Combining Unstructured, Fully Structured and Semi-Structured Information in Semantic Wikis
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Including Co-Referent Uris in a SPARQL Query
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Heriot Watt Pure Including Co-referent URIs in a SPARQL Query Christian Y A Brenninkmeijer1, Carole Goble1, Alasdair J G Gray1, Paul Groth2, Antonis Loizou2, and Steve Pettifer1 1 School of Computer Science, University of Manchester, UK. 2 Department of Computer Science, VU University of Amsterdam, The Netherlands. Abstract. Linked data relies on instance level links between potentially differing representations of concepts in multiple datasets. However, in large complex domains, such as pharmacology, the inter-relationship of data instances needs to consider the context (e.g. task, role) of the user and the assumptions they want to apply to the data. Such context is not taken into account in most linked data integration procedures. In this paper we argue that dataset links should be stored in a stand-off fashion, thus enabling different assumptions to be applied to the data links during query execution. We present the infrastructure developed for the Open PHACTS Discovery Platform to enable this and show through evaluation that the incurred performance cost is below the threshold of user perception. 1 Introduction A key mechanism of linked data is the use of equality links between resources across different datasets. However, the semantics of such links are often not triv- ial: as Halpin et al. have shown sameAs, is not always sameAs [12]; and equality is often context- and even task-specific, especially in complex domains. For ex- ample, consider the drug \Gleevec". A search over different chemical databases return records about two different chemical compounds { \Imatinib" and \Ima- tinib Mesylate" { which differ in chemical weight and other characteristics. -
Versioning Linked Datasets
Master’s Thesis Versioning Linked Datasets Towards Preserving History on the Semantic Web Author Paul Meinhardt 744393 July 13, 2015 Supervisors Magnus Knuth and Dr. Harald Sack Semantic Technologies Research Group Internet Technologies and Systems Chair Abstract Linked data provides methods for publishing and connecting structured data on the web using standard protocols and formats, namely HTTP, URIs, and RDF. Much like on the web of documents, linked data resources continuously evolve over time, but for the most part only their most recent state is accessible. In or- der to investigate the evolution of linked datasets and understand the impact of changes on the web of data it is necessary to make prior versions of such re- sources available. The lack of a common “self-service” versioning platform in the linked data community makes it more difficult for dataset maintainers to preserve past states of their data themselves. By implementing such a platform which also provides a consistent interface to historic information, dataset main- tainers can more easily start versioning their datasets while application develop- ers and researchers instantly have the possibility of working with the additional temporal data without laboriously collecting it on their own. This thesis, researches the possibility of creating a linked data versioning plat- form. It describes a basic model view for linked datasets, their evolution and presents a service approach to preserving the history of arbitrary linked datasets over time. Zusammenfassung Linked Data beschreibt Methoden für die Veröffentlichung und Verknüpfung strukturierter Daten im Web mithilfe standardisierter Protokolle und Formate, nämlich HTTP, URIs und RDF. Ähnlich wie andere Dokumente im World Wide Web, verändern sich auch Linked-Data-Resourcen stetig mit der Zeit. -
Semantics Developer's Guide
MarkLogic Server Semantic Graph Developer’s Guide 2 MarkLogic 10 May, 2019 Last Revised: 10.0-8, October, 2021 Copyright © 2021 MarkLogic Corporation. All rights reserved. MarkLogic Server MarkLogic 10—May, 2019 Semantic Graph Developer’s Guide—Page 2 MarkLogic Server Table of Contents Table of Contents Semantic Graph Developer’s Guide 1.0 Introduction to Semantic Graphs in MarkLogic ..........................................11 1.1 Terminology ..........................................................................................................12 1.2 Linked Open Data .................................................................................................13 1.3 RDF Implementation in MarkLogic .....................................................................14 1.3.1 Using RDF in MarkLogic .........................................................................15 1.3.1.1 Storing RDF Triples in MarkLogic ...........................................17 1.3.1.2 Querying Triples .......................................................................18 1.3.2 RDF Data Model .......................................................................................20 1.3.3 Blank Node Identifiers ..............................................................................21 1.3.4 RDF Datatypes ..........................................................................................21 1.3.5 IRIs and Prefixes .......................................................................................22 1.3.5.1 IRIs ............................................................................................22 -
