Technological advances in the field of machine learning (ML) are opening up new opportunities for the Semantic Web. By combining machine learning techniques with fundamental principles related to artificial intelligence, the Semantic Web can be transformed into a smarter and more efficient platform for users.
In this article, we will explore how machine learning can be applied to the Semantic Web and what the benefits and challenges are.
What is the Semantic Web?
The Semantic Web is a concept that has gained considerable interest in recent years. It is a way to make website content more accessible by organizing and interpreting data.
This allows users to find relevant information on the web more quickly. The Semantic Web is an extension of the web itself and uses semantics to extract meaning from structural data. The Semantic Web is characterized by the implementation of a system of linked data and ontologies that can be defined as networks of relationships between different concepts, classes and properties.
These ontologies can be used to create links between specific objects or concepts, allowing for more accurate and efficient searching on the web. In addition, the Semantic Web offers developers a way to model and manage information consistently across multiple applications and services. This allows for better data sharing, searchability and visibility, which is useful for businesses looking to expand on the web.
Semantic Web technology also offers the opportunity to use machine learning to analyze web data and draw relevant conclusions. Machine learning can be applied to various fields such as finance, health, agriculture and many others.
It can also be used to extract information from existing models or to create new models from unstructured raw data. The results obtained can then be used to create more intelligent systems capable of making decisions based on the analysis of available data. The Semantic Web is an essential technology if we want to fully exploit the possibilities offered by machine learning on the web.
Indeed, it provides an ad hoc framework to organize data so that it can be used efficiently by artificial intelligence (AI) systems. Moreover, it allows AI technologies to increase their ability to take into account the specific contexts they are confronted with, thus offering greater variety and practicality to existing systems.
Finally, the Semantic Web is essential to maintain the quality of the content available on the web because it allows AI systems to perform a deep analysis of the available information and thus bring relevant content to Internet users. Thanks to the Semantic Web, developers can create more intelligent applications capable of providing users with a personalized service based on their specific needs.
What is machine learning?
Machine Learning (ML) is a branch of artificial intelligence that focuses on developing computer systems that can acquire knowledge from data and use that knowledge to solve complex problems.
In other words, ML focuses on creating algorithms that can learn from data and adapt to new conditions without being explicitly programmed. This technology has been widely used to perform complex tasks, such as medical diagnosis, automatic natural language processing and speech recognition.
Machine learning is generally divided into three main categories: supervised learning, unsupervised learning and reinforcement learning. Supervised learning uses algorithms to learn from labeled data, which means there is some sort of human teaching involved. In this case, the algorithms learn from examples provided by a human teacher and then are tested on new data to verify their accuracy.
Unsupervised learning, on the other hand, involves finding hidden patterns or relationships in the data without any human guidance. Reinforcement learning is a more advanced type of machine learning where an autonomous system learns through interaction with its environment to maximize a defined performance metric.
The applications of Machine Learning are vast and varied.
It can be applied to almost any field related to information and communication technologies (ICT).
For example, it is used to analyze marketing databases to predict future trends and improve advertising campaigns; to improve computer image understanding and visual systems; to train robots to navigate and recognize their environments; or to develop expert systems that can solve problems without human intervention.
In addition, Machine Learning is also being used to create the Semantic Web, a network interconnecting the digital information sources available on the Internet to facilitate search and access to information. Machine Learning is fundamentally different from usual data processing because it allows machines to acquire complex behaviors without being explicitly programmed.
ML algorithms can evolve according to new parameters introduced and gradually adapt to changing conditions without losing accuracy or efficiency. With its growing application in various fields such as finance, the implementation of Machine Learning offers better management and faster processing of data.
Linguistic components of the Semantic Web
The linguistic components of the Semantic Web are an integral part of its structure and operation. These components are essential to understand and exploit the potential of machine learning technologies in the context of the Semantic Web.
One of the main linguistic components of the Semantic Web is the terminology or the specific vocabulary used to describe and represent concepts, entities and relationships between different elements of a web document. Terminology is an essential tool for creating links between documents, as well as for helping to understand and interpret the information contained in a web document.
