Semantic Integration of Social and Domain Knowledge in a Collaborative Network Platform
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Semantic Integration and Knowledge Discovery for Environmental Research
Journal of Database Management, 18(1), 43-67, January-March 2007 43 Semantic Integration and Knowledge Discovery for Environmental Research Zhiyuan Chen, University of Maryland, Baltimore County (UMBC), USA Aryya Gangopadhyay, University of Maryland, Baltimore County (UMBC), USA George Karabatis, University of Maryland, Baltimore County (UMBC), USA Michael McGuire, University of Maryland, Baltimore County (UMBC), USA Claire Welty, University of Maryland, Baltimore County (UMBC), USA ABSTRACT Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantics- based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution brows- ing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results. Keywords: environmental research, knowledge discovery and navigation, semantic integra- tion, semantic networks, -
A Question Answering System for Chemistry
A Question Answering System for Chemistry Preprint Cambridge Centre for Computational Chemical Engineering ISSN 1473 – 4273 A Question Answering System for Chemistry Xiaochi Zhou1, Daniel Nurkowski4, Sebastian Mosbach1;2, Jethro Akroyd1;2, Markus Kraft1;2;3 released: January 25, 2021 1 Department of Chemical Engineering 2 CARES and Biotechnology Cambridge Centre for Advanced University of Cambridge Research and Education in Singapore Philippa Fawcett Drive 1 Create Way Cambridge, CB3 0AS CREATE Tower, #05-05 United Kingdom Singapore, 138602 3 School of Chemical 4 CMCL Innovations and Biomedical Engineering Sheraton House Nanyang Technological University Castle Park, Castle Street 62 Nanyang Drive Cambridge, CB3 0AX Singapore, 637459 United Kingdom Preprint No. 266 Keywords: Question Answering system, Natural language processing, Knowledge Graph Edited by Computational Modelling Group Department of Chemical Engineering and Biotechnology University of Cambridge Philippa Fawcett Drive Cambridge, CB3 0AS United Kingdom CoMo E-Mail: [email protected] GROUP World Wide Web: https://como.ceb.cam.ac.uk/ Abstract This paper describes the implementation and evaluation of a proof-of-concept Ques- tion Answering system for accessing chemical data from knowledge graphs which offer data from chemical kinetics to chemical and physical properties of species. We trained a question type classification model and an entity extraction model to interpret chemistry questions of interest. The system has a novel design which applies a topic model to identify the question-to-ontology affiliation. The topic model helps the sys- tem to provide more accurate answers. A new method that automatically generates training questions from ontologies is also implemented. The question set generated for training contains 80085 questions under 8 types. -
Discussion #1 | University of Texas at Austin | August 13, 2018 Facilitated By: Itza A
Introduction to Linked Data Discussion #1 | University of Texas at Austin | August 13, 2018 Facilitated by: Itza A. Carbajal, LLILAS Benson Latin American Metadata Librarian Hello! My name is Itza A. Carbajal I will be facilitating this discussion series on Linked Data I am the Latin American Metadata Librarian for a Post Custodial project at LLILAS Benson Have questions? Email me at: [email protected] Like twitter? You can find me at: @archiviststan Housekeeping ◎ Discussions are meant to highlight collective wisdom of the group ◎ Attendance to all discussion meetings not required, but recommended ◎ Take home practices are not required, but encouraged to further an individual’s understanding ◎ Readings are not required, but should be considered as a method for self education or for sharing with others Link to LIVE syllabus: http://bit.ly/SYLLABUSUTLD Link to reading materials: http://bit.ly/READUTLD 1. Discussion #1 Topics Topics to Discuss ◎ Semantic Web ◎ Linked data principles ◎ RDF and Triple statements Related Topics not covered in discussions ◎ Linked Open Data ◎ Examples of linked open data sets ◎ Linked Data platform 2. First, the Semantic Web Semantic Web (project) ◎ Derives from the concept of semantics defined as the study of meanings ◎ Extension to the current World Wide Web ◎ Also known as Web 3.0 where the web can now “read-write-execute” ◎ Changes the current web of information to a web of data including data inside the web and outside ◎ Core functions include semantic markup ○ Semantic markup - data interchange formats accessible to humans and machine ◎ Focuses on adding meaning/context to information giving machines and humans the ability to communicate and cooperate ◎ Relies on machine-readable metadata to expresses that meaning/context ◎ No formal definition, still ongoing and constantly maturing 3. -
Constructing Reference Semantic Predictions from Biomedical Knowledge Sources
