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. We apply our data mining algorithm and interestingness measures to datasets from the Gene Expression Database at the Mouse Genome Informatics as a preliminary proof of concept to mine relationships between developmental stages in the mouse anatomy ontology and Gene Ontology concepts (biological process, molecular function and cellular component). In addition, we present a comparison of our interestingness metric to four existing metrics. Ontology-based annotation datasets provide a valuable resource for discovery of relationships across ontologies. The use of efficient data mining methods and appropriate interestingness metrics enables the identification of high quality relationships.
A service mining framework is proposed that enables discovering interesting relationships in Internet of Things services bottom-up. The service relationships are modeled based on spatial-temporal aspects, environment, people, and operation. An ontology-based service model is proposed to describe services. We present a set of metrics to evaluate the interestingness of discovered service relationships. Analytical and simulation results are presented to show the effectiveness of the proposed evaluation measures.
Motivated by the increased need for formalized representations of the domain of Data Mining, the success of using Formal Concept Analysis (FCA) and Ontology in several Computer Science fields, we present in this paper a new approach for automatic generation of Fuzzy Ontology of Data Mining (FODM), through the fusion of conceptual clustering, fuzzy logic, and FCA. In our approach, we propose to generate ontology taking in consideration another degree of granularity into the process of generation. Indeed, we suggest to define an ontology between classes resulting from a preliminary classificatio…
Nowadays, a huge amount of knowledge has been amassed in digital agriculture. This knowledge and know-how information are collected from various sources, hence the question is how to organise this knowledge so that it can be efficiently exploited. Although this knowledge about agriculture practices can be represented using ontology, rule-based expert systems, or knowledge model built from data mining processes, the scalability still remains an open issue. In this study, we propose a knowledge representation model, called an ontology-based knowledge map, which can collect knowledge from differe…
EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages
Yale University（耶鲁大学）；Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland Clinic（克利夫兰医学中心勒纳医学院，克利夫兰医学中心）；Medical Oncology, Yale School of Medicine（耶鲁医学院医学肿瘤学）
The proposed methodology is procedural i.e. it follows finite number of steps that extracts relevant documents according to users query. It is based on principles of Data Mining for analyzing web data. Data Mining first adapts integration of data to generate warehouse. Then, it extracts useful information with the help of algorithm. The task of representing extracted documents is done by using Vector Based Statistical Approach that represents each document in set of Terms.
Montclair State University（蒙特克莱尔州立大学）；Covenant University（圣约大学）
The ability of a system to meet its requirements is a strong determinant of success. Thus effective requirements specification is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure of software systems is the presence of unhandled IMRs. Since relevance of IMRs is important for efficient system functionality, there are methods developed to aid the identification and management of IM…
In this study, we present Swift Linked Data Miner, an interruptible algorithm that can directly mine an online Linked Data source (e.g., a SPARQL endpoint) for OWL 2 EL class expressions to extend an ontology with new SubClassOf: axioms. The algorithm works by downloading only a small part of the Linked Data source at a time, building a smart index in the memory and swiftly iterating over the index to mine axioms. We propose a transformation function from mined axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment, that most of the axioms mined by Swift Linked Data Miner a…
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…
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…
Technische Universität Berlin（柏林工业大学）；Sapienza University of Rome（罗马第一大学）
Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. The state-of-the-art approaches of knowledge graph embeddings and/or rule mining and reasoning are data-driven and, thus, solely based on the information the input knowledge graph contains. This leads to unsatisfactory prediction results which make such solutions inapplicable to crucial domains such as healthcare. To further enhance the accuracy of knowledge graph completion…
Understanding and visualizing human discourse has long being a challenging task. Although recent work on argument mining have shown success in classifying the role of various sentences, the task of recognizing concepts and understanding the ways in which they are discussed remains challenging. Given an email thread or a transcript of a group discussion, our task is to extract the relevant concepts and understand how they are referenced and re-referenced throughout the discussion. In the present work, we present a preliminary approach for extracting and visualizing group discourse by adapting W…
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…
