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Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research
 
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Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 3330 ClickMyProject
Final Year Projects| An Ontology-Based Text-Mining Method to Cluster Proposals for Research
 
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Final Year Projects | An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 1298 ClickMyProject
Text mining for ontology learning and matching
 
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http://togotv.dbcls.jp/20141117.html NBDC / DBCLS BioHackathon 2014 was held in Tohoku Medical Megabank in Sendai and Taikanso in Matsushima, Miyagi, Japan. Main focus of this BioHackathon is the standardization and utilization of human genome information with Semantic Web technologies in addition to our previous efforts on semantic interoperability and standardization of bioinformatics data and Web services. (read more about the past hackathons...) On the first day of the BioHackathon (Nov. 9), public symposium of the BioHackathon 2014 was held at Tohoku Medical Megabank in Sendai. In this talk, Jung-Jae Kim (Nanyang Technological University, Singapore) makes a presentation entitled "Text mining for ontology learning and matching". (16:09)
Views: 1658 togotv
An Ontology Based Text Mining Framework for R&D Project Selection
 
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An Ontology Based Text Mining Framework for R&D Project Selection ieee project in java
Views: 334 satya narayana
Ontology-based workflow extraction from texts using word sense disambiguation
 
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Author: Ahmed Halioui, The Université du Québec à Montréal Abstract: This paper introduces a method for automatic workflow extraction from texts using Process-Oriented Case-Based Reasoning (POCBR). While the current workflow management systems implement mostly different complicated graphical tasks based on advanced distributed solutions (e.g. cloud computing and grid computation), workflow knowledge acquisition from texts using case-based reasoning represents more expressive and semantic cases representations. We propose in this context, an ontology-based workflow extraction framework to acquire processual knowledge from texts. Our methodology extends classic NLP techniques to extract and disambiguate tasks in texts. Using a graph-based representation of workflows and a domain ontology, our extraction process uses a context-based approach to recognize workflow components : data and control flows. We applied our framework in a technical domain in bioinformatics : i.e. phylogenetic analyses. An evaluation based on workflow semantic similarities on a gold standard proves that our approach provides promising results in the process extraction domain. Both data and implementation of our framework are available in : http://labo.bioinfo.uqam.ca/tgrowler. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 66 KDD2017 video
Final Year Projects | An Ontology-based Approach to Text Summarization
 
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Including Packages ===================== * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 619 ClickMyProject
Final Year Projects 2015 |  Feature Based opinion mining through ontologies
 
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Including Packages ===================== * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 102 ClickMyProject
Auto Tagging: Semantic Technologies, Ontology Engineering
 
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We specialize in developing semantic technologies and ontologies for auto tagging of unstructured text. In contrast to the named entity recognition, our auto tagging software is a semantic technology that provides high accuracy and a strict focus on your domain of interest. For every client we develop proprietary ontology designed specifically for the client's domain of interest. Unlike named entity recognition semantic technology doesn't just process text -- it makes sense of it.
Ontology Systems | New to Ontology
 
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Ontology CEO, Benedict Enweani, explains how Ontology's semantic technology can search and centralise core applications, databases, big data sources, files, spread sheets, documents, emails... anywhere, without the cost and risk of integration. Search, don't integrate. For more information about this video visit: http://www.goramandvincent.com/work/ontology-systems
Views: 524 Goram & Vincent
Ontology-based integration and analysis of phenotypes
 
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Original version is here http://togotv.dbcls.jp/20110821.html NBDC / DBCLS BioHackathon 2011 was held in Kyoto, Japan. Main focus of the BioHackathon is to develop technologies for handling Linked Data in life science. The participants discussed, explored and developed SPARQL endpoints, semantic web services, triple stores, ontologies, natural language processing, visualization and Open Bio* tools to utilize RDF data. On the first day of the BioHackathon (Aug. 21), public symposium of the BioHackathon 2011 was held at Campus Plaza Kyoto. In this talk, Robert Hoehndorf makes a presentation entitled "Ontology-based integration and analysis of phenotypes."
Views: 175 togotv
What is an Ontology
 
