Elena Beisswanger

BioTop: An upper domain ontology for the life sciences: A description of its current structure, contents and interfaces to OBO ontologies

In the life sciences, there is an ample need for semantic interoperability of data. Thus shared vocabularies are needed for consistently expressing metadata in terms of semantic annotations as well as for querying bibliographic information systems. In the past years, lots of highly specialized, yet also fragmented terminologies have evolved. However, they lack principled forms of conceptual interlinkage. In order to provide an ontological basis for a seamless integration of such isolated parts of biological knowledge, we here introduce BioTop, an upper domain ontology for molecular biology. We describe its structure and contents, as well as its current interfaces to a selected set of OBO ontologies, which contain more detailed terminological knowledge about specific areas of molecular biology, e.g., cell types, molecular functions, biological processes and chemical compounds.

Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

BackgroundCompetitions in text mining have been used to measure the performance of automatic text processing solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is time-consuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups are chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their text processing solutions.ResultsAll four PPs from the CALBC project and in addition, 12 challenge participants (CPs) contributed annotated data sets for an evaluation against the SSC-I. CPs could ignore the training data and deliver the annotations from their genuine annotation system, or could train a machine-learning approach on the provided pre-annotated data. In general, the performances of the annotation solutions were lower for entities from the categories CHED and PRGE in comparison to the identification of entities categorized as DISO and SPE. The best performance over all semantic groups were achieved from two annotation solutions that have been trained on the SSC-I.The data sets from participants were used to generate the harmonised Silver Standard Corpus II (SSC-II), if the participant did not make use of the annotated data set from the SSC-I for training purposes. The performances of the participants’ solutions were again measured against the SSC-II. The performances of the annotation solutions showed again better results for DISO and SPE in comparison to CHED and PRGE.ConclusionsThe SSC-I delivers a large set of annotations (1,121,705) for a large number of documents (100,000 Medline abstracts). The annotations cover four different semantic groups and are sufficiently homogeneous to be reproduced with a trained classifier leading to an average F-measure of 85%. Benchmarking the annotation solutions against the SSC-II leads to better performance for the CPs’ annotation solutions in comparison to the SSC-I.

The extraction of pharmacogenetic and pharmacogenomic relations--a case study using PharmGKB.

In this paper, we report on adapting the JREX relation extraction engine, originally developed For the elicitation of protein-protein interaction relations, to the domains of pharmacogenetics and pharmacogenomics. We propose an intrinsic and an extrinsic evaluation scenario which is based on knowledge contained in the PharmGKB knowledge base. Porting JREX yields favorable results in the range of 80% F-score for Gene-Disease, Gene-Drug, and Drug-Disease relations.

Towards valid and reusable reference alignments — ten basic quality checks for ontology alignments and their application to three different reference data sets

Identifying relationships between hitherto unrelated entities in different ontologies is the key task of ontology alignment. An alignment is either manually created by domain experts or automatically by an alignment system. In recent years, several alignment systems have been made available, each using its own set of methods for relation detection. To evaluate and compare these systems, typically a manually created alignment is used, the so-called reference alignment. Based on our experience with several of these reference alignments we derived requirements and translated them into simple quality checks to ensure the alignments’ validity and also their reusability. In this article, these quality checks are applied to a standard reference alignment in the biomedical domain, the Ontology Alignment Evaluation Initiative Anatomy track reference alignment, and two more recent data sets covering multiple domains, including but not restricted to anatomy and biology.

Building a BioWordNet by using WordNet's data formats and WordNet's software infrastructure: a failure story

In this paper, we describe our efforts to build on WordNet resources, using WordNet lexical data, the data format that it comes with and WordNets software infrastructure in order to generate a biomedical extension of WordNet, the BioWordNet. We began our efforts on the assumption that the software resources were stable and reliable. In the course of our work, it turned out that this belief was far too optimistic. We discuss the stumbling blocks that we encountered, point out an error in the WordNet software with implications for research based on it, and conclude that building on the legacy of WordNet data structures and its associated software might preclude sustainable extensions that go beyond the domain of general English.

Exploiting relation extraction for ontology alignment

When multiple ontologies are used within one application system, aligning the ontologies is a prerequisite for interoperability and unhampered semantic navigation and search. Various methods have been proposed to compute mappings between elements from different ontologies, the majority of which being based on various kinds of similarity measures. As a major shortcoming of these methods it is difficult to decode the semantics of the results achieved. In addition, in many cases they miss important mappings due to poorly developed ontology structures or dissimilar ontology designs. I propose a complementary approach making massive use of relation extraction techniques applied to broad-coverage text corpora. This approach is able to detect different types of semantic relations, dependent on the extraction techniques used. Furthermore, exploiting external background knowledge, it can detect relations even without clear evidence in the input ontologies themselves.