Ernesto Jiménez-Ruiz

Assessment of disease named entity recognition on a corpus of annotated sentences

BackgroundIn recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions.ResultsAs part of our research work, we have taken a corpus that has been delivered in the past for the identification of associations of genes to diseases based on the UMLS Metathesaurus and we have reprocessed and re-annotated the corpus. We have gathered annotations for disease entities from two curators, analyzed their disagreement (0.51 in the kappa-statistic) and composed a single annotated corpus for public use. Thereafter, three solutions for disease named entity recognition including MetaMap have been applied to the corpus to automatically annotate it with UMLS Metathesaurus concepts. The resulting annotations have been benchmarked to compare their performance.ConclusionsThe annotated corpus is publicly available at ftp://ftp.ebi.ac.uk/pub/software/textmining/corpora/diseasesand can serve as a benchmark to other systems. In addition, we found that dictionary look-up already provides competitive results indicating that the use of disease terminology is highly standardized throughout the terminologies and the literature. MetaMap generates precise results at the expense of insufficient recall while our statistical method obtains better recall at a lower precision rate. Even better results in terms of precision are achieved by combining at least two of the three methods leading, but this approach again lowers recall. Altogether, our analysis gives a better understanding of the complexity of disease annotations in the literature. MetaMap and the dictionary based approach are available through the Whatizit web service infrastructure (Rebholz-Schuhmann D, Arregui M, Gaudan S, Kirsch H, Jimeno A: Text processing through Web services: Calling Whatizit. Bioinformatics 2008, 24:296-298).

Ontology Integration Using Mappings: Towards Getting the Right Logical Consequences

We propose a general method and novel algorithmic techniques to facilitate the integration of independently developed ontologies using mappings. Our method and techniques aim at helping users understand and evaluate the semantic consequences of the integration, as well as to detect and fix potential errors. We also present ContentMap, a system that implements our approach, and a preliminary evaluation which suggests that our approach is both useful and feasible in practice.

Safe and economic re-use of ontologies: a logic-based methodology and tool support

Driven by application requirements and using well-understood theoretical results, we describe a novel methodology and a tool for modular ontology design. We support the user in the safe use of imported symbols and in the economic import of the relevant part of the imported ontology. Both features are supported in a well-understood way: safety guarantees that the semantics of imported concepts is not changed, and economic import guarantees that no difference can be observed between importing the whole ontology and importing the relevant part.

Large-scale interactive ontology matching: algorithms and implementation

In this paper we present the ontology matching system LogMap 2, a much improved version of its predecessor LogMap. LogMap 2 supports user interaction during the matching process, which is essential for use cases requiring very accurate mappings. Interactivity, however, imposes very strict scalability requirements; we are able to satisfy these requirements by providing real-time user response even for large-scale ontologies. Finally, LogMap 2 implements scalable reasoning and diagnosis algorithms, which minimise any logical inconsistencies introduced by the matching process.

Optique: Zooming in on Big Data

Optique overcomes problems in current ontology-based data access systems pertaining to installation overhead, usability, scalability, and scope by integrating a user-oriented query interface, semi-automated managing methods, new query rewriting techniques, and temporal and streaming data processing in one platform.

Logic-based assessment of the compatibility of UMLS ontology sources

BackgroundThe UMLS Metathesaurus (UMLS-Meta) is currently the most comprehensive effort for integrating independently-developed medical thesauri and ontologies. UMLS-Meta is being used in many applications, including PubMed and ClinicalTrials.gov. The integration of new sources combines automatic techniques, expert assessment, and auditing protocols. The automatic techniques currently in use, however, are mostly based on lexical algorithms and often disregard the semantics of the sources being integrated.ResultsIn this paper, we argue that UMLS-Meta’s current design and auditing methodologies could be significantly enhanced by taking into account the logic-based semantics of the ontology sources. We provide empirical evidence suggesting that UMLS-Meta in its 2009AA version contains a significant number of errors; these errors become immediately apparent if the rich semantics of the ontology sources is taken into account, manifesting themselves as unintended logical consequences that follow from the ontology sources together with the information in UMLS-Meta. We then propose general principles and specific logic-based techniques to effectively detect and repair such errors.ConclusionsOur results suggest that the methodologies employed in the design of UMLS-Meta are not only very costly in terms of human effort, but also error-prone. The techniques presented here can be useful for both reducing human effort in the design and maintenance of UMLS-Meta and improving the quality of its contents.

Optique: Towards OBDA Systems for Industry

The recently started EU FP7-funded project Optique will develop an end-to-end OBDA system providing scalable end-user access to industrial Big Data stores. This paper presents an initial architectural specification for the Optique system along with the individual system components.

OptiqueVQS: towards an ontology-based visual query system for big data

A recent EU project, named Optique, with a strong industrial perspective, strives to enable scalable end-user access to Big Data. To this end, Optique employs an ontology-based approach, along with other techniques such as query optimisation and parallelisation, for scalable query formulation and evaluation. In this paper, we specifically focus on end-user visual query formulation, demonstrate our preliminary ontology-based visual query system (i.e., interface), and discuss initial insights for alleviating the affects of Big Data.

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings suffer from logical flaws, their usefulness may be diminished. In this paper we present an approximate method to detect and correct violations to the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. We show that this is indeed the case in our application domain based on the EU Optique project. Additionally, our extensive evaluation conducted with both the Optique use case and the data sets from the Ontology Alignment Evaluation Initiative (OAEI) suggests that our method is both useful and feasible in practice.

XML-based approaches for the integration of heterogeneous bio-molecular data

BackgroundThe todays public database infrastructure spans a very large collection of heterogeneous biological data, opening new opportunities for molecular biology, bio-medical and bioinformatics research, but raising also new problems for their integration and computational processing.ResultsIn this paper we survey the most interesting and novel approaches for the representation, integration and management of different kinds of biological data by exploiting XML and the related recommendations and approaches. Moreover, we present new and interesting cutting edge approaches for the appropriate management of heterogeneous biological data represented through XML.ConclusionXML has succeeded in the integration of heterogeneous biomolecular information, and has established itself as the syntactic glue for biological data sources. Nevertheless, a large variety of XML-based data formats have been proposed, thus resulting in a difficult effective integration of bioinformatics data schemes. The adoption of a few semantic-rich standard formats is urgent to achieve a seamless integration of the current biological resources.