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Lawrence Berkeley National Laboratory Recent Work Title Big-Data Science in Porous Materials: Materials Genomics and Machine Learning. Permalink https://escholarship.org/uc/item/3ss713pj Journal Chemical reviews, 120(16) ISSN 0009-2665 Authors Jablonka, Kevin Maik Ongari, Daniele Moosavi, Seyed Mohamad et al. Publication Date 2020-08-01 DOI 10.1021/acs.chemrev.0c00004 Peer reviewed eScholarship.org Powered by the California Digital Library University of California This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes. pubs.acs.org/CR Review Big-Data Science in Porous Materials: Materials Genomics and Machine Learning Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, and Berend Smit* Cite This: Chem. Rev. 2020, 120, 8066−8129 Read Online ACCESS Metrics & More Article Recommendations ABSTRACT: By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal−organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many materials to be processed using conventional, brute force, methods. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We show how to select appropriate training sets, survey approaches that are used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. -
Property Graph Vs RDF Triple Store: a Comparison on Glycan Substructure Search
RESEARCH ARTICLE Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search Davide Alocci1,2, Julien Mariethoz1, Oliver Horlacher1,2, Jerven T. Bolleman3, Matthew P. Campbell4, Frederique Lisacek1,2* 1 Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland, 2 Computer Science Department, University of Geneva, Geneva, 1227, Switzerland, 3 Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland, 4 Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia * [email protected] Abstract Resource description framework (RDF) and Property Graph databases are emerging tech- nologies that are used for storing graph-structured data. We compare these technologies OPEN ACCESS through a molecular biology use case: glycan substructure search. Glycans are branched Citation: Alocci D, Mariethoz J, Horlacher O, tree-like molecules composed of building blocks linked together by chemical bonds. The Bolleman JT, Campbell MP, Lisacek F (2015) molecular structure of a glycan can be encoded into a direct acyclic graph where each node Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search. PLoS ONE 10(12): represents a building block and each edge serves as a chemical linkage between two build- e0144578. doi:10.1371/journal.pone.0144578 ing blocks. In this context, Graph databases are possible software solutions for storing gly- Editor: Manuela Helmer-Citterich, University of can structures and Graph query languages, such as SPARQL and Cypher, can be used to Rome Tor Vergata, ITALY perform a substructure search. Glycan substructure searching is an important feature for Received: July 16, 2015 querying structure and experimental glycan databases and retrieving biologically meaning- ful data. -
Unstructured Data Is a Risky Business
R&D Solutions for OIL & GAS EXPLORATION AND PRODUCTION Unstructured Data is a Risky Business Summary Much of the information being stored by oil and gas companies—technical reports, scientific articles, well reports, etc.,—is unstructured. This results in critical information being lost and E&P teams putting themselves at risk because they “don’t know what they know”? Companies that manage their unstructured data are better positioned for making better decisions, reducing risk and boosting bottom lines. Most users either don’t know what data they have or simply cannot find it. And the oil and gas industry is no different. It is estimated that 80% of existing unstructured information. Unfortunately, data is unstructured, meaning that the it is this unstructured information, often majority of the data that we are gen- in the form of internal and external PDFs, erating and storing is unusable. And PowerPoint presentations, technical while this is understandable considering reports, and scientific articles and publica- 18 managing unstructured data takes time, tions, that contains clues and answers money, effort and expertise, it results in regarding why and how certain interpre- 3 x 10 companies wasting money by making tations and decisions are made. ill-informed investment decisions. If oil A case in point of the financial risks of bytes companies want to mitigate risk, improve not managing unstructured data comes success and recovery rates, they need to from a customer that drilled a ‘dry hole,’ be better at managing their unstructured costing the company a total of $20 million data. In this paper, Phoebe McMellon, dollars—only to realize several years later Elsevier’s Director of Oil and Gas Strategy that they had drilled a ‘dry hole’ ten years & Segment Marketing, discusses how big Every day we create approximately prior two miles away. -
1 Application of Text Mining to Biomedical Knowledge Extraction: Analyzing Clinical Narratives and Medical Literature
