MOTIVATION: Human Phenotype Ontology (HPO)-based phenotype concept recognition (CR) underpins a faster and more effective mechanism to create patient phenotype profiles or to document novel phenotype-centred knowledge statements. While the increasing adoption of large language models (LLMs) for natural language understanding has led to several LLM-based solutions, we argue that their intrinsic resource-intensive nature is not suitable for realistic management of the phenotype CR lifecycle. Consequently, we propose to go back to the basics and adopt a dictionary-based approach that enables both an immediate refresh of the ontological concepts as well as efficient re-analysis of past data. RESULTS: We developed a dictionary-based approach using a pre-built large collection of clusters of morphologically equivalent tokens-to address lexical variability and a more effective CR step by reducing the entity boundary detection strictly to candidates consisting of tokens belonging to ontology concepts. Our method achieves state-of-the-art results (0.76 F1 on the GSC+ corpus) and a processing efficiency of 10 000 publication abstracts in 5 s. AVAILABILITY AND IMPLEMENTATION: FastHPOCR is available as a Python package installable via pip. The source code is available at https://github.com/tudorgroza/fast_hpo_cr. A Java implementation of FastHPOCR will be made available as part of the Fenominal Java library available at https://github.com/monarch-initiative/fenominal. The up-to-date GCS-2024 corpus is available at https://github.com/tudorgroza/code-for-papers/tree/main/gsc-2024.

MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. AVAILABILITY AND IMPLEMENTATION: Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

Automatically extracting phenotypes (i.e., the composite of ones observable characteristics/traits) from free text such as scientific literature or clinical notes and associating phenotypes with diseases is an important task. Such associations can be used in, for example, recommending candidate genes for diseases, investigating drug targets, or performing differential diagnosis. In this paper, we focus on extracting disease-phenotype relations with association rule mining techniques and compare results with two other methods. We show that association rule mining offers promising alternative method for detecting disease-phenotype relations.

Phenotypes have gained increased notoriety in the clinical and biological domain owing to their application in numerous areas such as the discovery of disease genes and drug targets, phylogenetics and pharmacogenomics. Phenotypes, defined as observable characteristics of organisms, can be seen as one of the bridges that lead to a translation of experimental findings into clinical applications and thereby support 'bench to bedside' efforts. However, to build this translational bridge, a common and universal understanding of phenotypes is required that goes beyond domain-specific definitions. To achieve this ambitious goal, a digital revolution is ongoing that enables the encoding of data in computer-readable formats and the data storage in specialized repositories, ready for integration, enabling translational research. While phenome research is an ongoing endeavor, the true potential hidden in the currently available data still needs to be unlocked, offering exciting opportunities for the forthcoming years. Here, we provide insights into the state-of-the-art in digital phenotyping, by means of representing, acquiring and analyzing phenotype data. In addition, we provide visions of this field for future research work that could enable better applications of phenotype data.

Ontologies have become a central resource for defining biomedical concepts but linkage to and from textual data is still an unresolved technology. In this paper we approach the task of concept recognition in text by comparing four extant systems (cTAKES, NCBO Annotator, BeCAS and Metamap) with default parameter settings. The systems are compared on benchmark data consisting of 2,163 scientific abstracts and 906 clinical trial reports using an automatically constructed “silver” standard and a random semi-gold standard evaluation methodology. Furthermore, evaluation is conducted on the basis of specific concept identifiers. Experimental results show: (i) Generally higher levels of concept recognition on clinical trial reports than on scientific abstracts; (ii) The best performing system we observed on the silver standard was cTAKES on both the abstract and clinical trial corpora, however NCBO Annotator performed stronger when considering only the selected broad semantic types; (iii) BeCAS and Metamap had a tendency to annotate coarser-grained annotations; (iv) the random semi-gold evaluation places an upper bound on the performance of systems. This shows broad agreement with the silver standard evaluation but highlights areas where the silver standard methodology might be improved.

BACKGROUND: Skeletal dysplasias are a rare and heterogeneous group of genetic disorders affecting skeletal development. Patients with skeletal dysplasias suffer from many complex medical issues including degenerative joint disease and neurological complications. Because the data and expertise associated with this field is both sparse and disparate, significant benefits will potentially accrue from the availability of an ontology that provides a shared conceptualisation of the domain knowledge and enables data integration, cross-referencing and advanced reasoning across the relevant but distributed data sources. RESULTS: We introduce the design considerations and implementation details of the Bone Dysplasia Ontology. We also describe the different components of the ontology, including a comprehensive and formal representation of the skeletal dysplasia domain as well as the related genotypes and phenotypes. We then briefly describe SKELETOME, a community-driven knowledge curation platform that is underpinned by the Bone Dysplasia Ontology. SKELETOME enables domain experts to use, refine and extend and apply the ontology without any prior ontology engineering experience--to advance the body of knowledge in the skeletal dysplasia field. CONCLUSIONS: The Bone Dysplasia Ontology represents the most comprehensive structured knowledge source for the skeletal dysplasias domain. It provides the means for integrating and annotating clinical and research data, not only at the generic domain knowledge level, but also at the level of individual patient case studies. It enables links between individual cases and publicly available genotype and phenotype resources based on a community-driven curation process that ensures a shared conceptualisation of the domain knowledge and its continuous incremental evolution.

BACKGROUND: Over the course of the last few years there has been a significant amount of research performed on ontology-based formalization of phenotype descriptions. In order to fully capture the intrinsic value and knowledge expressed within them, we need to take advantage of their inner structure, which implicitly combines qualities and anatomical entities. The first step in this process is the segmentation of the phenotype descriptions into their atomic elements. RESULTS: We present a two-phase hybrid segmentation method that combines a series individual classifiers using different aggregation schemes (set operations and simple majority voting). The approach is tested on a corpus comprised of skeletal phenotype descriptions emerged from the Human Phenotype Ontology. Experimental results show that the best hybrid method achieves an F-Score of 97.05% in the first phase and F-Scores of 97.16% / 94.50% in the second phase. CONCLUSIONS: The performance of the initial segmentation of anatomical entities and qualities (phase I) is not affected by the presence / absence of external resources, such as domain dictionaries. From a generic perspective, hybrid methods may not always improve the segmentation accuracy as they are heavily dependent on the goal and data characteristics.

Presence management, i.e., the ability to automatically identify the status and availability of communication partners, is becoming an invaluable tool for collaboration in enterprise contexts. In this paper, we argue for efficient presence management by means of a holistic view of both physical cont

Argumentative discussions are common in Web 2.0 applications, but the social Web still offers limited or no explicit support for argumentation. As Web 2.0 applications become more popular, modeling argumentation happening in these systems becomes important, to enable reuse and further understanding of online discussions. After reviewing four genres of online conversations – Web bulletin boards, Wiki talk pages, blog comments, and microblogs – and four current Web 2.0 argumentation systems, the paper suggests how Semantic Web technologies can be used to provide an interoperability layer for argumentation modeling across applications.

Using the current state of the art in life science publication search (e.g., PubMed), one can efficiently search for resources containing particular key-words or their combinations. It is impossible to search for abstract concepts and expressive relations between them (e.g., type of, different from or part of), though. Nevertheless, such a more expressive - semantic - search could largely reduce the efforts related to finding appropriate answers in biomedical articles. In this paper we identify challenges related to building a semantic publication search engine. Then we describe the architecture and usage principles of a tool tackling them. Eventually, we report on the tool's deployment on oncological literature data and preliminary tests with domain experts.

Classic argumentative discussions can be found in a variety of domains from traditional scientific publishing to today's modern social software. An interactive argumentative discussion usually consists of an initial proposition stated by a single creator, followed by supporting propositions or counter-propositions from other contributors. Thus, the actual argumentation semantics is hidden in the content created by the contributors. Although there are approaches that try to deal with this challenge, most of them focus on a particular domain, limiting the scope of the argumentation to that domain only. In this paper, we describe an abstract model for argumentation which captures the semantics independently of the domain. Following a modularized approach, we also take into account additional important aspects of the argumentation, like the provenance information or its evolution (the temporal side).

In this paper we present the SALT (Semantically Annotated LATEX) Triple, a set of tools built to demonstrate a complete annotation workflow from creation to usage. The Triple set contains the authoring and annotation framework, an editor and a web publisher which helps the generation or uses the generated metadata for a specific purpose. The demos show three phases part of the workflow: (i) authoring -- first we introduce the way in which concurrent annotations can be created during the authoring process by using the iSALT editor as a front-end for the SALT framework; (ii) generation -- then we show how the metadata is generated and embedded into the final result of the authoring and annotation proces, i.e. a semantically enriched PDF document; (iii) usage -- and finally we demostrate a way how the metadata can be used for generating a set of rich online workshop proceedings.

The structure of a document has an important influence on the perception of its content. Considering scientific publications, we can affirm that by making use of the ordinary linear layout, a well organized publication, following a "red wire", will always be better understood and analyzed than one having a poor or chaotic structure, but not necessarily poor content. Reading a publication in a linear way, from the first page to the last page means a lot of unnecessary information processing to the reader. Looking at a publication from another perspective by accessing the key-points or argumentative structure directly can give better insights into the author's thoughts, and for certain tasks (i.e. getting a first impression of an article) a representation of the document reduced to its core could be more important than its linear structure. In this paper, we will show how one can build different representations of the same document, by exploiting the semantics captured in the text. The focus will be on scientific publications and as building foundation we use the SALT (Semantically Annotated LATEX) annotation framework for creating Semantic PDF Documents.