Neural Representations of Concepts and Texts for Biomedical Information Retrieval

Information retrieval (IR) methods are an indispensable tool in the current landscape of exponentially increasing textual data, especially on the Web. A typical IR task involves fetching and ranking a set of documents (from a large corpus) in terms of relevance to a user\u27s query, which is often expressed as a short phrase. IR methods are the backbone of modern search engines where additional system-level aspects including fault tolerance, scale, user interfaces, and session maintenance are also addressed. In addition to fetching documents, modern search systems may also identify snippets within the documents that are potentially most relevant to the input query. Furthermore, current systems may also maintain preprocessed structured knowledge derived from textual data as so called knowledge graphs, so certain types of queries that are posed as questions can be parsed as such; a response can be an output of one or more named entities instead of a ranked list of documents (e.g., what diseases are associated with EGFR mutations? ). This refined setup is often termed as question answering (QA) in the IR and natural language processing (NLP) communities. In biomedicine and healthcare, specialized corpora are often at play including research articles by scientists, clinical notes generated by healthcare professionals, consumer forums for specific conditions (e.g., cancer survivors network), and clinical trial protocols (e.g., www.clinicaltrials.gov). Biomedical IR is specialized given the types of queries and the variations in the texts are different from that of general Web documents. For example, scientific articles are more formal with longer sentences but clinical notes tend to have less grammatical conformity and are rife with abbreviations. There is also a mismatch between the vocabulary of consumers and the lingo of domain experts and professionals. Queries are also different and can range from simple phrases (e.g., COVID-19 symptoms ) to more complex implicitly fielded queries (e.g., chemotherapy regimens for stage IV lung cancer patients with ALK mutations ). Hence, developing methods for different configurations (corpus, query type, user type) needs more deliberate attention in biomedical IR. Representations of documents and queries are at the core of IR methods and retrieval methodology involves coming up with these representations and matching queries with documents based on them. Traditional IR systems follow the approach of keyword based indexing of documents (the so called inverted index) and matching query phrases against the document index. It is not difficult to see that this keyword based matching ignores the semantics of texts (synonymy at the lexeme level and entailment at phrase/clause/sentence levels) and this has lead to dimensionality reduction methods such as latent semantic indexing that generally have scale-related concerns; such methods also do not address similarity at the sentence level. Since the resurgence of neural network methods in NLP, the IR field has also moved to incorporate advances in neural networks into current IR methods. This dissertation presents four specific methodological efforts toward improving biomedical IR. Neural methods always begin with dense embeddings for words and concepts to overcome the limitations of one-hot encoding in traditional NLP/IR. In the first effort, we present a new neural pre-training approach to jointly learn word and concept embeddings for downstream use in applications. In the second study, we present a joint neural model for two essential subtasks of information extraction (IE): named entity recognition (NER) and entity normalization (EN). Our method detects biomedical concept phrases in texts and links them to the corresponding semantic types and entity codes. These first two studies provide essential tools to model textual representations as compositions of both surface forms (lexical units) and high level concepts with potential downstream use in QA. In the third effort, we present a document reranking model that can help surface documents that are likely to contain answers (e.g, factoids, lists) to a question in a QA task. The model is essentially a sentence matching neural network that learns the relevance of a candidate answer sentence to the given question parametrized with a bilinear map. In the fourth effort, we present another document reranking approach that is tailored for precision medicine use-cases. It combines neural query-document matching and faceted text summarization. The main distinction of this effort from previous efforts is to pivot from a query manipulation setup to transforming candidate documents into pseudo-queries via neural text summarization. Overall, our contributions constitute nontrivial advances in biomedical IR using neural representations of concepts and texts

Geospatial Semantics

Geospatial semantics is a broad field that involves a variety of research areas. The term semantics refers to the meaning of things, and is in contrast with the term syntactics. Accordingly, studies on geospatial semantics usually focus on understanding the meaning of geographic entities as well as their counterparts in the cognitive and digital world, such as cognitive geographic concepts and digital gazetteers. Geospatial semantics can also facilitate the design of geographic information systems (GIS) by enhancing the interoperability of distributed systems and developing more intelligent interfaces for user interactions. During the past years, a lot of research has been conducted, approaching geospatial semantics from different perspectives, using a variety of methods, and targeting different problems. Meanwhile, the arrival of big geo data, especially the large amount of unstructured text data on the Web, and the fast development of natural language processing methods enable new research directions in geospatial semantics. This chapter, therefore, provides a systematic review on the existing geospatial semantic research. Six major research areas are identified and discussed, including semantic interoperability, digital gazetteers, geographic information retrieval, geospatial Semantic Web, place semantics, and cognitive geographic concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova, and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information Systems, Elsevier. Oxford, U

Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development

This open access book provides the first systematic overview of existing challenges and opportunities for responsible data linkage, and a cutting-edge assessment of which steps need to be taken to ensure that plant data are ethically shared and used for the benefit of ensuring global food security – one of the UN’s Sustainable Development Goals. The volume focuses on the contemporary contours of such challenges through sustained engagement with current and historical initiatives and discussion of best practices and prospective future directions for ensuring responsible plant data linkage. The volume is divided into four sections that include case studies of plant data use and linkage in the context of particular research projects, breeding programs, and historical research. It address technical challenges of data linkage in developing key tools, standards and infrastructures, and examines governance challenges of data linkage in relation to socioeconomic and environmental research and data collection. Finally, the last section addresses issues raised by new data production and linkage methods for the inclusion of agriculture’s diverse stakeholders. This book brings together leading experts in data curation, data governance and data studies from a variety of fields, including data science, plant science, agricultural research, science policy, data ethics and the philosophy, history and social studies of plant science

Ontology-based knowledge management for technology intensive industries

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development

This open access book provides the first systematic overview of existing challenges and opportunities for responsible data linkage, and a cutting-edge assessment of which steps need to be taken to ensure that plant data are ethically shared and used for the benefit of ensuring global food security – one of the UN’s Sustainable Development Goals. The volume focuses on the contemporary contours of such challenges through sustained engagement with current and historical initiatives and discussion of best practices and prospective future directions for ensuring responsible plant data linkage. The volume is divided into four sections that include case studies of plant data use and linkage in the context of particular research projects, breeding programs, and historical research. It address technical challenges of data linkage in developing key tools, standards and infrastructures, and examines governance challenges of data linkage in relation to socioeconomic and environmental research and data collection. Finally, the last section addresses issues raised by new data production and linkage methods for the inclusion of agriculture’s diverse stakeholders. This book brings together leading experts in data curation, data governance and data studies from a variety of fields, including data science, plant science, agricultural research, science policy, data ethics and the philosophy, history and social studies of plant science

Large-Scale Pattern-Based Information Extraction from the World Wide Web

Extracting information from text is the task of obtaining structured, machine-processable facts from information that is mentioned in an unstructured manner. It thus allows systems to automatically aggregate information for further analysis, efficient retrieval, automatic validation, or appropriate visualization. This work explores the potential of using textual patterns for Information Extraction from the World Wide Web

Proceedings, MSVSCC 2018

Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp