Wiktoria Golik

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

BackgroundBacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field.MethodsWe present a new method for extracting relationships between bacteria and their locations using the Alvis framework. Recognition of bacteria and their locations was achieved using a pattern-based approach and domain lexical resources. For the detection of environment locations, we propose a new approach that combines lexical information and the syntactic-semantic analysis of corpus terms to overcome the incompleteness of lexical resources. Bacteria location relations extend over sentence borders, and we developed domain-specific rules for dealing with bacteria anaphors.ResultsWe participated in the BioNLP 2011 Bacteria Biotope (BB) task with the Alvis system. Official evaluation results show that it achieves the best performance of participating systems. New developments since then have increased the F-score by 4.1 points.ConclusionsWe have shown that the combination of semantic analysis and domain-adapted resources is both effective and efficient for event information extraction in the bacteria biotope domain. We plan to adapt the method to deal with a larger set of location types and a large-scale scientific article corpus to enable microbiologists to integrate and use the extracted knowledge in combination with experimental data.

ATOL: The Multi-species Livestock Trait Ontology

This paper presents the multi-species Animal Trait Ontology for Livestock (ATOL) and the methodology used for its design. ATOL has been designed as a reference source for indexing phenotype databases and scientific papers. It covers five major topics related to animal productions: growth and meat quality, animal nutrition, milk production, reproduction and welfare. It is composed of species-independent concepts subsuming species-specific ones so that cross-species and species-specific reasoning can be performed consistently. In order to ensure a large consensus, three complementary approaches have successively been applied to its design: reuse of existing ontologies, integration of production-specific livestock traits by a large team of domain experts and curators and terminology analysis of scientific papers. It resulted in a detailed taxonomy of 1,654 traits that is available at http://www.atol-ontology.com

Building large lexicalized ontologies from text: a use case in automatic indexing of biotechnology patents

This paper presents a tool, TyDI, and methods experimented in the building of a termino-ontology, i.e. a lexicalized ontology aimed at fine-grained indexation for semantic search applications. TyDI provides facilities for knowledge engineers and domain experts to efficiently collaborate to validate, organize and conceptualize corpus extracted terms. A use case on biotechnology patent search demonstrates TyDIs potential.

Information Extraction from Bibliography for Marker-Assisted Selection in Wheat

Improvement of most animal and plant species of agronomical interest in the near future has become an international stake because of the increasing demand for feeding a growing world population and to mitigate the reduction of the industrial resources. The recent advent of genomic tools contributed to improve the discovery of linkage between molecular markers and genes that are involved in the control of traits of agronomical interest such as grain number or disease resistance. This information is mostly published as scientific papers but rarely available in databases. Here, we present a method aiming at automatically extract this information from the scientific literature and relying on a knowledge model of the target information and on the WheatPhenotype ontology that we developed for this purpose. The information extraction results were evaluated and integrated into the on-line semantic search engine AlvisIR WheatMarker.