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Description of an ontology and its benefits. Please contact [email protected] for more information.
Views: 135566 SpryKnowledge
SEMANTIXS: Ontology-guided information extraction from unstructured text - [lo-res version]
 
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SEMANTIXS: System for Extraction of doMAin-specific iNformation from Text Including compleX Structures Refer the following links for more information on the project and related thesis -- - http://www.cs.iastate.edu/~semantix/ - http://sourceforge.net/projects/semantixs/
Views: 1788 semantixs
Ontology-based integration and analysis of phenotypes
 
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NBDC / DBCLS BioHackathon 2011 was held in Kyoto, Japan. Main focus of the BioHackathon is to develop technologies for handling Linked Data in life science. The participants discussed, explored and developed SPARQL endpoints, semantic web services, triple stores, ontologies, natural language processing, visualization and Open Bio* tools to utilize RDF data. On the first day of the BioHackathon (Aug. 21), public symposium of the BioHackathon 2011 was held at Campus Plaza Kyoto. In this talk, Robert Hoehndorf makes a presentation entitled "Ontology-based integration and analysis of phenotypes." http://2011.biohackathon.org/documents/symposium
Views: 377 togotv
Final Year Projects 2015 | An Ontology-based Approach to Text Summarization
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 2559 myproject bazaar
ML could solve NLP challenges - Ontology Management - Erik Huddleston
 
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Erik Huddleston - CEO of TrendKite H2O World 2015, Day 3 Contribute to H2O open source machine learning software https://github.com/h2oai Check out more slides on open source machine learning software at: http://www.slideshare.net/0xdata
Views: 934 H2O.ai
Information Extraction Based on Extraction Ontologies: Design, Deployment and Evaluation
 
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TO USE OR PRINT this presentation click : http://videosliders.com/r/898 ============================================================== Information Extraction Based on Extraction Ontologies: Design, Deployment and Evaluation Martin Labský, Vojtěch Svátek Dept. of Knowledge Engineering, UEP {labsky,svatek}@vse.cz AI Seminar, November 13th 2008 ,Agenda Example applications of Web IE Difficulties in practical applications Extraction Ontologies Extraction process Experimental results Future work and Conclusion AI Seminar IE based on Extraction Ontologies ,Example apps of Web IE (1/5): online products AI Seminar IE based on Extraction Ontologies ,Example apps of Web IE (2/5): contact information AI Seminar IE based on Extraction Ontologies ,Example apps of Web IE (3/5): seminars, events AI Seminar IE based on Extraction Ontologies ,Example apps of Web IE (4/5): bike products AI Seminar IE based on Extraction Ontologies ,Example apps of Web IE (4/5) Store the extracted results in a DB to enable structured search over documents information retrieval database-like querying e.g. online product search engine, e.g. building a contact DB Support for web page quality assessment involved in an EU project MedIEQ to support medical website accreditation agencies Source documents internet, intranet, emails can be very diverse AI Seminar IE based on Extraction Ontologies ,Agenda Example applications of Web IE Difficulties in practical IE applications Extraction Ontologies Extraction process Experimental results Future work and Conclusion AI Seminar IE based on Extraction Ontologies ,Difficulties in practical applications (1/3) Requirements quickly prototype IE applications not necessarily with the best accuracy initially often needed for a proof-of-concept application then more work can be done to boost accuracy the extraction model changes meaning of to-be-extracted items may shift, new items are often added or removed AI Seminar IE based on Extraction Ontologies ,Difficulties in practical applications (2/3) Purely manual rules writing extraction rules manually does not scale when more complex extraction rules need to be encoded not easy to combine with trained models when training data become available in later phases Training data trainable IE systems often require large amounts of training data: these are typically not available for the desired task when training data is collected, it is not easy to adapt it to modified or additional criteria Wrappers cannot rely on wrapper-only systems when extracting from multiple websites non-wrapper systems often do not utilize regular formatting cues AI Seminar IE based on Extraction Ontologies ,Difficulties in practical applications (3/3) Seems interesting to exploit at the same time extraction knowledge from domain experts training data formatting regularities AI Seminar IE based on Extraction Ontologies ,Agenda Example applications of Web IE Difficulties in practical applications Extraction Ontologies Extraction process Experimental results Future work and Conclusion AI Seminar IE based on Extraction Ontologies ,Extraction ontologies An extraction ontology is a part of a domain ontology transformed to suit extraction needs Contains classes composed of attributes more like UML class diagrams, less like ontologies where e.g. relations are standalone also contains axioms related to classes or attributes Classes and attributes are augmented with extraction evidence manually provided patterns for content and context axioms value or length ranges links to trained models Person name {1} degree {0-5} email {0-2} phone {0-3} Responsible AI Seminar IE based on Extraction Ontologies ,Extraction
Views: 424 slide show me
Final Year Projects | Domain Ontology based Semantic Search
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 683 ClickMyProject
Towards Ontology Based Data Access for Statoil. Part 1: Introduction
 
