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Observing LOD: Its knowledge Domains and the Varying Behavior of Ontologies Across Them

IEEE ACCESS 2023年 11卷 21127-21143页

作者： Asprino, Luigi Presutti, Valentina Univ Bologna Dept Modern Languages Literatures & Cultures I-40126 Bologna Italy

Linked Open data (LOD) is the largest, collaborative, distributed, and publicly-accessible knowledge Graph (KG) uniformly encoded in the Resource Description Framework (RDF) and formally represented according to the semantics of the Web Ontology Language (OWL). LOD provides researchers with a unique opportunity to study knowledge engineering as an empirical science: to observe existing modelling practices and possibly understanding how to improve knowledge engineering methodologies and knowledge representation formalisms. Following this perspective, several studies have analysed LOD to identify (mis-)use of OWL constructs or other modelling phenomena e.g. class or property usage, their alignment, the average depth of taxonomies. A question that remains open is whether there is a relation between observed modelling practices and knowledge domains (natural science, linguistics, etc.): do certain practices or phenomena change as the knowledge domain varies? Answering this question requires an assessment of the domains covered by LOD as well as a classification of its datasets. Existing approaches to classify LOD datasets provide partial and unaligned views, posing additional challenges. In this paper, we introduce a classification of knowledge domains, and a method for classifying LOD datasets and ontologies based on it. We classify a large portion of LOD and investigate whether a set of observed phenomena have a domain-specific character.

关键词： Ontologies Intelligent systems Web services Semantic Web knowledge engineering data engineering Linked data Open data Intelligent web services and semantic web knowledge engineering methodologies knowledge and data engineering tools and techniques

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Building and Exploiting EmotiNet, a knowledge Base for Emotion Detection Based on the Appraisal Theory Model

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2012年第1期3卷 88-101页

作者： Balahur, Alexandra Hermida, Jesus M. Montoyo, Andres Univ Alicante Dept Lenguajes & Sistemas Informat E-03080 Alicante Spain

The task of automatically detecting emotion in text is challenging. This is due to the fact that most of the times, textual expressions of affect are not direct-using emotion words-but result from the interpretation and assessment of the meaning of the concepts and interaction of concepts described in the text. This paper presents the core of EmotiNet, a new knowledge base (KB) for representing and storing affective reaction to real-life contexts, and the methodology employed in designing, populating, and evaluating it. The basis of the design process is given by a set of self-reported affective situations in the International Survey on Emotion Antecedents and Reactions (ISEAR) corpus. We cluster the examples and extract triples using Semantic Roles. We subsequently extend our model using other resources, such as VerbOcean, ConceptNet, and SentiWordNet, with the aim of generalizing the knowledge contained. Finally, we evaluate the approach using the representations of other examples in the ISEAR corpus. We conclude that EmotiNet, although limited by the domain and small quantity of knowledge it presently contains, represents a semantic resource appropriate for capturing and storing the structure and the semantics of real events and predicting the emotional responses triggered by chains of actions.

关键词： Affective computing emotion detection emotion theory knowledge and data engineering tools and techniques affect sensing and analysis

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Entropy-Based Scheduling Policy for Cross Aggregate Ranking Workloads

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2018年第3期11卷 507-520页

作者： Dai, Chengcheng Nutanong, Sarana Chow, Chi-Yin Cheng, Reynold City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

Many data exploration applications require the ability to identify the top-k results according to a scoring function. We study a class of top-k ranking problems where top-k candidates in a dataset are scored with the assistance of another set. We call this class of workloads cross aggregate ranking. Example computation problems include evaluating the Hausdorff distance between two datasets, finding the medoid or radius within one dataset, and finding the closest or farthest pair between two datasets. In this paper, we propose a parallel and distributed solution to process cross aggregate ranking workloads. Our solution subdivides the aggregate score computation of each candidate into tasks while constantly maintains the tentative top-k results as an uncertain top-k result set. The crux of our proposed approach lies in our entropy-based scheduling technique to determine result-yielding tasks based on their abilities to reduce the uncertainty of the tentative result set. Experimental results show that our proposed approach consistently outperforms the best existing one in two different types of cross aggregate rank workloads using real datasets.