University of São Paulo, Institute of Mathematical and Computer Sciences（圣保罗大学数学与计算机科学研究所）
Visual Analytics might be defined as data mining assisted by interactive visual interfaces. The field has been receiving prominent consideration by researchers, developers and the industry. The literature, however, is complex because it involves multiple fields of knowledge and is considerably recent. In this article we describe an initial tentative organization of the knowledge in the field as an OWL ontology and a SKOS vocabulary. This effort might be useful in many ways that include conceptual considerations and software implementations. Within the results and discussions, we expose a core …
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent …
Recent machine learning approaches have been effective in Artificial Intelligence (AI) applications. They produce robust results with a high level of accuracy. However, most of these techniques do not provide human-understandable explanations for supporting their results and decisions. They usually act as black boxes, and it is not easy to understand how decisions have been made. Explainable Artificial Intelligence (XAI), which has received much interest recently, tries to provide human-understandable explanations for decision-making and trained AI models. For instance, in digital agriculture,…
Université du Québec en Outaouais（魁北克大学乌塔韦分校）；HEC Montreal（蒙特利尔高等商学院）
Discovering meaningful conceptual structures is a substantial task in data mining and knowledge discovery applications. While off-the-shelf interestingness indices defined in Formal Concept Analysis may provide an effective relevance evaluation in several situations, they frequently give inadequate results when faced with massive formal contexts (and concept lattices), and in the presence of irrelevant concepts. In this paper, we introduce the Conceptual Relevance (CR) score, a new scalable interestingness measurement for the identification of actionable concepts. From a conceptual perspective…
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.…
Ontology-aligned structuring and reuse of multimodal materials data and workflows towards automatic reproduction
Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-Universität Bochum（波鸿鲁尔大学先进材料模拟跨学科中心）；Max Planck Institute for Sustainable Materials（马克斯·普朗克可持续材料研究所）；BAM Federal Institute for Materials Research and Testing（BAM联邦材料研究与测试研究所）
Wikipedia is a goldmine of information; not just for its many readers, but also for the growing community of researchers who recognize it as a resource of exceptional scale and utility. It represents a vast investment of manual effort and judgment: a huge, constantly evolving tapestry of concepts and relations that is being applied to a host of tasks. This article provides a comprehensive description of this work. It focuses on research that extracts and makes use of the concepts, relations, facts and descriptions found in Wikipedia, and organizes the work into four broad categories: applying …
Nanjing University of Information Science and Technology（南京信息工程大学）；Aalto University（阿尔托大学）；Monash University（莫纳什大学）
This paper presents a framework for exact discovery of the top-k sequential patterns under Leverage. It combines (1) a novel definition of the expected support for a sequential pattern - a concept on which most interestingness measures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for the exact discovery of top-k sequential patterns under a given measure of interest. Our interestingness measure employs the partition approach. A pattern is interesting to the extent that it is more frequent than can be explained by assuming independence between any of the pairs of patterns fr…
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…
Huazhong Agricultural University（华中农业大学）；Hubei HongShan Laboratory（湖北洪山实验室）；University of South Australia（南澳大学）；Wuhan University（武汉大学）；Macro Agricultural Research Institute（宏观农业研究院）
A critical step in sharing semantic content online is to map the structural data source to a public domain ontology. This problem is denoted as the Relational-To-Ontology Mapping Problem (Rel2Onto). A huge effort and expertise are required for manually modeling the semantics of data. Therefore, an automatic approach for learning the semantics of a data source is desirable. Most of the existing work studies the semantic annotation of source attributes. However, although critical, the research for automatically inferring the relationships between attributes is very limited. In this paper, we pro…
Université Sorbonne Paris Nord（索邦巴黎西岱大学）；Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）；Laboratoire de Recherche en Informatique, CNRS/Université Paris-Sud/Université Paris-Saclay（计算机研究实验室，法国国家科学研究中心/巴黎南大学/巴黎萨克雷大学）
Clinical trials are the basis of Evidence-Based Medicine. Trial results are reviewed by experts and consensus panels for producing meta-analyses and clinical practice guidelines. However, reviewing these results is a long and tedious task, hence the meta-analyses and guidelines are not updated each time a new trial is published. Moreover, the independence of experts may be difficult to appraise. On the contrary, in many other domains, including medical risk analysis, the advent of data science, big data and visual analytics allowed moving from expert-based to fact-based knowledge. Since 12 yea…
National University of Computer and Emerging Sciences（国立计算机与新兴科学大学）；Hamdard University（哈姆达德大学）