Ontologies are another important linguistic component in the Semantic Web. Ontologies are an abstract view of conceptual structure that defines the relationships between different concepts, their attributes and properties. Ontologies can be used to organize and represent information on the web, providing a common framework for representing structured data. They can also be used to provide a common vocabulary model that allows software systems to understand the structure and content of web documents.
XML is one of the primary tools used in the Semantic Web to define the structure, syntax, and metadata associated with Web documents. XML enables software systems to understand and correctly interpret the content of documents, making it possible for different computer systems to interoperate and for data to be accessed by multiple applications.
Another important linguistic component is natural language (NL), which is a set of techniques that allow computers to understand the meaning and relationships between words in a sentence or text. Natural language allows computer systems to parse and extract relevant information from raw text, allowing direct machine involvement in various processes such as sentiment analysis or named entity identification.
Speech recognition is another technique related to automatic natural language processing (NLP). It allows computers to recognize and understand human spoken language by transforming sounds into computer-understandable sentences so that they can take an appropriate action. This technology is very useful for allowing users to perform complex tasks without having to type anything on a keyboard or touch screen.
Finally, semantic markup is a commonly used method of representing knowledge on the web by assigning specific keywords to each text or HTML content that can be found on the Internet. Tags make it easier for the web browser to recognize what relevant information is on each web page, making it easier to navigate through the various documents available on the Internet. In addition, it allows software systems to more easily access relevant information by directly querying the tags associated with each individual document.
Artificial Intelligence and Semantic Web
Artificial intelligence and the semantic web are technologies that improve the way we interact with the Internet. They offer a variety of possibilities for businesses and organizations around the world and can be applied to many fields.
Their applications are varied, and their potential is considerable. The semantic web is a technology that allows computers to interpret the content of web pages to understand their meanings. It uses metadata to describe the content of web pages, allowing computers to recognize specific information such as authors, titles, dates and associated keywords. It can also be used to create links between different types of content on the Internet.
Artificial intelligence is a computer technique that allows a computer to simulate human intelligence by taking into account various data and learning by itself from past experiences. It is widely used to solve complex problems such as voice and image recognition, automated decision making or sentiment analysis.
It also enables computers to perform various tasks without human intervention, such as machine learning-based decision making or predicting future behavior. When these technologies are combined, they can provide a better understanding of web content and help computers perform more complex tasks.
This can be used to improve web search and help find answers to complex questions more easily. Possible applications include extracting relevant information from large numbers of articles or creating virtual agents that can interact with the user. Advances in machine learning have also enabled the development of the semantic web.
Machine learning is a form of artificial intelligence that allows machines to analyze large amounts of data in depth to find hidden patterns and patterns that can be useful for solving difficult problems.
This technology can be applied to the semantic web to more efficiently extract relevant information from a large number of web pages and provide better understanding of web content and provide better answers to user queries.
This study explored the possibilities and benefits of machine learning on the semantic web. The results showed that the use of machine learning on the Semantic Web can provide a variety of solutions to improve the efficiency of information systems and applications. In addition, it can improve access to information while reducing costs and process complexity. In conclusion, machine learning on the Semantic Web offers significant benefits and can be an excellent solution for many companies and organizations.
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What is the goal of machine learning on the Semantic Web?
The goal of Machine Learning on the Semantic Web is to develop computer systems that can interpret and analyze the data contained in the Semantic Web and improve their accuracy by learning from the examples.
What are the main advantages of machine learning on the Semantic Web?
The main benefits of machine learning on the Semantic Web are the ability to capture knowledge from complex data, the ability to reuse acquired knowledge and the improvement of system performance.
Since its birth in 2016, Google Assistant has not stopped enriching itself with new features. With Memory, Google wants to bring order to your digital life this time.
Since its inception, Google’s mission has been “to organize the world’s information to make it accessible and useful.” After doing it for the web, Google wants to do it for your life.
