Federated Ontology Based Query System
- 1. Integrated Ontology for Sports (Domains: Cricket, Football and Tennis.) Database Interoperability Project Abhishek Agrawal, George Sam, Hari Haran Venugopal, Noopur Joshi
- 2. • Problem Statement and Motivation • Scope of the Project • Our Approach • Data sources – Scraper • Data Cleaning – Google refining, Karma • Ontology Creation – Using existing ontology to create Federated • Data Modeling – Karma Tool • Data Publishing – RDF and Triple Store Creation. • Data Extraction – Using OpenRDF for SPARQL Query • Future Work and Challenges • Conclusion Outline: 2
- 3. Problem Statement and Motivation 3 Why do we need Ontologies? - Need for constant, intelligent access to up-to-date, integrated and detailed information from the Web - Helps to aggregate data from various sources Why Federated Sports Ontology? - Helps to represent different sports and presents a common view - Is easily extendible - Intelligent information gathering - Scores: Who's winning, and how did the score change? - Schedules: Who's playing who, when, and where? - Standings: Who's in first place? Who's closest to qualifying ? - Data Analysis - Statistics: How do the players and/or teams measure up against one another in various categories? - News: How do we combine editorial coverage of sports with all data feeds??
- 4. Tennis - Players - Tournaments Cricket - Players - Matches - Rankings Football - Players - Leagues Scope of the Project 4
- 5. Data Extraction Data Cleaning Ontology Creation Date Modeling Querying using SPARQL Our Approach 5
- 6. Web Scraping: (web harvesting or web data extraction) is a computer software technique of extracting information from websites. Data Source: Scraper Scraping tools: • Beautiful Soap – Simple methods, Unicode support and consists of parsers like lxml and html5lib. • Jsoup – Java HTML Parser, WHATWG HTML5 specification, and parses HTML to the same DOM as modern browsers do. • Chrome Web scrapper – Using this extension you can create a plan (sitemap) how a web site should be traversed and what should be extracted. Using these sitemaps the Web Scraper will navigate the site accordingly and extract all data. 6
- 7. Data Cleaning Data cleansing, data cleaning or data scrubbing: is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database. Data Cleaning tools: • Karma Tool – Karma offers a programming- by-example interface to enable users to define data transformation scripts that transform data expressed in multiple data formats into a common format. • Google Refine – a power tool for working with messy data, cleaning it up, transforming it from one format into another. 7
- 8. Ontology : Class Hierarchy 8
- 10. Data Modeling Tool Used: KARMA (USC ISI) • Browser based Data Integration/ Data Modeling tool • Advantage – Data Integration and Publishing is easy • Steps: 1. Load Ontologies and data sets 2. Primitive Data Filtering 3. Setting semantic types for attributes 4. Building semantics for sports individually • Karma intelligently creates semantic mappings for higher concepts. • Create URL for entities. 10
- 11. Screenshot 11
- 12. Data Publishing • Available frameworks : OpenRDF, Protégé, ApacheJena. • OpenRDF : Browser based framework Integrated with KARMA Publish each Data set 1. JSON 2. R2RML Model 3. RDF Create Triple Store for RDF Load RDF into OpenRDF Triplestore 12
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- 14. Data Extraction SPARQL • Language used to extract information from RDF • Query Based SELECT * WHERE { ?Subject ?Predicate ?Object } 14
- 15. Future Work 1. Inclusion of other sports 2. Creating a web/ mobile based interface to query data 3. Creating an application for university level players and teams 4. Providing more specific information like : • Details about a particular team from the year 1990 – 2014 • Images of the players/teams • Details of all the matches played between two players/ teams 15
- 16. References • http://www.isi.edu/integration/karma/ • http://phd.jabenitez.com/wp-content/uploads/2014/03/A- Practical-Guide-To-Building-OWL-Ontologies-Using-Protege-4.pdf • http://ict.siit.tu.ac.th/~sun/SW/Protege%20Tutorial.pdf • http://www.crummy.com/software/BeautifulSoup/ • https://chrome.google.com/webstore/detail/web- scraper/jnhgnonknehpejjnehehllkliplmbmhn?hl=en • https://code.google.com/p/google-refine/ • http://www.datacleansing.net.au/Data_Cleansing_Services 16