Application of Ontology in Semantic Information Retrieval by Prof Shahrul Azman from FSTM, UKM

Introductory lecture to the VU University Amsterdam Master course on Ontology Engineering. See http://semanticweb.cs.vu.nl/OE2012/

Presented by Ted Xiao at RobotXSpace on 4/18/2017. This workshop covers the fundamentals of Natural Language Processing, crucial NLP approaches, and an overview of NLP in industry.

Designing website and intranet taxonomies with a consideration to serving user experience. Presented at World IA Day 2020.

This document discusses ontology-based data access. It begins by defining ontology as a representation of concepts and relationships that define a domain. It then provides examples of ontology elements like concepts, attributes, and relations. It describes how ontologies can be used to share understanding, enable knowledge reuse, and separate domain from operational knowledge. The document outlines the process for developing ontologies including scope, capture, encoding, integration, and evaluation. It discusses using ontologies to provide a user-oriented view of data and facilitate query access across data sources. The document concludes by discussing ongoing work on semantic query analysis and graphical ontology mapping tools.

An ontology formally represents knowledge as concepts and relationships within a domain. It defines classes and subclasses of concepts, as well as properties and instances of classes. An ontology uses a structured format like RDF triples to define these components and can be used to model and reason about a domain. Popular ontology languages include RDF, RDFS, and OWL, which allow linking open data from different sources using common identifiers.

"SPARQL Cheat Sheet" is a short collection of slides intended to act as a guide to SPARQL developers. It includes the syntax and structure of SPARQL queries, common SPARQL prefixes and functions, and help with RDF datasets. The "SPARQL Cheat Sheet" is intended to accompany the SPARQL By Example slides available at http://www.cambridgesemantics.com/2008/09/sparql-by-example/ .

An inverted file indexes a text collection to speed up searching. It contains a vocabulary of distinct words and occurrences lists with information on where each word appears. For each term in the vocabulary, it stores a list of pointers to occurrences called an inverted list. Coarser granularity indexes use less storage but require more processing, while word-level indexes enable proximity searches but use more space. The document describes how inverted files are structured and constructed from text and discusses techniques like block addressing that reduce their space requirements.

Presentation based on two papers published on text similarity using corpus-based and knowledge-based approaches like wordnet and wikipedia.

Presentation given on March 12, 2013 by Marjorie M.K. Hlava of Access Innovations, Inc. as a webinar for the San Francisco chapter of the Special Libraries Association.

Reference, Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology.

The Boolean model is a classical information retrieval model based on set theory and Boolean logic. Queries are specified as Boolean expressions to retrieve documents that either contain or do not contain the query terms. All term frequencies are binary and documents are retrieved based on an exact match to the query terms. However, this model has limitations as it does not rank documents and queries are difficult for users to translate into Boolean expressions, often returning too few or too many results.

Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)

The document discusses key concepts related to information retrieval including data, information, knowledge, and wisdom. It defines information retrieval as the tracing and recovery of specific information from stored data through searching. The main aspects of the information retrieval process are described as querying a collection to retrieve relevant objects that may partially match the query. Precision and recall are discussed as important measures for information retrieval systems.

A Timeline to Semantic Web Developments at Google, including Google's Second Patent, the Knowledge Graph, Hummingbird and other inventions.

The document introduces an ontology tutorial that will cover basic concepts of the Semantic Web, Linked Data, and the Resource Description Framework data model as well as the ontology languages RDFS and OWL. The tutorial is intended for information professionals who want to gain an introductory understanding of ontologies, ontology concepts, and terminology. The tutorial will explain how to model and structure data as RDF triples and create basic RDFS ontologies.

This document discusses ontology quality and the role of ontology design patterns (ODPs) in improving quality. It addresses three dimensions of ontology quality: correctness, precision, and accuracy. While ODPs aim to improve reusability, their simplicity may decrease interoperability if connections between patterns are overlooked. The original intent of competency questions was for more complex queries than simple lookups. Properly defining terms and examples/counter-examples for a target community helps improve an ontology's quality.

The document provides an overview of ontology and its various aspects. It discusses the origin of the term ontology, which derives from Greek words meaning "being" and "science," so ontology is the study of being. It distinguishes between scientific and philosophical ontologies. Social ontology examines social entities. Perspectives on ontology include philosophy, library and information science, artificial intelligence, linguistics, and the semantic web. The goal of ontology is to encode knowledge to make it understandable to both people and machines. It provides motivations for developing ontologies such as enabling information integration and knowledge management. The document also discusses ontology languages, uniqueness of ontologies, purposes of ontologies, and provides references.

