint (author's final draft

The value of usage scenarios for thesaurus alignment in cultural heritage context

Thesaurus alignment is important for efficient access to heterogeneous Cultural Heritage data. Current ontology alignment techniques provide solutions, but with limited value in practice, because the requirements from usage scenarios are rarely taken in account. In this paper, we start from particular requirements for book re-indexing and investigate possible ways of developing, deploying and evaluating thesaurus alignment techniques in this context. We then compare different aspects of this scenario with others from a more general perspective

Knowledge graph embedding for ecotoxicological effect prediction

Exploring the effects of a chemical compound on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. Here, we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical knowledge. These knowledge sources are integrated by ontology alignment techniques. Our experimental results show that the knowledge graph and its embeddings augment the baseline models.publishedVersio

Knowledge Graph Embedding for Ecotoxicological Effect Prediction

Exploring the effects a chemical compound has on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. In this paper we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity. The publicly available effect data is integrated to the knowledge graph using ontology alignment techniques. Our experimental results show that the knowledge graph based approach improves the selected baselines

Analyses linguistiques et techniques d'alignement pour créer et enrichir une ontologie topographique

National audienceOne of the goals of the GéOnto project is to build an ontology of topographic concepts. This ontology results from the enrichment of a first taxonomy developed beforehand, through the analysis of two types of textual documents: technical database specifications and description of journeys. This work relies on natural language processing and ontology alignment techniques, as well as external knowledge resources such as dictionaries and gazetteers.Dans cet article, nous présentons le projet GéOnto dont un des buts est de construire une ontologie de concepts topographiques. Cette ontologie est réalisée par enrichissement d'une première taxonomie de termes réalisée précédemment, et ce grâce à l'analyse de deux types de documents textuels : des spécifications techniques de bases de données et des récits de voyage. Cet enrichissement s'appuie sur des techniques automatiques de traitement du langage et d'alignement d'ontologies, ainsi que sur des connaissances externes comme des dictionnaires et des bases de toponymes

A genetic algorithms-based approach for optimizing similarity aggregation in ontology matching

[Abstract] Ontology matching consists of finding the semantic relations between different ontologies and is widely recognized as an essential process to achieve an adequate interoperability between people, systems or organizations that use different, overlapping ontologies to represent the same knowledge. There are several techniques to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results. Nevertheless, combining multiple similarity measures into a single metric is a complex problem, which has been traditionally solved using weights determined manually by an expert, or through general methods that do not provide optimal results. In this paper, a genetic algorithms based approach to aggregate different similarity metrics into a single function is presented. Starting from an initial population of individuals, each one representing a combination of similarity measures, our approach allows to find the combination that provides the optimal matching quality.Instituto de Salud Carlos III; FISPI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Xunta de Galicia; CN2012/217Xunta de Galicia; CN2011/034Xunta de Galicia; CN2012/21

Ontology alignment based on word embedding and random forest classification.

Ontology alignment is crucial for integrating heterogeneous data sources and forms an important component for realising the goals of the semantic web. Accordingly, several ontology alignment techniques have been proposed and used for discovering correspondences between the concepts (or entities) of different ontologies. However, these techniques mostly depend on string-based similarities which are unable to handle the vocabulary mismatch problem. Also, determining which similarity measures to use and how to effectively combine them in alignment systems are challenges that have persisted in this area. In this work, we introduce a random forest classifier approach for ontology alignment which relies on word embedding to discover semantic similarities between concepts. Specifically, we combine string-based and semantic similarity measures to form feature vectors that are used by the classifier model to determine when concepts match. By harnessing background knowledge and relying on minimal information from the ontologies, our approach can deal with knowledge-light ontological resources. It also eliminates the need for learning the aggregation weights of multiple similarity measures. Our experiments using Ontology Alignment Evaluation Initiative (OAEI) dataset and real-world ontologies highlight the utility of our approach and show that it can outperform state-of-the-art alignment systems