Amy Neustein, S. Sagar Imambi, Mário Rodrigues, António Teixeira and Liliana Ferreira 1 Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature Abstract: One of the tools that can aid researchers and clinicians in coping with the surfeit of biomedical information is text mining. In this chapter, we explore how text mining is used to perform biomedical knowledge extraction. By describing its main phases, we show how text mining can be used to obtain relevant information from vast online databases of health science literature and patients’ electronic health records. In so doing, we describe the workings of the four phases of biomedical knowledge extraction using text mining (text gathering, text preprocessing, text analysis, and presentation) entailed in retrieval of the sought information with a high accuracy rate. The chapter also includes an in depth analysis of the differences between clinical text found in electronic health records and biomedical text found in online journals, books, and conference papers, as well as a presentation of various text mining tools that have been developed in both university and commercial settings. 1.1 Introduction The corpus of biomedical information is growing very rapidly. New and useful results appear every day in research publications, from journal articles to book chapters to workshop and conference proceedings. Many of these publications are available online through journal citation databases such as Medline – a subset of the PubMed interface that enables access to Medline publications – which is among the largest and most well-known online databases for indexing profes- sional literature. Such databases and their associated search engines contain important research work in the biological and medical domain, including recent findings pertaining to diseases, symptoms, and medications. -
Big Data Mining Tools for Unstructured Data: a Review YOGESH S
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by International Journal of Innovative Technology and Research (IJITR) Yogesh S. Kalambe* et al. (IJITR) INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND RESEARCH Volume No.3, Issue No.2, February – March 2015, 2012 – 2017. Big Data Mining Tools for Unstructured Data: A Review YOGESH S. KALAMBE D. PRATIBA M.Tech-Student Assistant Professor, Computer Science and Engineering, Department of Computer Science and Engineering, R V College of Engineering, R V College of Engineering, Bangalore, Karnataka, India. Bangalore, Karrnataka, India. Dr. PRITAM SHAH Associate Professor, Department of Computer Science and Engineering, R V College of Engineering, Bangalore, Karnataka, India. Abstract— Big data is a buzzword that is used for a large size data which includes structured data, semi- structured data and unstructured data. The size of big data is so large, that it is nearly impossible to collect, process and store data using traditional database management system and software techniques. Therefore, big data requires different approaches and tools to analyze data. The process of collecting, storing and analyzing large amount of data to find unknown patterns is called as big data analytics. The information and patterns found by the analysis process is used by large enterprise and companies to get deeper knowledge and to make better decision in faster way to get advantage over competition. So, better techniques and tools must be developed to analyze and process big data. Big data mining is used to extract useful information from large datasets which is mostly unstructured data. -
Extracting Unstructured Data from Template Generated Web Documents
Extracting Unstructured Data from Template Generated Web Documents Ling Ma Nazli Goharian Abdur Chowdhury Information Retrieval Laboratory Information Retrieval Laboratory America Online Inc. Computer Science Department Computer Science Department [email protected] Illinois Institute of Technology Illinois Institute of Technology [email protected] [email protected] ABSTRACT section of a page contributes to the same problem. A problem that We propose a novel approach that identifies web page templates is not well studied is the negative effect of such noise data on the and extracts the unstructured data. Extracting only the body of the result of the user queries. Removing this data improves the page and eliminating the template increases the retrieval effectiveness of search by reducing the irrelevant results. precision for the queries that generate irrelevant results. We Furthermore, we argue that the irrelevant results, even covering a believe that by reducing the number of irrelevant results; the small fraction of retrieved results, have the restaurant-effect, users are encouraged to go back to a given site to search. Our namely users are ten times less likely to return or use the search experimental results on several different web sites and on the service after a bad experience. This is of more importance, whole cnnfn collection demonstrate the feasibility of our approach. considering the fact that an average of 26.8% of each page is formatting data and advertisement. Table 1 shows the ratio of the formatting data to the real data in the eight web sites we examined. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Search Process Table 1: Template Ratio in Web Collections Web page Collection Template Ratio General Terms Cnnfn.com 34.1% Design, Experimentation Netflix.com 17.8% Keywords NBA.com 18.1% Automatic template removal, text extraction, information retrieval, Amazon.com 19.3% Retrieval Accuracy Ebay.com 22.5% Disneylandsource.com 54.1% 1. -
A Digital Library for Plant Information with Performance Comparison Between a Relational Database and a Nosql Database (RDF Triple Store)