This is the presentation slides for the joint conference of the 134th SIG conference of Information Fundamentals and Access Technologies (IFAT) and 112th SIG conference of Document Communication (DC), Information Processing Society of Japan (IPSJ)March 22, 2019, at Toyo University, Hakusan Campus. Cite: Kei Kurakawa, Yuan Sun, and Satoko Ando, Applying a new subject classification scheme for a database by a data-driven correspondence, IPSJ SIG Technical Report, Vol.2019-IFAT-134/2019-DC-112, No.7, pp.1-10, (2019).

A guide and a process for creating OWL ontologies. Semantic Web course e-Lite group (https://elite.polito.it) Politecnico di Torino, 2017

Fahim Imam Workshop on using Multiple Ontologies, International Conference on Biomedical Ontologies (ICBO 2011), Buffalo, NY July 26, 2011

The document defines ontologies as explicit descriptions of a domain that define concepts, properties, attributes, and constraints. It discusses the history of categorization in philosophy and the development of knowledge models like semantic nets and conceptual graphs. The document outlines different methods for building ontologies and different types of ontologies. It also discusses ontology tools like Protege and TopBraid Composer and how ontologies are used on the semantic web through languages like OWL.

This document outlines a course on Knowledge Representation (KR) on the Web. The course aims to expose students to challenges of applying traditional KR techniques to the scale and heterogeneity of data on the Web. Students will learn about representing Web data through formal knowledge graphs and ontologies, integrating and reasoning over distributed datasets, and how characteristics such as volume, variety and veracity impact KR approaches. The course involves lectures, literature reviews, and milestone projects where students publish papers on building semantic systems, modeling Web data, ontology matching, and reasoning over large knowledge graphs.

We describe a rule-based approach for the automatic acquisition of salient scientific entities from Computational Linguistics (CL) scholarly article titles. Two observations motivated the approach: (i) noting salient aspects of an article’s contribution in its title; and (ii) pattern regularities capturing the salient terms that could be expressed in a set of rules. Only those lexico-syntactic patterns were selected that were easily recognizable, occurred frequently, and positionally indicated a scientific entity type. The rules were developed on a collection of 50,237 CL titles covering all articles in the ACL Anthology. In total, 19,799 research problems, 18,111 solutions, 20,033 resources, 1,059 languages, 6,878 tools, and 21,687 methods were extracted at an average precision of 75%.

Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.

Use of probabilistic topic models to create scalable representation of documents aim to: (1) organize, summarise and search them, (2) explore them in a way that you can index of ideas contained in them, and (3) browse them in a way that you can find documents dealing specific areas

This document proposes a methodology for discovering patterns in scientific literature using a case study of digital library evaluation. It involves: 1. Classifying documents to identify relevant papers using naive Bayes classification. 2. Semantically annotating papers with concepts from a Digital Library Evaluation Ontology using the GoNTogle annotation tool. Over 2,600 annotations were generated. 3. Clustering the annotated papers into coherent groups using k-means clustering. 4. Interpreting the clusters with the assistance of the ontology to discover patterns and trends in the literature. Benchmarking tests were performed to evaluate effectiveness of the methodology.

Ron Daniel and Corey Harper of Elsevier Labs present at the Columbia University Data Science Institute: https://www.elsevier.com/connect/join-us-as-elsevier-data-scientists-present-at-columbia-university

Amit Sheth with TK Prasad, "Semantic Technologies for Big Science and Astrophysics", Invited Plenary Presentation, at Earthcube Solar-Terrestrial End-User Workshop, NJIT, Newark, NJ, August 13, 2014. Like many other fields of Big Science, Astrophysics and Solar Physics deal with the challenges of Big Data, including Volume, Variety, Velocity, and Veracity. There is already significant work on handling volume related challenges, including the use of high performance computing. In this talk, we will mainly focus on other challenges from the perspective of collaborative sharing and reuse of broad variety of data created by multiple stakeholders, large and small, along with tools that offer semantic variants of search, browsing, integration and discovery capabilities. We will borrow examples of tools and capabilities from state of the art work in supporting physicists (including astrophysicists) [1], life sciences [2], material sciences [3], and describe the role of semantics and semantic technologies that make these capabilities possible or easier to realize. This applied and practice oriented talk will complement more vision oriented counterparts [4]. [1] Science Web-based Interactive Semantic Environment: http://sciencewise.info/ [2] NCBO Bioportal: http://bioportal.bioontology.org/ , Kno.e.sis’s work on Semantic Web for Healthcare and Life Sciences: http://knoesis.org/amit/hcls [3] MaterialWays (a Materials Genome Initiative related project): http://wiki.knoesis.org/index.php/MaterialWays [4] From Big Data to Smart Data: http://wiki.knoesis.org/index.php/Smart_Data

Document classification is a part of Natural language processing. We have different methodology and technique for processing the document classification. The purpose of this article is to survey some papers related to document classification. Those survey will help the researcher to understand which will be the best approach to use for natural language processing

study or concern about what kinds of things exist what entities there are in the universe. the ontology derives from the Greek onto (being) and logia (written or spoken). It is a branch of metaphysics , the study of first principles or the root of things.

