Research Items for Project
Here are some major research topics regarding project description:
- Medical Ontologies: Ontology is defined as a set of concepts and categories in a subject area or domain that shows their properties and the relations between them. Therefore, a medical ontology is the set of concepts of medical terminologies and the relations between them. Medical ontology enables the sharing of medical knowledge. In our Medical Experience Sharing Platform Project, such medical knowledge must be provided to the users. Therefore, use of a medical ontology will provide this knowledge to our platform.
To give an idea of what an ontology is, a small example of a medical ontology is visualized below:
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Biomedical Ontologies and Controlled Vocabularies Information about and resources for working with biomedical ontologies and controlled vocabularies
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BioSharing.org: A curated, informative and educational resource on inter-related standards, databases, and policies in the life, environmental and biomedical sciences. BioSharing shows the relationships between terminologies and the databases, standards and policies which implement them.
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NCBO BioPortal: The National Center for Biomedical Ontologies maintains a comprehensive repository of biomedical ontologies.
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The OBO Foundry: A collective of ontology developers that are committed to collaboration and adherence to shared principles. The mission of the OBO Foundry is to develop a family of interoperable ontologies that are both logically well-formed and scientifically accurate.
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Where to Publish and Find Ontologies?
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A Survey of Ontology Libraries : A review of eleven ontology libraries, with guidance for choosing a library for finding or publishing ontologies.
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DBPedia:
- DBpedia (from "DB" for "database") is a project aiming to extract structured content from the information created in the Wikipedia project. This structured information is made available on the World Wide Web. DBpedia allows users to semantically query relationships and properties of Wikipedia resources, including links to other related datasets.
- In 2008,Tim Berners-Lee described DBpedia as one of the most famous parts of the decentralized LinkedData effort.
- The 2016-04 release of the DBpedia data set describes 6.0 million entities, out of which 5.2 million are classified in a consistent ontology, including 1.5M persons, 810k places, 135k music albums, 106k films, 20k video games, 275k organizations, 301k species and 5k diseases. DBpedia uses the Resource Description Framework (RDF) to represent extracted information and consists of 9.5 billion RDF triples, of which 1.3 billion were extracted from the English edition of Wikipedia and 5.0 billion from other language editions.
Resources: Oxford Living Dictionary, National Library of Medicine , ResearchGate , UNIVERSITY OF MICHIGAN LIBRARY, Wikipedia , dbpedia
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2021 Group 10 made a Contents table consisting of anchor text to the parts of the requirements page one might want to see directly. It is a convenience and we might add it to our page as well.
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2021 Group 9 modified their page to have collapsible sections. Here we can learn how to add this functionality to our page if we wanted to add this functionality, too.
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Several groups sorted their Glossary alphabetically.
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Some groups classified their users as "Guest" and "Registered" and wrote their requirements under these sub sections accordingly.
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Requirements are created according to their projects specifically. Some projects have distinct parts that requires a part in the requirements such as Event Requirements in a project that has "Events" in it. I think we could add a Chatbot Requirements for our project
First of all, what is a chatbot?
A chatbot is a type of software that can help customers by automating conversations and interacting with them through messaging platforms. Chatbots are used in dialog systems for various purposes including customer service, request routing, or information gathering. Most chatbots are accessed online via website popups or through virtual assistants.
So, what are the advantages and disadvantages of chatbots?
Advantages:
- Chatbots help you get to know the customers. They figure out what the customer needs and what their questions are. Thus, action can be taken.
- Chatbots offer customer service at any given time, 7/24. The customer can find answers to their questions in just a matter of time with the help of chatbots without a need for waiting.
- Chatbots optimize the costs and the time spent. From the perspective of the customer, they spent less time for solutions while seeking through the internet since they can get answers instantly. From the perspective of the company, they spent less money on live-support since one bot can be cheaper than several human supporters.
Disadvantages:
- Chatbots are not human agents. Since they have a limited understanding of queries, they may suffocate at some point. The problem can be solved with an improved AI.
- Chatbots need regular revision, maintenance, and optimization just like any other software. When you think of their knowledge base should grow with every new customer, maintenance becomes a priority.
Some additional information about Telegram's Chatbot API
The Bot API is an HTTP-based interface created for developers who are interested in building chat bot applications. In other words, the Bot is an automated software application that does some tasks repeatedly which runs inside the telegram. Using Bot API allows you to manage HTTP requests. It can be integrated with third party web services. Besides that, Telegram Bots do not require any additional installation, they run on any platform that supports Telegram. The Bots can be created for social interactions, productivity, gaming, e-commerce services. Apart from these, they can also provide customer support on different platforms.
Some useful repositories for Chatbot API:
-For .NET
-For Node.js
Resources: Chatbot Wikipedia, Aivo, Telegram
In its literal meaning, annotation is the practice and the result of adding a note or gloss to a text, which may include highlights or underlining, comments, footnotes, tags, and links. In physical documents, an annotation can be writing within the page of a book or highlighting a line in the book.
In the digital context, it is the act of creating associations between distinct pieces of information, and it is a pervasive activity online in many guises. For example, comments about shared photos or videos, reviews of products, or even social network mentions of web resources can be considered as web annotations.
Annotations can be used to "provide a trace of use; third party commentary; information sharing; information filtering; semantic labeling of document content; and enhanced search". Annotations can help readers discover new content by subscribing to annotation feeds. They can also share annotations, thereby creating communities of common interests. Publishers can use annotations to add value to their content.
When we look at annotation tools and services; mosaic browser that was released in 1993 had support for annotations. Third Voice was an annotation service during 1999-2001. Predating this, there have been other open-source annotation apps: CritSuite, JotBot, ComMentor and Xanadu. Angel-funded Fleck existed during 2006-2008. At the 2013 I Annotate Conference, many new services were presented: Domeo, Maphub, Pelagios, Authorea, dotdotdot, Hypothes.is.
To standardize the use and the structure of these web annotations, World Wide Web Consortium (W3C) which is the main international standards organization for the World Wide Web, announced the W3C Web Annotation Data Model on February 23rd, 2017.
The Web Annotation Data Model specification describes a structured model and format to enable annotations to be shared and reused across different hardware and software platforms. The specification provides a specific JSON format for ease of creation and consumption of annotations based on the conceptual model that accommodates many different use cases.
The Web Annotation Data Model is defined using the following basic principles:
- An Annotation is a rooted, directed graph that represents a relationship between resources.
- There are two primary types of resource that participate in this relationship, Bodies and Targets.
- Annotations have 0 or more Bodies.
- Annotations have 1 or more Targets.
- The content of the Body resources is related to, and typically "about", the content of the Target resources.
- Annotations, Bodies and Targets may have their own properties and relationships, typically including creation and descriptive information.
- The intent behind the creation of an Annotation or the inclusion of a particular Body or Target is an important property and represented by a Motivation resource.
{
"@context": "http://www.w3.org/ns/anno.jsonld",
"id": "http://example.org/anno1",
"type": "Annotation",
"body": "http://example.org/post1",
"target": "http://example.com/page1"
}
- https://en.wikipedia.org/wiki/Text_annotation
- https://www.w3.org/TR/annotation-model/
- https://en.wikipedia.org/wiki/World_Wide_Web_Consortium
- https://web.hypothes.is/blog/annotation-is-now-a-web-standard/
- https://commons.wikimedia.org/wiki/File:Web-anno-intro.png
- https://devopedia.org/web-annotation#qst-ans-1
- https://devopedia.org/web-annotation#qst-ans-2