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Ontology Based Data Access (OBDA) is a prominent approach to provide end-users with high-level access to data via an ontology that is 'connected' to the data via mappings. State-of-the-art OBDA systems, however, suffer from limitations restricting their applicability in industry. In particular, development of necessary prerequisites to deploy an OBDA system, i.e., ontologies and mappings, as well as end-user oriented query interfaces, are poorly addressed. Moreover, solutions often focus on separate critical components of OBDA systems, while, to the best of our knowledge, there is no end-to-end OBDA solution. The Optique platform provides an integrated end-to-end OBDA system that addresses a number of practical challenges including support for development of deployment prerequisites and user-oriented query interfaces. During the demonstration one can try the platform with preconfigured scenarios from the petroleum industry and music domain, and try its end-to-end functionality: from deployment to query answering. In the first part we provide a general description of the OBDA approach in general and our system particularly.
Views: 754 Optique Project
Ontology Based Geo-Information Retrival and Process
 
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The problem of accessing useful information and use of it make information retrieval popular and one of the most important research fields in computer science. In spatial domain, the need of organizing, storing, searching and accessing accurate information triggers the occurrence of spatial information retrieval concept. This study represents a survey about spatial information retrieval techniques. It defines different methods to provide geospatial data storing and searching and different index structures and how to combine them is also explained. Additionally, a geo-information retrieval architecture is proposed. Many indexing techniques have been proposed in information retrieval for past years. Inverted indexes are the most common type of these techniques. Before index creation some of the natural language processing methods (tokenizing text, eliminating stop words, stemming, etc.) are used to create index terms. These terms are written to the index in an inverted way. As seen from Figure 1 [9], an inverted index associates to each word in the text (organized as a vocabulary) the list of pointers to the positions where the word appears in the documents. The inverted file index stores for each keyword, a sorted list of object ids in which the keyword appears its score, and frequency. by C. Öcal , M. Komesli
Ontology Based Categorization of Habitat Entities using Information Retrieval Techniques
 
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by Hakan Şahin and Mert Tiftikci Advisor : Arzucan Özgür A graduation project for the degree of Bachelor of Science submitted to the Department of Computer Engineering, Boğaziçi University, Spring 2016 This application acts as a tool for the normalization of habitat entities extracted from scientific paper abstracts with using OntoBiotope ontology. Even though it is applied on biomedical domain, the tool is applicable to other domains as well. The aim of this application is to overcome the problem addressed as a "Entity Categorization" subtask of the BioNLP Bacteria Biotope (BB) Shared Task 2016, which represents growing tendency towards information extraction for biology. Project Report : https://drive.google.com/open?id=0B1TLHKq4yUAPRHlpUnBUcXhvSUk
Views: 101 Hakan Şahin
An Incremental and Distributed Inference Method for Large-Scale Ontologies Based on MapReduce
 
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An Incremental and Distributed Inference Method for Large-Scale Ontologies Based on MapReduce Paradigm To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Views: 222 jpinfotechprojects
SEMANTIXS: A system for ontology-guided extraction of structured information from unstructured text
 
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SEMANTIXS: System for Extraction of doMAin-specific iNformation from Text Including compleX Structures Refer the following links for more information on the project and related thesis -- - http://www.cs.iastate.edu/~semantix/ - http://sourceforge.net/projects/semantixs/
Views: 527 semantixs
How NLP text mining works: find knowledge hidden in unstructured data
 
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Connect with us: http://www.linguamatics.com/contact What use is big data if you can't find what you're looking for? Follow: @Linguamatics https://twitter.com/Linguamatics https://www.linkedin.com/company/linguamatics https://www.facebook.com/Linguamatics https://plus.google.com/+Linguamatics https://www.youtube.com/user/Linguamatics/videos In knowledge driven industries such as the life sciences and healthcare, finding the right information quickly from huge volumes of text is crucial in supporting the best business decisions. However, around 80% of available information exists as unstructured text, and conventional keyword searches only retrieve documents, which still have to be read. This is very time consuming, unreliable, and, when important decisions rest on it, costly. Linguamatics’ text mining solution, I2E, uses Natural Language Processing to identify and extract relevant knowledge at least 10 times faster than conventional search, often uncovering insights that would otherwise remain unknown. I2E analyses the meaning of the text using powerful linguistic algorithms, enabling you to ask open questions, find the relevant facts and identify valuable connections. Going beyond simple keywords, I2E can recognise concepts and the different ways the same thing can be expressed, increasing the recall of relevant information. I2E then presents high quality results as structured, actionable knowledge, enabling fast review and analysis, and providing dramatically improved speed to insight. Our market leading software is supported by highly qualified domain experts who work with our customers to ensure successful project outcomes. Text mining for beginners: https://www.youtube.com/watch?v=40QIW9Sr6Io
Views: 14368 Linguamatics
Kriton Speech: Knowledge Acquisition for Ontologies
 
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Kriton Speech is a general purpose knowledge acquisition tool incorporating a variety of elicitation methods, such as interview techniques, protocol analysis, text mining and machine learning. Psychological interview techniques are used to obtain domain knowledge from an expert, in this case a clinical psychologist. Kriton Speech uses voice as the user interface. The system interviews the user and as a result, builds ontologies and rule-based systems. The output is a Web Ontology Language (OWL) file that can be edited by use of ontology editors such as Protege. Please see http://psychologynetwork.com.au/KritonSpeechWhitePaper.pdf. For more information, email [email protected]
Views: 877 Psychology Network
Minimal Semantic Units in Text Analysis
 
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Speaker: Jake Ryland Williams, Drexel University Presented on December 1, 2017, as part of the 2017 TextXD Conference (https://bids.berkeley.edu/events/textxd-conference) at the Berkeley Institute for Data Science (BIDS) (bids.berkeley.edu).
Towards Ontology Based Data Access for Statoil. Part 3: Installation
 
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Ontology Based Data Access (OBDA) is a prominent approach to provide end-users with high-level access to data via an ontology that is 'connected' to the data via mappings. State-of-the-art OBDA systems, however, suffer from limitations restricting their applicability in industry. In particular, development of necessary prerequisites to deploy an OBDA system, i.e., ontologies and mappings, as well as end-user oriented query interfaces, are poorly addressed. Moreover, solutions often focus on separate critical components of OBDA systems, while, to the best of our knowledge, there is no end-to-end OBDA solution. The Optique platform provides an integrated end-to-end OBDA system that addresses a number of practical challenges including support for development of deployment prerequisites and user-oriented query interfaces. During the demonstration one can try the platform with preconfigured scenarios from the petroleum industry and music domain, and try its end-to-end functionality: from deployment to query answering. In the third part, we overview the installation functionality of the system.
Views: 405 Optique Project
The Ontology of Emotions
 
