In the medical domain, the continuous stream of scientific research contains contradictory results supported by arguments and counter-arguments. As medical expertise occurs at different levels, part of the human agents have difficulties to face the huge amount of studies, but also to understand the reasons and pieces of evidences claimed by the proponents and the opponents of the debated topic. To better understand the supporting arguments for new findings related to current state of the art in the medical domain we need tools able to identify arguments in scientific papers. Our work here aims to fill the above technological gap.
Quite aware of the difficulty of this task, we embark to this road by relying on the well-known interleaving of domain knowledge with natural language processing. To formalise the existing medical knowledge, we rely on ontologies. To structure the argumentation model we use also the expressivity and reasoning capabilities of Description Logics. To perform argumentation mining we formalise various linguistic patterns in a rule-based language. We tested our solution against a corpus of scientific papers related to breast cancer. The run experiments show a F-measure between 0.71 and 0.86 for identifying conclusions of an argument and between 0.65 and 0.86 for identifying premises of an argument.
Formal Concept Analysis (FCA) is a mathematical theory based on the formalization of the notions of concept and concept hierarchies. It has been successfully applied to several Computer Science fields such as data mining,software engineering, and knowledge engineering, and in many domains like medicine, psychology, linguistics and ecology. For instance, it has been exploited for the design, mapping and refinement of ontologies. In this paper, we show how FCA can benefit from a given domain ontology by analyzing the impact of a taxonomy (on objects and/or attributes) on the resulting concept la…
The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies.…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Arizona（亚利桑那大学）；Mississippi State University（密西西比州立大学）
Community annotation of biological entities with concepts from multiple bio-ontologies has created large and growing repositories of ontology-based annotation data with embedded implicit relationships among orthogonal ontologies. Development of efficient data mining methods and metrics to mine and assess the quality of the mined relationships has not kept pace with the growth of annotation data. In this study, we present a data mining method that uses ontology-guided generalization to discover relationships across ontologies along with a new interestingness metric based on information theory. …
Microbiology research has access to a very large amount of public information on the habitats of microorganisms. Many areas of microbiology research uses this information, primarily in biodiversity studies. However the habitat information is expressed in unstructured natural language form, which hinders its exploitation at large-scale. It is very common for similar habitats to be described by different terms, which makes them hard to compare automatically, e.g. intestine and gut. The use of a common reference to standardize these habitat descriptions as claimed by (Ivana et al., 2010) is a nec…
In this paper we dealt with the comparison and linking between lexical resources with domain knowledge provided by ontologies. It is one of the issues for the combination of the Semantic Web Ontologies and Text Mining. We investigated the relations between the linguistics oriented and domain-specific semantics, by associating the GO biological process concepts to the FrameNet semantic frames. The result shows the gaps between the linguistics-oriented and domain-specific semantics on the classification of events and the grouping of target words. The result provides valuable information for the …
Université de Lorraine, CNRS, Inria, LORIA（洛林大学、法国国家科学研究中心、法国国家信息与自动化研究所、洛林计算机科学与应用研究实验室）
Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining ap…
Stanford University（斯坦福大学）；Graz University of Technology（格拉茨技术大学）；GESIS（莱布尼茨社会科学研究所）；University of Koblenz-Landau（科布伦茨-兰道大学）
Ontologies in the biomedical domain are numerous, highly specialized and very expensive to develop. Thus, a crucial prerequisite for ontology adoption and reuse is effective support for exploring and finding existing ontologies. Towards that goal, the National Center for Biomedical Ontology (NCBO) has developed BioPortal---an online repository designed to support users in exploring and finding more than 500 existing biomedical ontologies. In 2016, BioPortal represents one of the largest portals for exploration of semantic biomedical vocabularies and terminologies, which is used by many researc…
Peking University Shenzhen Graduate School（北京大学深圳研究生院）；Université du Havre（勒阿弗尔大学）；Université Paris Ouest（巴黎第十大学（巴黎西部大学））；IER Business Development Center（IER业务发展中心）
Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision s…
We propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular we consider the setting of using a language that combines description logics with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data m…
University of Leipzig（莱比锡大学）；Leipzig University of Applied Sciences（莱比锡应用科技大学）；Paderborn University（帕德博恩大学）
Graph representations of large knowledge bases may comprise billions of edges. Usually built upon human-generated ontologies, several knowledge bases do not feature declared ontological rules and are far from being complete. Current rule mining approaches rely on schemata or store the graph in-memory, which can be unfeasible for large graphs. In this paper, we introduce HornConcerto, an algorithm to discover Horn clauses in large graphs without the need of a schema. Using a standard fact-based confidence score, we can mine close Horn rules having an arbitrary body size. We show that our method…
Université Paris 8（巴黎第八大学）；IRCAM（法国音响与音乐研究和协作学院）；CReSTIC de l’Université de Reims（兰斯大学CReSTIC研究所）
Traditional staging is based on a formal approach of similarity leaning on dramaturgical ontologies and instanciation variations. Inspired by interactive data mining, that suggests different approaches, we give an overview of computer science and theater researches using computers as partners of the actor to escape the a priori specification of roles.
