This paper introduces a new web-based software tool for annotating text, Text Annotation Graphs, or TAG. It provides functionality for representing complex relationships between words and word phrases that are not available in other software tools, including the ability to define and visualize relationships between the relationships themselves (semantic hypergraphs). Additionally, we include an approach to representing text annotations in which annotation subgraphs, or semantic summaries, are used to show relationships outside of the sequential context of the text itself. Users can use these subgraphs to quickly find similar structures within the current document or external annotated documents. Initially, TAG was developed to support information extraction tasks on a large database of biomedical articles. However, our software is flexible enough to support a wide range of annotation tasks for any domain. Examples are provided that showcase TAG's capabilities on morphological parsing and event extraction tasks.
is inspired by the rapid annotation tool @cite_1 . is widely used for representing syntactic structure, but can also represent semantic events, and has been applied to a range of domain-specific NLP tasks, including biomedical data @cite_9 . supports a range of useful features that improve the overall efficiency of manual annotation tasks. However, does not support the ability to draw links between links, which makes it difficult to represent relations linking several predicate-less relations, a feature necessary to completely describe complex events. Complex relations are previously explored by the authors in a range of different visualization projects that represent hierarchically-nested and or clustered data derived from the machine reading of scientific texts describing biochemical events @cite_11 @cite_3 @cite_5 @cite_4 @cite_7 @cite_14 @cite_2 @cite_17 .
@cite_14 @cite_4 @cite_7 @cite_9 @cite_1 @cite_17 @cite_3 @cite_2 @cite_5 @cite_11
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Background Understanding complicated networks of interactions and chemical components is essential to solving contemporary problems in modern biology, especially in domains such as cancer and systems research. In these domains, biological pathway data is used to represent chains of interactions that occur within a given biological process. Visual representations can help researchers understand, interact with, and reason about these complex pathways in a number of ways. At the same time, these datasets offer unique challenges for visualization, due to their complexity and heterogeneity. Systems biologists and cancer researchers require interactive visualization tools that enable them to more easily navigate and discover patterns at different levels of the biological hierarchy of signaling pathways. Furthermore, biologists are often interested in understanding and exploring the causal biochemical links between processes. When exploring the literature of particular biological pathways or specific proteins within those pathways, biologists find it useful to know the contexts in which biochemical links are active and, importantly, to be aware of potential conflicts when different experiments introduce alternative interpretations of the function of a pathway or biochemical reaction. We introduce BioLinker, a interactive visualization system that helps users to perform bottom-up exploration of complex protein interaction networks. Five interconnected views provide the user with a range of ways to explore pathway data, including views that show potential conflicts within pathway databases and publications and that highlight contextual information about individual proteins. Additionally, we discuss system details to show how our system manages the large amount of protein interactions extracted from the literature of biological pathways. We introduce the brat rapid annotation tool (BRAT), an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annotation for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. We discuss several case studies of real-world annotation projects using pre-release versions of BRAT and present an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task, showing a 15 decrease in total annotation time. BRAT is available under an open-source license from: http: brat.nlplab.org Node-link diagrams are widely used for visualizing relational data in a wide range of fields. However, in many situations it is useful to provide set membership information for elements in networks. We present BranchingSets, an interactive visualization technique that uses visual encodings similar to Kelp Diagrams in order to augment traditional node-link diagrams with information about the categories that both nodes and links belong to. BranchingSets introduces novel user-driven methods to procedurally navigate the graph topology and to interactively inspect complex, hierarchical data associated with individual nodes. Results indicate that users find the technique engaging and easy to use. This is further confirmed by a quantitative study that compares the effectiveness of the visual encodings used in BranchingSets to other techniques for displaying set membership within node-link diagrams, finding our technique more accurate and more efficient for facilitating interactive queries on networks containing nodes that belong to multiple sets. Background Biologists make use of pathway visualization tools for a range of tasks, including investigating inter-pathway connectivity and retrieving details about biological entities and interactions. Some of these tasks require an understanding of the hierarchical nature of elements within the pathway or the ability to make comparisons between multiple pathways. We introduce a technique inspired by LineSets that enables biologists to fulfill these tasks more effectively. This paper introduces CactusTree, a novel visualization technique for representing hierarchical datasets. We introduce details about the construction of CactusTrees and describe how they can be used to represent nested data and relationships between elements in the data. We explain how our design decisions were informed by tasks common to a range of scientific domains. A key contribution of this article is the introduction of descriptive features that can be used to characterize trees in terms of their structural and connective qualities. Background Molecular and systems biologists are tasked with the comprehension and analysis of incredibly complex networks of biochemical interactions, called pathways, that occur within a cell. Through interviews with domain experts, we identified four common tasks that require an understanding of the causality within pathways, that is, the downstream and upstream relationships between proteins and biochemical reactions, including: visualizing downstream consequences of perturbing a protein; finding the shortest path between two proteins; detecting feedback loops within the pathway; and identifying common downstream elements from two or more proteins.