Mobile Ad hoc Network (MANET) is an infrastructure-less network formed between a set of mobile nodes. The discovery of services in MANET is a challenging job due to the unique properties of network. In this paper, a novel service discovery framework called Hybrid Association Rules Based Network Layer Discovery of Services for Ad hoc Networks (HANDY) has been proposed. HANDY provides three major research contributions. At first, it adopts a cross-layer optimized design for discovery of services that is based on simultaneous discovery of services and corresponding routes. Secondly, it provides a…
Technical and Vocational University（技术与职业大学）；Iran University of Science and Technology（伊朗科技大学）
As the volume of data on the web grows, the web structure graph, which is a graph representation of the web, continues to evolve. The structure of this graph has gradually shifted from content-based to non-content-based. Furthermore, spam data, such as noisy hyperlinks, in the web structure graph adversely affect the speed and efficiency of information retrieval and link mining algorithms. Previous works in this area have focused on removing noisy hyperlinks using structural and string approaches. However, these approaches may incorrectly remove useful links or be unable to detect noisy hyperl…
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…
Fusion is a common tool for the analysis and utilization of available datasets and so an essential part of data mining and machine learning processes. However, a clear definition of the type of fusion is not always provided due to inconsistent literature. In the following, the process of fusion is defined depending on the fusion components and the abstraction level on which the fusion occurs. The focus in the first part of the paper at hand is on the clear definition of the terminology and the development of an appropriate ontology of the fusion components and the fusion level. In the second p…
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学及其应用实验室）；Orange Labs（奥兰治实验室）；LIMICS（LIMICS实验室）；Inserm（法国国家健康与医学研究院）；Université Sorbonne Paris Nord（索邦巴黎北大学）；Sorbonne Université（索邦大学）；CHU de St Etienne（圣艾蒂安大学附属医院）；NGERE（NGERE实验室）；Centre Régional de Pharmacovigilance（区域药物警戒中心）；CHRU de Nancy（南锡大学区域医院中心）；Inria Paris（法国国家信息与自动化研究所巴黎中心）；Université de Paris（巴黎大学）
Adverse Drug Reactions (ADRs) are characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. Aside from clinical trials, many elements of knowledge about drug ingredients are available in open-access knowledge graphs. In addition, drug classifications that label drugs as either causative or not for several ADRs, have been established. We propose to mine knowledge graphs for identifying biomolecular features that may enable reproducing automatically expert classifications that distinguish drug causative or no…
University of Oran1（奥兰第一大学）；LIRMM（蒙彼利埃计算机科学、机器人学与微电子实验室）；University of Montpellier（蒙彼利埃大学）；IMT Atlantique（大西洋国立高等矿业电信学校）
Discovering relevant patterns for a particular user remains a challenging tasks in data mining. Several approaches have been proposed to learn user-specific pattern ranking functions. These approaches generalize well, but at the expense of the running time. On the other hand, several measures are often used to evaluate the interestingness of patterns, with the hope to reveal a ranking that is as close as possible to the user-specific ranking. In this paper, we formulate the problem of learning pattern ranking functions as a multicriteria decision making problem. Our approach aggregates differe…
The University of Texas at Austin（得克萨斯大学奥斯汀分校）；AbbVie Inc.（艾伯维公司）；Indiana University（印第安纳大学）
Knowledge Graphs have been one of the fundamental methods for integrating heterogeneous data sources. Integrating heterogeneous data sources is crucial, especially in the biomedical domain, where central data-driven tasks such as drug discovery rely on incorporating information from different biomedical databases. These databases contain various biological entities and relations such as proteins (PDB), genes (Gene Ontology), drugs (DrugBank), diseases (DDB), and protein-protein interactions (BioGRID). The process of semantically integrating heterogeneous biomedical databases is often riddled w…
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；University of New Mexico（新墨西哥大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；Advanced Research in Cyber Systems, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室网络系统高级研究部）；Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）
Mining Java Memory Errors using Subjective Interesting Subgroups with Hierarchical Targets
Univ Lyon, INSA Lyon, CNRS, LIRIS UMR 5205（里昂大学、里昂国立应用科学学院、法国国家科学研究中心、里昂信息与图像信息系统实验室）；Infologic（Infologic公司）；Ecole Nationale Supérieure de Rennes（雷恩国立高等学院）；EPITA Research Laboratory (LRE)（EPITA研究实验室（LRE））
This paper introduces a new similarity measure based on edge counting in a taxonomy like WorldNet or Ontology. Measurement of similarity between text segments or concepts is very useful for many applications like information retrieval, ontology matching, text mining, and question answering and so on. Several measures have been developed for measuring similarity between two concepts: out of these we see that the measure given by Wu and Palmer [1] is simple, and gives good performance. Our measure is based on their measure but strengthens it. Wu and Palmer [1] measure has a disadvantage that it …