My seminar on ontology engineering, mainly targeting linguists with a basic background in Web ontologies.

1. The document discusses representing ontology using the Classified Interrelated Object Model (CIOM) data modeling technique. CIOM represents ontology components like classes, subclasses, attributes, and relationships between classes. 2. Key components of an ontology like classes, subclasses, attributes, and inter-class relationships are described and examples are given of how each would be represented using CIOM notation. 3. CIOM provides a general purpose methodology for representing ontologies using existing database technologies and overcomes limitations of specialized ontology languages and tools.

This document discusses generating lexical information for terms in a bioinformatics ontology. It proposes a model called LexInfo for associating linguistic information with ontologies. The authors lexicalize a bioinformatics ontology called myGrid by creating a LexInfo-based lexicon that captures morphological, syntactic and semantic properties of terms. They generate lexicons both semi-automatically using domain resources and automatically using LexInfo tools. The automatic lexicon has some errors due to POS tagging and tokenization issues that could be addressed using domain knowledge. The enriched ontology may help with automatic annotation of bioinformatics services.

This document summarizes an OKFN Korea hackathon event focused on open data. It discusses modeling Seoul open government data using ontologies, linking it to external datasets like cultural heritage data, and publishing the enriched data in RDF format. It covers topics like linked data, modeling with RDF/RDFS/OWL, reusing existing vocabularies, ontology development best practices, and triple store storage solutions.

This document provides an overview of the Next Generation Science Standards. It discusses that the standards were developed by Achieve in partnership with other organizations to create science standards focused on big ideas. It describes the Framework for K-12 Science Education that the standards are based on, which outlines three dimensions for each standard. It then explains the organization and structure of the Next Generation Science Standards, comparing them to previous standards.

Presented at DocTrain East 2007 by Joe Gelb, Suite Solutions -- Designing, building and maintaining a coherent information architecture is critical to proper planning, creation, management and delivery of documentation and training content. This is especially true when your content is based on a modular or topic-based model such as DITA and SCORM or if you are migrating to such a model. But where to start? Terms such as taxonomy, semantics, and ontology can be intimidating, and recognized standards like RDF, OWL, Topic Maps (XTM) and SKOS seem so abstract. This pragmatic workshop will provide an overview of the standards and concepts, and a chance to use them hands-on to turn the abstract into tangible skills. We will demonstrate how a well-designed information architecture facilitates reuse and how the information model is integrally connected to conditional and multi-purpose publishing. We will introduce an innovative, comprehensive methodology for information modeling and content development called SOTA Solution Oriented Topic Architecture. SOTA does not aim to be yet another new standard, but rather a concrete methodology backed up with open-source and accessible tools for using existing standards. We will demonstrate ֖and practice—hands-on—how this powerful methodology can help you organize and express information, determine which content actually needs to be created or updated, and build documentation and training deliverables from your content based on the rules you define. This workshop is essential for successfully implementing topic models like DITA and SCORM, multi-purpose conditional publishing, and successfully facilitating content reuse.

This document provides an overview of research methods for narrative analysis. It discusses key concepts in narrative analysis including scripts, stories, patterns, themes, coding, and temporal organization. It also covers approaches like contextual analysis, focus groups, retelling narratives, and assumptions related to subjectivity and usefulness. Narrative analysis is presented as an exploratory qualitative methodology to give respondents a venue to articulate their own viewpoints and standards.

http://KOKUIS.my/html5 HTML5 – refers to the modern day of HTML which promotes native handling of video & audio & animation without having to install additional plugins to browser. . Bootstrap – A HTML framework supports responsive web design to provide one time webpage development for smartphone, tablet and desktop. . Mobirise – a free web design studio that support HTML5 & Bootstrap’s famous ‘block’ design.

This document discusses developing mobile web apps using HTML5, jQuery, and PhoneGap/Apache Cordova. It covers the hybrid approach of using HTML/CSS/JavaScript for the front-end and PhoneGap to package it as a native mobile app. Tools mentioned include Apache Cordova, Node.js, Eclipse, and Xcode. It provides an overview of key topics to be covered in subsequent days, such as the mobile web page structure using jQuery Mobile, connecting to online databases using PHP and MySQL, and building apps with PhoneGap Build.