The ever-increasing amount of data in biomedical research, and in cancer research in particular, needs to be managed to support efficient data access, exchange and integration. Existing software infrastructures, such caGrid, support access to distributed information annotated with a domain ontology. However, caGrid's current querying functionality depends on the structure of individual data resources without exploiting the semantic annotations. In this paper, we present the design and development of an ontology-based querying functionality that consists of: the generation of OWL2 ontologies fr…
Responsible Integration of AI in Cancer Genomics: Barriers, Risks, and Pathways to Trustworthy Clinical Translation
Robert Koch Institute（罗伯特·科赫研究所）；European Commission（欧洲委员会）；Deree-The American College of Greece（希腊 Deree 美国学院）；Åbo Akademi University（奥布学术大学）；Arcada University of Applied Sciences（阿卡达应用科学大学）；University of Münster（明斯特大学）
One of the main problems that emerges in the classic approach to semantics is the difficulty in acquisition and maintenance of ontologies and semantic annotations. On the other hand, the Internet explosion and the massive diffusion of mobile smart devices lead to the creation of a worldwide system, which information is daily checked and fueled by the contribution of millions of users who interacts in a collaborative way. Search engines, continually exploring the Web, are a natural source of information on which to base a modern approach to semantic annotation. A promising idea is that it is po…
RWTH Aachen University（亚琛工业大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；ZBMED - Information Center for Life Sciences（ZB MED——生命科学信息中心）；University of Cologne（科隆大学）
Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A domain-specific knowledge graph~(KG) is an explicit conceptualization of a specific subject-matter domain represented w.r.t semantically interrelated entities and relations. A KG can be constructed by integrating such facts and data and be used for data integration, exploration, and…
Graz University of Technology（格拉茨工业大学）；GESIS - Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）；University of Koblenz-Landau（科布伦茨-兰道大学）；Stanford University（斯坦福大学）
Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This…
Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate Knowledge Base entries based on their similarity with disease mention. We …
In recent years, data science has evolved significantly. Data analysis and mining processes become routines in all sectors of the economy where datasets are available. Vast data repositories have been collected, curated, stored, and used for extracting knowledge. And this is becoming commonplace. Subsequently, we extract a large amount of knowledge, either directly from the data or through experts in the given domain. The challenge now is how to exploit all this large amount of knowledge that is previously known for efficient decision-making processes. Until recently, much of the knowledge gai…
Fraunhofer Institute for Algorithms and Scientific Computing（弗劳恩霍夫算法与科学计算研究所）；University of Cologne（科隆大学）
Here we present a holistic approach for data exploration on dense knowledge graphs as a novel approach with a proof-of-concept in biomedical research. Knowledge graphs are increasingly becoming a vital factor in knowledge mining and discovery as they connect data using technologies from the semantic web. In this paper we extend a basic knowledge graph extracted from biomedical literature by context data like named entities and relations obtained by text mining and other linked data sources like ontologies and databases. We will present an overview about this novel network. The aim of this work…
National Centre for Text Mining（国家文本挖掘中心）；Manchester Centre for Genomic Medicine（曼彻斯特基因组医学中心）；St Mary’s Hospital（圣玛丽医院）；The University of Manchester（曼彻斯特大学）；Manchester University Hospitals NHS Foundation Trust（曼彻斯特大学医院 NHS 基金会信托）；School of Biological Sciences（生物科学学院）；Faculty of Biology, Medicine and Health（生物、医学与健康学部）；School of Medical Sciences（医学科学学院）；Christie NHS Foundation Trust（克里斯蒂 NHS 基金会信托）
To interpret the genetic profile present in a patient sample, it is necessary to know which mutations have important roles in the development of the corresponding cancer type. Named entity recognition is a core step in the text mining pipeline which facilitates mining valuable cancer information from the scientific literature. However, due to the scarcity of related datasets, previous NER attempts in this domain either suffer from low performance when deep learning based models are deployed, or they apply feature based machine learning models or rule based models to tackle this problem, which …
Rapid growth of documents, web pages, and other types of text content is a huge challenge for the modern content management systems. One of the problems in the areas of information storage and retrieval is the lacking of semantic data. Ontologies can present knowledge in sharable and repeatedly usable manner and provide an effective way to reduce the data volume overhead by encoding the structure of a particular domain. Metadata in relational databases can be used to extract ontology from database in a special domain. According to solve the problem of sharing and reusing of data, approaches ba…
LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Models
TIB Leibniz Information Centre for Science and Technology（莱布尼茨科学与技术信息中心（TIB））；TU/e Eindhoven University of Technology（埃因霍温理工大学）；University of Warwick（华威大学）；University of Brescia（布雷西亚大学）；L3S Research Center（L3S研究中心）
Classification is one of the major issues in Data Mining Research fields. The classification problems in medical area often classify medical dataset based on the result of medical diagnosis or description of medical treatment by the medical practitioner. This research work discusses the classification process of Gene Expression data for three different cancers which are breast cancer, lung cancer and leukemia cancer with two classes which are cancerous stage and non cancerous stage. We have applied a fuzzy soft set similarity based classifier to enhance the accuracy to predict the stages among…
A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms
Cairo University（开罗大学）；Scientific Academy for Service Technology e.V. (ServTech)（服务技术科学协会（ServTech））；Egypt University of Informatics（埃及信息学大学）；University of New South Wales（新南威尔士大学）
In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMAKA, a system that explores the variations within the case base to elicit adaptation knowledge. This system …
Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints
University of Oxford（牛津大学）；NLPie Research（NLPie研究院）；Infectious Diseases Data Observatory (IDDO)（传染病数据观测站（IDDO））；Sharif University of Technology（谢里夫理工大学）；ISARIC（国际严重急性呼吸道感染联盟）；Pandemic Sciences Institute（大流行科学研究所）；Oxford-Suzhou Centre for Advanced Research（牛津-苏州高等研究中心）