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The term ‘ontology’ is nowadays employed in the information-driven sciences to refer to a controlled structured vocabulary of terms used to describe the entities in a given domain of study. Ontologies of this sort are being successfully employed especially within the biomedical sciences, where they are used to annotate or tag scientific research results deriving from multiple heterogeneous sources to enable searching, integration and analysis of data within a single common framework. We will describe the Emotion Ontology, which has been developed to support interdisciplinary research into emotions and other affective phenomena in fields such as cognitive neuroscience, behavioral psychology, psychiatry, literary analysis, and artificial intelligence. Problems arise because each of these disciplines has its own separate terminology and systems of codes. The ontology provides a common language in which results gathered separately can be brought together into a single whole. To be effective, the terms and relations in an ontology must be provided with logical definitions which enable reasoning with the data, and it is at this point that philosophical issues play a role. We summarize the philosophical background of contemporary ontology research, pointing above all to the work of Husserl. We then outline how emotions are defined in the ontological framework, setting forth their relations to acts of appraisal, subjective feelings, emotional action tendencies, and physiological responses. We also sketch how ontology-tagged data is being used for purposes of what is called ‘sentiment mining’ for example in identifying subjects with high risk of suicide. We conclude with an application of the Emotion Ontology to aproblems in aesthetics relating to the pleasure people experience when watching horror films while undergoing the normally unpleasurable subjective feelings associated with fear.
Views: 319 Barry Smith
Ontology-based annotation and retrieval of services in the cloud Java Project
 
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Project Link : http://kasanpro.com/p/java/ontology-based-annotation-retrieval-services-cloud , Title :Ontology-based annotation and retrieval of services in the cloud
Views: 111 kasanpro
Natural Language Processing with Graphs
 
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William Lyon, Developer Relations Enginner, Neo4j:During this webinar, we’ll provide an overview of graph databases, followed by a survey of the role for graph databases in natural language processing tasks, including: modeling text as a graph, mining word associations from a text corpus using a graph data model, and mining opinions from a corpus of product reviews. We'll conclude with a demonstration of how graphs can enable content recommendation based on keyword extraction.
Views: 29573 Neo4j
An Incremental and Distributed Inference Method for Large-Scale Ontologies Based on MapReduce
 
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An Incremental and Distributed Inference Method for Large-Scale Ontologies Based on MapReduce Paradigm TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com With the upcoming data deluge of semantic data, the fast growth of ontology bases has brought significant challenges in performing efficient and scalable reasoning. Traditional centralized reasoning methods are not sufficient to process large ontologies. Distributed reasoning methods are thus required to improve the scalability and performance of inferences. This paper proposes an incremental and distributed inference method for large-scale ontologies by using MapReduce, which realizes high-performance reasoning and runtime searching, especially for incremental knowledge base. By constructing transfer inference forest and effective assertional triples, the storage is largely reduced and the reasoning process is simplified and accelerated. Finally, a prototype system is implemented on a Hadoop framework and the experimental results validate the usability and effectiveness of the proposed approach.
Views: 268 jpinfotechprojects
Final Year Projects| Domain Ontology based Semantic Search for Efficient Information Retrieval
 
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Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 +91 958-553-3547 +91 967-774-8277 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected] chat: http://support.elysiumtechnologies.com/support/livechat/chat.php
Views: 3235 myproject bazaar
Custom Named Entity Recognition with Spacy in Python
 
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Spacy is a Python library designed to help you build tools for processing and "understanding" text. It can be used to build information extraction or natural language understanding systems, or to pre-process text for deep learning. This talk will discuss how to use Spacy for Named Entity Recognition, which is a method that allows a program to determine that the Apple in the phrase "Apple stock had a big bump today" is a company and not a pie filling. We'll also cover how to add your own entities, train a custom recognizer, and deploying your model as a REST microservice. Expertise with Spacy (https://spacy.io/) is not required. Presented by Josh Smith at Code & Supply
Views: 1608 Code & Supply
Ontology in Data Mining
 
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For free academic projects, please visit www.freeacademicprojects.com, just register and get your project with free* of cost. * Conditions Apply Call us on +9140 64512789 To get the projects list we have logon to http://www.freeacademicprojects.com/ To like our facebook page follow this link https://www.facebook.com/freeacademicprojects To like our Company please follow this link https://www.facebook.com/srujanatechnologies
Strategies for effective learning outcomes with students new to text mining and text analysis
 
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Kelvin Smith Library 2014 Digital Scholarship Colloquium Strategies for effective learning outcomes with students new to text mining and text analysis Mace Mentch, Consultant, Instructional Design and Technology 11/6/2014 Depending on the source, it has been estimated that 80% of existing data is in the form of unstructured text. The processes and methods used to transform unstructured textual data into structured data through turning the text into numbers and then back into text to discover relationships and create knowledge is complex. This presentation will cover methods derived from instructional systems design that serve to effectively facilitate student learning outcomes for the text mining and text analysis process. Colloquium website: http://library.case.edu/ksl/freedmancenter/colloquium/2014colloquium/
Views: 73 case
What is ontology? Introduction to the word and the concept
 