关键词： Query processing knowledge and data engineering tools and techniques

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Fuzzy Web data Tables Integration Guided by an Ontological and Terminological Resource

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IEEE TRANSACTIONS ON knowledge AND data engineering 2013年第4期25卷 805-819页

作者： Buche, Patrice Dibie-Barthelemy, Juliette Ibanescu, Liliana Soler, Lydie INRA Joint Res Unit IATE F-34060 Montpellier France LIRMM GraphIK 2 F-34060 Montpellier 02 France INRA Met Risk F-75231 Paris 05 France AgroParisTech F-75231 Paris 05 France

In this paper, we present the design of ONDINE system which allows the loading and the querying of a data warehouse opened on the Web, guided by an Ontological and Terminological Resource (OTR). The data warehouse, composed of data tables extracted from Web documents, has been built to supplement existing local data sources. First, we present the main steps of our semiautomatic method to annotate data tables driven by an OTR. The output of this method is an XML/RDF data warehouse composed of XML documents representing data tables with their fuzzy RDF annotations. We then present our flexible querying system which allows the local data sources and the data warehouse to be simultaneously and uniformly queried, using the OTR. This system relies on SPARQL and allows approximate answers to be retrieved by comparing preferences expressed as fuzzy sets with fuzzy RDF annotations.

关键词： knowledge and data engineering tools and techniques XML/XSL/RDF uncertainty "fuzzy," and probabilistic reasoning representations data structures and transforms knowledge modeling

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The Influence of Digital Technologies on knowledge Management in engineering: A Systematic Literature Review

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IEEE TRANSACTIONS ON knowledge AND data engineering 2024年第2期36卷 854-867页

作者： Yao, Yuxin Patterson, Eann A. Taylor, Richard J. Univ Manchester Sch Engn Manchester M13 9PL England Univ Liverpool Dept Mech Mat & Aerosp Engn Liverpool L69 3BX England Univ Manchester Dept Mech Aerosp & Civil Engn Manchester M13 9PL England

Digital technologies are gaining widespread acceptance in engineering and offer opportunities for collating and curating knowledge during and beyond the life cycle of engineering products. knowledge is central to strategy and operations in most engineering organizations and digital technologies have been employed in attempts to improve current knowledge management practices. A systematic literature review was undertaken to address the question: how do digital technologies influence knowledge management in the engineering sector? Twenty-seven primary studies were identified from 3097 papers on these topics within the engineering literature published between 2010 and 2022. Four knowledge management processes supported by digital technologies were recognized: knowledge creation, storage and retrieval, sharing and application. In supporting knowledge management, digital technologies were found to have been acting in five roles: repositories, transactive memory systems, communication spaces, boundary objects and non-human actors. However, the ability of digital technologies to perform these roles simultaneously had not been considered and similarly knowledge management had not been addressed as a holistic process. Hence, it was concluded that a holistic approach to knowledge management combined with the deployment of digital technologies in multiple roles simultaneously would likely yield significant competitive advantage and organizational value for organizations in the engineering sector.

关键词： engineering information technology and systems knowledge and data engineering tools and techniques knowledge management social technologies

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Occurrence-Oriented Design Strategy for Developing Business Process Monitoring Systems

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IEEE TRANSACTIONS ON knowledge AND data engineering 2014年第7期26卷 1749-1762页

作者： Dominguez, Eladio Perez, Beatriz Rubio, Angel L. Zapata, Maria A. Lavilla, Juan Allue, Alberto Univ Zaragoza Dept Informat & Ingn Sistemas Zaragoza 50006 Spain Univ La Rioja Dept Matemat & Comp La Rioja 26004 Spain Infozara Consultoria Informat Zaragoza 50006 Spain

In a competitive market, business process improvement is a requirement for any organization. This improvement can only be achieved with the support of comprehensive systems that fully monitor business processes. We propose an occurrence-based approach to business process monitoring that provides a holistic perspective of system dynamics, lending support to evolution aspects. More specifically, we present a three-dimensional artifact, called Occurrence, in which structure, behavior, and guidance are considered simultaneously. Based on this, we define more complex structures, namely Occurrence Base and Occurrence Management System, which serve as scaffolding to develop business process monitoring systems. We also present a specific occurrence-based design strategy that, using an MDE approach, has been applied by our research group for the development of successful monitoring applications.

关键词： Design concepts knowledge and data engineering tools and techniques knowledge management applications Workflow management Health

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第6期9卷 1790-1804页

作者： Amin, Mohammad Shafkat Finley, Russell L., Jr. Jamil, Hasan M. Wayne State Univ Dept Comp Sci Sunnyvale CA 94086 USA Wayne State Univ Ctr Mol Med & Genet Detroit MI 48201 USA Univ Idaho Dept Comp Sci Moscow ID 83844 USA

Many emerging database applications entail sophisticated graph-based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact subgraph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. In this paper, we propose a novel technique called TraM for approximate graph matching that off-loads a significant amount of its processing on to the database making the approach viable for large graphs. Moreover, the vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We annotate nodes of the query graphs by means of their global topological properties and compare them with neighborhood biased segments of the data-graph for proper matches. We have conducted experiments on several real data sets, and have demonstrated the effectiveness and efficiency of the proposed method