Grand Valley State University ScholarWorks@GVSU Technical Library School of Computing and Information Systems 2015 A Digital Library for Plant Information with Performance Comparison between a Relational Database and a NoSQL Database (RDF Triple Store) Md Arman Ullah Grand Valley State University Follow this and additional works at: https://scholarworks.gvsu.edu/cistechlib ScholarWorks Citation Ullah, Md Arman, "A Digital Library for Plant Information with Performance Comparison between a Relational Database and a NoSQL Database (RDF Triple Store)" (2015). Technical Library. 205. https://scholarworks.gvsu.edu/cistechlib/205 This Project is brought to you for free and open access by the School of Computing and Information Systems at ScholarWorks@GVSU. It has been accepted for inclusion in Technical Library by an authorized administrator of ScholarWorks@GVSU. For more information, please contact [email protected]. A Digital Library for Plant Information with Performance Comparison between a Relational Database and a NoSQL Database (RDF Triple Store) By Md Arman Ullah A project submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Information System at Grand Valley State University April, 2015 Dr. Tao Yonglei Date Abstract This project is to develop a digital library that allows the user to browse, search, and retrieve information about plants. It uses plant information acquired from the United States Department of Agriculture (USDA), which contains native and naturalized plants in North American territories. In addition, the user is allowed to contribute information and the administrator is able to add or edit plant information. This project is built by using a relational database and also a NoSQL database (RDF Triple Store), allowing to compare performance between the two databases. -
Some Experiments on the Usage of a Deductive Database for RDFS Querying and Reasoning
Some experiments on the usage of a deductive database for RDFS querying and reasoning Giovambattista Ianni1;2, Alessandra Martello1, Claudio Panetta1, and Giorgio Terracina1 1 Dipartimento di Matematica, Universit`adella Calabria, I-87036 Rende (CS), Italy, 2 Institut fÄurInformationssysteme 184/3, Technische UniversitÄatWien Favoritenstra¼e 9-11, A-1040 Vienna, Austria fianni,a.martello,panetta,[email protected] Abstract. Ontologies are pervading many areas of knowledge represen- tation and management. To date, most research e®orts have been spent on the development of su±ciently expressive languages for the represen- tation and querying of ontologies; however, querying e±ciency has re- ceived attention only recently, especially for ontologies referring to large amounts of data. In fact, it is still uncertain how reasoning tasks will scale when applied on massive amounts of data. This work is a ¯rst step toward this setting: based on a previous result showing that the SPARQL query language can be mapped to a Datalog, we show how e±cient querying of big ontologies can be accomplished with a database oriented exten- sion of the well known system DLV, recently developed. We report our initial results and we discuss about bene¯ts of possible alternative data structures for representing RDF graphs in our architecture. 1 Introduction The Semantic Web [4, 11] is an extension of the current Web by standards and technologies that help machines to understand the information on the Web so that they can support richer discovery, data integration, navigation, and au- tomation of tasks. Roughly, the main ideas behind the Semantic Web aim to (i) add a machine-readable meaning to Web pages, (ii) use ontologies for a precise de¯nition of shared terms in Web resources, (iii) make use of KR technology for automated reasoning on Web resources, and (iv) apply cooperative agent technology for processing the information of the Web. -
Top Natural Language Processing Applications in Business UNLOCKING VALUE from UNSTRUCTURED DATA for Years, Enterprises Have Been Making Good Use of Their 1
Top Natural Language Processing Applications in Business UNLOCKING VALUE FROM UNSTRUCTURED DATA For years, enterprises have been making good use of their 1. Internet of Things (IoT): applying technologies, such structured data (tables, spreadsheets, etc.). However, as real-time analytics, machine learning (ML), and smart the larger part of enterprise data, nearly 80 percent, is sensors, to manage and analyze machine-generated unstructured and has been much less accessible. structured data 2. Computer Vision: using digital imaging technologies, From emails, text documents, research and legal reports ML, and pattern recognition to interpret image and video to voice recordings, videos, social media posts and content more, unstructured data is a huge body of information. 3. Document Understanding: combining NLP and ML to But, compared to structured data, it has been much more gain insights into human-generated, natural language challenging to leverage. unstructured data. Traditional search has done a great job helping users As it powers document understanding applications that discover and derive insights from some of this data. But unlock value from unstructured data, NLP has become an enterprises need to go beyond search to maximize the use of essential enabler of the AI evolution in today’s enterprises. unstructured data as a resource for enhanced analytics and decision making. This white paper discusses the emergence of NLP as a key insight discovery technique, followed by examples This is where natural language processing (NLP), a field of of impactful NLP applications that Accenture has helped artificial intelligence (AI) that’s used to handle the processing clients implement. and analysis of large volumes of unstructured data, can be a real game changer.