Constructing Reference Semantic Predictions from Biomedical Knowledge Sources Demeke Ayele1, J. P. Chevallet2, Million Meshesha1, Getnet Kassie1 (1) Addis Ababa University, Addis Ababa, Ethiopia (2) University of Grenoble, Grenoble, France {demekeayele, meshe8, getnetmk}@gmail.com, jean- [email protected] ABSTRACT Semantic tuples are core component of text mining and knowledge extraction systems in biomedicine. The practical success of these systems significantly depends on the correctness and quality of the extracted semantic tuples. The quality and correctness of the semantic predictions can be measured against a benchmark semantic structure. In this article, we presented an approach for constructing a reference semantic tuple structure based on the existing biomedical knowledge sources in which the evaluation is based on the UMLS knowledge sources. In the evaluation, 7400 semantic triples are extracted from UMLS knowledge sources and the semantic predictions are constructed using the proposed approach. In the semantic triples, 87 concepts are found redundantly classified and 207 pair of semantic triples showed hierarchically inconsistent. 128 are found to be non-taxonomically inconsistent. The quality of the semantic triple is also judged using expert evaluators. The Cohen's kappa coefficient is used to measure the degree of agreement between two evaluators and the result is promising (0.9). Construire des prévisions de référence sémantique à partir de sources de connaissances biomédicales Les "tuples sémantiques" forment un élément essentiel à la fouille de texte et aux systèmes d'extraction de connaissances dans le domaine biomédical. Le succès en pratique des systèmes exploitant ces informations sémantique, dépend fortement de l'exactitude et de la qualité des tuples sémantiques. -
Semantic Integration Across Heterogeneous Databases Finding Data Correspondences Using Agglomerative Hierarchical Clustering and Artificial Neural Networks
DEGREE PROJECT IN COMPUTER SCIENCE AND ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2018 Semantic Integration across Heterogeneous Databases Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks MARK HOBRO KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Semantic Integration across Heterogeneous Databases Finding Data Correspondences using Agglomerative Hierarchical Clustering and Artificial Neural Networks MARK HOBRO Master in Computer Science Date: April 11, 2018 Supervisor: John Folkesson Examiner: Hedvig Kjellström Swedish title: Semantisk integrering mellan heterogena databaser: Hitta datakopplingar med hjälp av hierarkisk klustring och artificiella neuronnät School of Computer Science and Communication iii Abstract The process of data integration is an important part of the database field when it comes to database migrations and the merging of data. The research in the area has grown with the addition of machine learn- ing approaches in the last 20 years. Due to the complexity of the re- search field, no go-to solutions have appeared. Instead, a wide variety of ways of enhancing database migrations have emerged. This thesis examines how well a learning-based solution performs for the seman- tic integration problem in database migrations. Two algorithms are implemented. One that is based on informa- tion retrieval theory, with the goal of yielding a matching result that can be used as a benchmark for measuring the performance of the machine learning algorithm. The machine learning approach is based on grouping data with agglomerative hierarchical clustering and then training a neural network to recognize patterns in the data. This al- lows making predictions about potential data correspondences across two databases. -
Distributed Semantic Sensor Networks How to Use Semantics and Knowledge Distribution to Integrate Sensor Data of Disparate Data Sources
2007 Distributed Semantic Sensor Networks How to use semantics and knowledge distribution to integrate sensor data of disparate data sources. In this research project we focused on sensor networks and how semantic web technologies and knowledge sharing can make integration of distributed sensor data possible. To achieve this we have first identified the main challenges to address to allow for data integration. A distributed semantic sensor network framework is proposed that uses semantic web techniques and knowledge sharing to address these challenges. We propose that knowledge sharing in a sensor network is a key aspect of data integration in a dynamic environment, since it allows the network to handle changes in the environment. This project is innovative in that it proposes new ways to handle semantic integration in a distributed environment, by using question federation and data conversion on semantically annotated data and questions. It contributes to current research in that our framework enables data integration of distributed sensor data. Masters Student M.J. van der Veen, Department of Artificial Intelligence, Rijksuniversiteit Groningen, Groningen Supervisors Bart Verheij, Department of Artificial Intelligence, Rijksuniversiteit Groningen Arnoud de Jong, TNO ICT & Telecommunicatie, Groningen 1 People A masters student at the Department of Artificial Intelligence at the Rijksuniversiteit Groningen in The Netherlands. This thesis is the final work in his masters studies. His main interests besides writing this thesis is doing sports, coaching, travelling and making music . Masters Student: Maarten van der Veen A tenured lecturer/researcher (in Dutch: universitair docent) at the University of Groningen, Department of Artificial Intelligence and a member of the ALICE institute. -
Applying the Semantic Web to Computational Chemistry