关键词： Graphs and networks knowledge and data engineering tools and techniques bioinformatics graph and tree search strategies biology and genetics

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Behavior Action Mining

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IEEE ACCESS 2019年 7卷 19954-19964页

作者： Su, Peng Zeng, Daniel Zhao, Huimin Dali Univ Sch Math & Comp Sci Dali 671003 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Intelligent Control & Management Co Beijing 100190 Peoples R China Univ Wisconsin Sheldon B Lubar Sch Business Milwaukee WI 53201 USA

The actionable behavioral rules suggest specific actions that may influence certain behavior in the stakeholders' best interest. In mining such rules, it was assumed previously that all attributes are categorical while the numerical attributes have been discretized in advance. However, this assumption significantly reduces the solution space, and thus hinders the potential of mining algorithms, especially when the numerical attributes are prevalent. As the numerical data are ubiquitous in business applications, there is a crucial need for new mining methodologies that can better leverage such data. To meet this need, in this paper, we define a new data mining problem, named behavior action mining, as a problem of continuous variable optimization of expected utility for action. We then develop three approaches to solving this new problem, which uses regression as a technical basis. The experimental results based on a marketing dataset demonstrate the validity and superiority of our proposed approaches.

关键词： Business decision support knowledge and data engineering tools and techniques mining methods and algorithms

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Toward Long-Term and Archivable Reproducibility

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COMPUTING IN SCIENCE & engineering 2021年第3期23卷 82-91页

作者： Akhlaghi, Mohammad Infante-Sainz, Raul Roukema, Boudewijn F. Khellat, Mohammadreza Valls-Gabaud, David Baena-Galle, Roberto Inst Astrofis Canarias Tenerife 38205 Spain Univ La Laguna Tenerife 38205 Spain Nicolaus Copernicus Univ PL-87100 Torun Poland Ideal Informat Muscat 133 Oman Paris Observ F-75014 Paris France Univ Int La Rioja Logrono 26006 Spain

Analysis pipelines commonly use high-level technologies that are popular when created, but are unlikely to be readable, executable, or sustainable in the long term. A set of criteria is introduced to address this problem: completeness (no execution requirement beyond a minimal Unix-like operating system, no administrator privileges, no network connection, and storage primarily in plain text);modular design;minimal complexity;scalability;verifiable inputs and outputs;version control;linking analysis with narrative;and free and open-source software. As a proof of concept, we introduce "Maneage" (managing data lineage), enabling cheap archiving, provenance extraction, and peer verification that has been tested in several research publications. We show that longevity is a realistic requirement that does not sacrifice immediate or short-term reproducibility. The caveats (with proposed solutions) are then discussed and we conclude with the benefits for the various stakeholders. This article is itself a Maneage'd project (project commit 313db0b). Appendices-Two comprehensive appendices that review the longevity of existing solutions are available as supplementary "Web extras," which are available in the IEEE Computer Society Digital Library at http://***/10.1109/MCSE.2021.3072860. Reproducibility-All products available in zenodo.4913277, the Git history of this paper's source is at ***/***, which is also archived in Software Heritage Heritage: swh:1:dir:33fea87068c1612daf011f161b97787b9a0df39f. Clicking on the SWHIDs in the digital format will provide more "context" for same content.

关键词： Software Containers Kernel Libraries tools Virtual machining Buildings Workflow management Systems database Management Information Technology and Systems knowledge and data engineering tools and techniques Computers in Other Systems Computer Applications

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Malevolent Activity Detection with Hypergraph-Based Models

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IEEE TRANSACTIONS ON knowledge AND data engineering 2017年第5期29卷 1115-1128页

作者： Guzzo, Antonella Pugliese, Andrea Sacca, Domenico Piccolo, Antonio Univ Calabria DIMES Dept I-87036 Arcavacata Di Rende Italy

We propose a hypergraph-based framework for modeling and detecting malevolent activities. The proposed model supports the specification of order-independent sets of action symbols along with temporal and cardinality constraints on the execution of actions. We study and characterize the problems of consistency checking, equivalence, and minimality of hypergraph-based models. In addition, we define and characterize the general activity detection problem, that amounts to finding all subsequences that represent a malevolent activity in a sequence of logged actions. Since the problem is intractable, we also develop an index data structure that allows the security expert to efficiently extract occurrences of activities of interest.

关键词： Graphs and networks knowledge and data engineering tools and techniques

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