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In a philosophical context 0:28 Why ontology is important 1:08 Ontological materialism 1:34 Ontological idealism 1:59 In a non-philosophical context 2:24 Information systems 2:40 Social ontology 3:25 The word ontology comes from two Greek words: "Onto", which means existence, or being real, and "Logia", which means science, or study. The word is used both in a philosophical and non-philosophical context. ONTOLOGY IN A PHILOSOPHICAL CONTEXT In philosophy, ontology is the study of what exists, in general. Examples of philosophical, ontological questions are: What are the fundamental parts of the world? How they are related to each other? Are physical parts more real than immaterial concepts? For example, are physical objects such as shoes more real than the concept of walking? In terms of what exists, what is the relationship between shoes and walking? Why is ontology important in philosophy? Philosophers use the concept of ontology to discuss challenging questions to build theories and models, and to better understand the ontological status of the world. Over time, two major branches of philosophical ontology has developed, namely: Ontological materialism, and ontological idealism. Ontological materialism From a philosophical perspective, ontological materialism is the belief that material things, such as particles, chemical processes, and energy, are more real, for example, than the human mind. The belief is that reality exists regardless of human observers. Ontological idealism Idealism is the belief that immaterial phenomenon, such as the human mind and consciousness, are more real, for example, than material things. The belief is that reality is constructed in the mind of the observer. ONTOLOGY IN A NON-PHILOSOPHICAL CONTEXT Outside philosophy, ontology is used in a different, more narrow meaning. Here, an ontology is the description of what exist specifically within a determined field. For example, every part that exists in a specific information system. This includes the relationship and hierarchy between these parts. Unlike the philosophers, these researchers are not primarily interested in discussing if these things are the true essence, core of the system. Nor are they discussing if the parts within the system are more real compared to the processes that take place within the system. Rather, they are focused on naming parts and processes and grouping similar ones together into categories. Outside philosophy, the word ontology is also use, for example, in social ontology. Here, the idea is to describe society and its different parts and processes. The purpose of this is to understand and describe the underlying structures that affect individuals and groups. Suggested reading You can read more about ontology in some of the many articles available online, for example: http://www.streetarticles.com/science/what-is-ontology Copyright Text and video (including audio) © Kent Löfgren, Sweden
Views: 245732 Kent Löfgren
Cell Line Ontology-based Standardization, Integration and Analysis of LINCS Cell Lines
 