Applying the Semantic Web to Computational Chemistry Neil S. Ostlund, Mirek Sopek Chemical Semantics Inc. , Gainesville, Florida, USA {ostlund, sopek}@chemicalsemantics.com Abstract. Chemical Semantics is a new start-up devoted to bringing the Semantic Web to computational chemistry, with a future goal to cover chemical results of other kinds, including experimental. We have created a prototype test bed that enables computational chemists to publish the results of their computations (for example: structure, energies and wave functions of Ab Initio calculations) on servers (powered by semantic triple stores) holding RDF data. In addition, scientists will be able to search across results produced by themselves and other researchers using SPARQL queries and faceted search built on top of it. The semantics of computational chemistry oriented RDF data is defined by an ontology identified as “GC” (Gainesville Core). Gainesville Core defines concepts such as: theoretical models used in computations, molecular systems (as collections of atoms and molecules), molecular arrangements which describe system evolution in time or in phase space and so on. The poster demonstrates the project during its early stage, including a description and demonstration of the prototype portal created by Chemical Semantics. This paper is the concise description of the poster content. Keywords: computational chemistry, semantic web, linked data, Gainesville Core, ontology Chemical Semantics portal main targets The Chemical Semantic portal, now in its prototype version, has the following long term goals: 1. Interoperable PUBLISHING of Computational Chemistry calculations using Linked Data principles 2. Enhanced search across all published results with basic reasoning enabled by the new ontology 3. FEDERATION of published data with existing web-based chemical datasets 4. -
L Dataspaces Make Data Ntegration Obsolete?
DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles DBKDA 2011 Panel Discussion: Will Dataspaces Make Data Integration Obsolete? Moderator: Fritz Laux, Reutlingen Univ., Germany Panelists: Kazuko Takahashi, Kwansei Gakuin Univ., Japan Lena Strömbäck, Linköping Univ., Sweden Nipun Agarwal, Oracle Corp., USA Christopher Ireland, The Open Univ., UK Fritz Laux, Reutlingen Univ., Germany DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles The Dataspace Idea Space of Data Management Scalable Functionality and Costs far Web Search Functionality virtual Organization pay-as-you-go, Enterprise Dataspaces Admin. Portal Schema Proximity Federated first, DBMS DBMS scient. Desktop Repository Search DBMS schemaless, near unstructured high Semantic Integration low Time and Cost adopted from [Franklin, Halvey, Maier, 2005] DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles Dataspaces (DS) [Franklin, Halevy, Maier, 2005] is a new abstraction for Information Management ● DS are [paraphrasing and commenting Franklin, 2009] – Inclusive ● Deal with all the data of interest, in whatever form => but semantics matters ● We need access to the metadata! ● derive schema from instances? ● Discovering new data sources => The Münchhausen bootstrap problem? Theodor Hosemann (1807-1875) DBKDA 2011, January 23-28, 2011 – St. Maarten, The Netherlands Antilles Dataspaces (DS) [Franklin, Halevy, Maier, 2005] is a new abstraction for Information Management ● DS are [paraphrasing and commenting Franklin, 2009] – Co-existence -
Learning to Match Ontologies on the Semantic Web
The VLDB Journal manuscript No. (will be inserted by the editor) Learning to Match Ontologies on the Semantic Web AnHai Doan1, Jayant Madhavan2, Robin Dhamankar1, Pedro Domingos2, Alon Halevy2 1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA fanhai,[email protected] 2 Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA fjayant,pedrod,[email protected] Received: date / Revised version: date Abstract On the Semantic Web, data will inevitably come and much of the potential of the Web has so far remained from many different ontologies, and information processing untapped. across ontologies is not possible without knowing the seman- In response, researchers have created the vision of the Se- tic mappings between them. Manually finding such mappings mantic Web [BLHL01], where data has structure and ontolo- is tedious, error-prone, and clearly not possible at the Web gies describe the semantics of the data. When data is marked scale. Hence, the development of tools to assist in the ontol- up using ontologies, softbots can better understand the se- ogy mapping process is crucial to the success of the Seman- mantics and therefore more intelligently locate and integrate tic Web. We describe GLUE, a system that employs machine data for a wide variety of tasks. The following example illus- learning techniques to find such mappings. Given two on- trates the vision of the Semantic Web. tologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well- founded probabilistic definitions to several practical similar- Example 1 Suppose you want to find out more about some- ity measures, and show that GLUE can work with all of them. -
Relext: Relation Extraction Using Deep Learning Approaches for Cybersecurity Knowledge Graph Improvement
RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement Aditya Pingle, Aritran Piplai, Sudip Mittal, Anupam Joshi James Holt, and Richard Zak University of Maryland, Baltimore County, Baltimore, MD 21250, USA Laboratory for Physical Sciences fadiping1, apiplai1, smittal1, [email protected] fholt, [email protected] Abstract—Security Analysts that work in a ‘Security Opera- cybersecurity reports, CVE [21], CWE [22], National Vul- tions Center’ (SoC) play a major role in ensuring the security nerability Datasets [28], and any cybersecurity text publicly of the organization. The amount of background knowledge they available. Various SIEM systems, fetch, process, and store have about the evolving and new attacks makes a significant difference in their ability to detect attacks. Open source threat much of the cyber threat intelligence available through OSINT intelligence sources, like text descriptions about cyber-attacks, sources. Knowledge Graphs (KG) specifically to store cyber can be stored in a structured fashion in a cybersecurity knowl- threat intelligence has been used by many cybersecurity in- edge graph. A cybersecurity knowledge graph can be paramount formatics systems. These cybersecurity knowledge graphs can in aiding a security analyst to detect cyber threats because it not only store but are also be able to retrieve data and answer stores a vast range of cyber threat information in the form of semantic triples which can be queried. A semantic triple complex queries asked by the security analysts. contains two cybersecurity entities with a relationship between The dependence of various cybersecurity informatics sys- them. In this work, we propose a system to create semantic tems on various knowledge representation schemes makes triples over cybersecurity text, using deep learning approaches it imperative that we develop systems that improve these to extract possible relationships. -
Semantic Integration in the IFF Gies , Etc
ogies, composing ontologies via fusions, noting dependen- cies between ontologies, declaring the use of other ontolo- 3 Semantic Integration in the IFF gies , etc. The IFF takes a building blocks approach to- wards the development of object-level ontological struc- ture. This is a rather elaborate categorical approach, which Robert E. Kent uses insights and ideas from the theory of distributed logic Ontologos known as information flow (Barwise and Seligman, 1997) [email protected] and the theory of formal concept analysis (Ganter and Wille, 1999). The IFF represents metalogic, and as such operates at the structural level of ontologies. In the IFF, Abstract there is a precise boundary between the metalevel and the object level. The IEEE P1600.1 Standard Upper Ontology (SUO) The modular architecture of the IFF consists of metale- project aims to specify an upper ontology that will provide vels, namespaces and meta-ontologies. There are three me- a structure and a set of general concepts upon which do- talevels: top, upper and lower. This partition, which cor- main ontologies could be constructed. The Information responds to the set-theoretic distinction between small Flow Framework (IFF), which is being developed under (sets), large (classes) and generic collections, is permanent. the auspices of the SUO Working Group, represents the Each metalevel services the level below by providing a structural aspect of the SUO. The IFF is based on category language that is used to declare and axiomatize that level. theory. Semantic integration of object-level ontologies in * The top metalevel services the upper metalevel, the upper the IFF is represented with its fusion construction . -
Rule-Based Intelligence on the Semantic Web Implications for Military Capabilities
UNCLASSIFIED Rule-Based Intelligence on the Semantic Web Implications for Military Capabilities Dr Paul Smart Senior Research Fellow School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ United Kingdom 26th November 2007 UNCLASSIFIED Report Documentation Page Report Title: Rule-Based Intelligence on the Semantic Web Report Subtitle Implications for Military Capabilities Project Title: N/A Number of Pages: 57 Version: 1.2 Date of Issue: 26/11/2007 Due Date: 22/11/2007 Performance EZ~01~01~17 Number of 95 Indicator: References: Reference Number: DT/Report/RuleIntel Report Availability: APPROVED FOR PUBLIC RELEASE; LIMITED DISTRIBUTION Abstract Availability: APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED Authors: Paul Smart Keywords: semantic web, ontologies, reasoning, decision support, rule languages, military Primary Author Details: Client Details: Dr Paul Smart Senior Research Fellow School of Electronics and Computer Science University of Southampton Southampton, UK SO17 1BJ tel: +44 (0)23 8059 6669 fax: +44 (0)23 8059 2783 email: [email protected] Abstract: Rules are a key element of the Semantic Web vision, promising to provide a foundation for reasoning capabilities that underpin the intelligent manipulation and exploitation of information content. Although ontologies provide the basis for some forms of reasoning, it is unlikely that ontologies, by themselves, will support the range of knowledge-based services that are likely to be required on the Semantic Web. As such, it is important to consider the contribution that rule-based systems can make to the realization of advanced machine intelligence on the Semantic Web. This report aims to review the current state-of-the-art with respect to semantic rule-based technologies.