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In the LINCS data science research webinar which took place on December 20, 2016, Yongqun "Oliver" He DVM, PhD, University of Michigan Medical School (DCIC External Data Science Research Project) presented "Cell Line Ontology-based Standardization, Integration and Analysis of LINCS Cell Lines". A biomedical ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific biomedical domain and how they relate to each other. Ontologies have played a critical role in biomedical data, metadata, and knowledge standardization, exchange, integration, as well as inferring new knowledge. Cell lines have been widely used in biomedical research. As a member of the Open Biological/Biomedical Ontologies (OBO) Foundry library ontologies, the community-based Cell Line Ontology (CLO) covers the domain of cell lines. Co-developed by many ontology development groups and societies, CLO has established consensus definitions of cell line-specific terms such as ‘cell line’, ‘cell line cell’, and ‘cell line culturing’. A community-agreed CLO cell line design pattern has also been established and used for CLO to represent nearly 40,000 cell lines from various resources such as ATCC, HyperCLDB, Coriell Cell lines, and Japan Riken cell lines. CLO has also been used in different applications, for example, in the modeling of cell line cell-vaccine/pathogen interactions. Cell lines are crucial to study molecular signatures and pathways, and are widely used in the NIH Library of Integrated Network-based Cellular Signatures (LINCS) project. To better serve the LINCS research community, we generated a CLO subset/view (LINCS-CLOview) of LINCS cell lines. The LINCS-CLOview includes 1,097 LINCS cell lines. For each cell line, the CLO subset includes its cell type, original tissue/organ/organism information, and associated disease, and how these entities are related. In total, 121 diseases, including three benign neoplasms (e.g., breast fibrocystic disease associated with MCF10A and MCF 10F cell lines) and 118 various types of cancers, were found and laid out using the hierarchical structure of the Disease Ontology (DOID). These LINCS cell lines are also associated with various human or mouse tissue and organ types, represented by 131 UBERON ontology terms. Forty-three cell types, represented by the Cell Type Ontology (CL), are associated with LINCS cell lines. The information in the CLO LINCS subset can be easily queries using SPARQL scripts, and it can also be used to enhance the performance of website queries of cell line related information. Next stage of CLO-related research will also be discussed. For example, we are currently developing a CLO branch to represent various stem cell lines. CLO can also be further developed to model and analyze the data and knowledge of cell line gene transfection, cell signature markers, drug-induced molecular interaction pathways, and other cell line phenotypes. Overall, CLO can be used as an ontological basis and extendable platform to support integrative and systematic cell line cell-based research. BD2K-LINCS DCIC: http://lincs-dcic.org LINCS Project: http://www.lincsproject.org/
Views: 184 BD2K-LINCS DCIC
SF Text: Jeff Lerman, Advantages of Modeling Knowledge in an Ontology @Groupon
 
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Ingenuity Systems (now a part of QIAGEN) provides software solutions to interpret biological datasets. By aligning customer data to the Ingenuity Knowedgebase (KB), datasets can be viewed and analyzed in the context of relevant biological and biomedical knowledge. The Ingenuity KB is represented as a frame-based ontology, and that structure facilitates a range of powerful features, including inference and testing functionality. I’ll discuss the ontology structure, building process, maintenance regime, and some use-cases. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 343 FunctionalTV
Ontology-Based Data Access and Integration by Roman Kontchakov
 
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"Ontology-Based Data Access and Integration" by Roman Kontchakov, Bikbeck University of London, UK Slides: https://www.dropbox.com/s/uvwcvdsoe2ulhcf/School-Roman-Kontchakov.pdf?dl=0
Views: 644 ISST Laboratory
Process Execution Through Application Ontologies
 
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Demonstrates knowledge-based, event driven, data centric interoperable process execution.
Views: 1705 fadyart.com
Knowledge Based Interactive Post Mining of Association Rules Using Ontologies
 
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In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. To overcome this drawback, several methods were proposed in the literature such as itemset concise representations, redundancy reduction, and post processing. However, being generally based on statistical information, most of these methods do not guarantee that the extracted rules are interesting for the user. Thus, it is crucial to help the decision-maker with an efficient postprocessing step in order to reduce the number of rules. This paper proposes a new interactive approach to prune and filter discovered rules. First, we propose to use ontologies in order to improve the integration of user knowledge in the post processing task. Second, we propose the Rule Schema formalism extending the specification language proposed by Liu et al. for user expectations. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. Applying our new approach over voluminous sets of rules, we were able, by integrating domain expert knowledge in the post processing step, to reduce the number of rules to several dozens or less. Moreover, the quality of the filtered rules was validated by the domain expert at various points in the interactive process.
Ontology-Driven Extraction ProM Plug-in
 
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This is a tutorial that show how to use "Ontology-Driven Extraction Plugin" in ProM
Views: 86 Alifah Syamsiyah
Large-scale information extraction through text mining
 
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Original version is here http://togotv.dbcls.jp/20110826.html NBDC / DBCLS BioHackathon 2011 was held in Kyoto, Japan. Main focus of the BioHackathon is to develop technologies for handling Linked Data in life science. The participants discussed, explored and developed SPARQL endpoints, semantic web services, triple stores, ontologies, natural language processing, visualization and Open Bio* tools to utilize RDF data. On the first day of the BioHackathon (Aug. 21), public symposium of the BioHackathon 2011 was held at Campus Plaza Kyoto. In this talk, Martin Gerner makes a presentation entitled "Large-scale information extraction through text mining."
Views: 183 togotv