The site 9to5Google has indeed just spotted a new feature in test within Google Assistant called “Memory”. Halfway between the to-do list, a note-taking application and a Pocket-like content backup service, Memory aims to put order in your digital life.
A platform to save everything
In concrete terms, Memory is a kind of digital whiteboard on which you can pin complete press articles with the source URL, photos, handwritten notes, drafts of ideas, reminders and a lot of other content such as information about an upcoming trip or a movie that is about to be released.
In short, everything displayed on your smartphone screen can be saved in Memory using a voice command or a shortcut to be placed on the home screen of your mobile. Each item will take the form of a small card arranged antechronologically and can be “tagged” to put it in an appropriate category.
Putting Google Assistant at the heart of your digital life
But where Google wants to add value is on the context surrounding each content. Memory will be able to automatically enrich your notes by integrating, for example, a YouTube trailer if you save the page of a movie you want to see or by highlighting the cooking time of a recipe you have saved. And of course, it will be possible to search through all the saved content.
Memory is therefore a way of placing Google Assistant even more at the heart of your digital life by offering a sort of hybrid to-do list available on your smartphones, tablets and connected screens. The feature is currently being tested internally at Google and no deployment date has yet been announced.
The origin of the integration of structured data does not date from yesterday. The idea of a structured web goes back to 1994 with the creation of the W3C led by Tim Berners-Lee, father of the web, confirmed in 1998 (with the beginning of the work in 1999). It will take a few years of work to define the first protocols and formats.
A rise in industrial power
From 2004 onwards, several protocols and formats to structure data appeared: RDF (Resource Description Framework, the basic language of the Semantic Web), its complement RDF Schema (which brings together processes and tools to define the ontologies that structure RDF resources); OWL (Web Ontology Language, which defines the RDF vocabulary) and SPARQL (a language based on RDF to query data resources).
All these tools, although they allow data to be structured, have yet to be widely used in information management tools. The major groups are not mistaken and “we have seen an increase in industrial use since 2007,” says Fabien Gandon, a researcher at Inria (the French National Institute for Research in Computer Science and Control).
For him, six main issues are at the heart of the development of Web 3.0. First, the confirmation of standards, which benefit from the feedback of 6 years of experience, and redefined in the RDFa 1.1. Then, the massive implementation of the Semantic Web through large companies (Oracle, IBM but also Yahoo even if the question arises since the integration of its search engine in Microsoft Bing, or Google which seems to do “websem” without admitting it). “The full deployment requires tools that are ready,” says the researcher.
Creating an ecosystem around data
Putting public data online is also among the most important scenarios to contribute to the diffusion of the technology. But the issue is necessarily political and its dissemination is likely to be delayed as the interests of some (citizens in particular) are not necessarily those of industry (which wants to monetize this value of information).
“We have a lot of trouble getting people to understand that we can create an ecosystem around data and not value on the real time processing of information”, Nicolas Chauvat regrets. Another issue is skills: “One out of every two phone calls I receive is about a profile search,” says Fabien Gandon. There is a real demand for semantic web engineers and technicians, and also a need for decision makers. In other words, as long as the critical mass of skills is not reached, there is no salvation for the semantic web.
An obstacle that the propagation of the dynamics to go beyond the implementation of standards will allow to reach. This obviously implies continuing research work, particularly through the scaling of processing, taking into account new uses such as mobility and social networks, but also the quality of data and the need to keep interaction as simple as possible through interfaces adapted to democratize the use of the semantic web.
The future of companies faced with mountains of data is at stake, which tends to become more common after a certain size. “Semantic web technologies are emerging as a solution for information management,” concludes the Inria spokesperson.
A partnership agreement has just been signed between Google and Inria. The American giant wants to promote research projects related to the semantic web and machine learning.
Google has strong needs in terms of fundamental research. In order to quench its thirst for new technologies, the firm is getting closer to Inria, with which it has just set up a strategic partnership.
According to the terms of the agreement signed on Monday, the National Institute for Research in Computer Science and Control will “support Google’s innovation strategy” by facilitating interactions with its teams.
In return, the Mountain View Internet group will guarantee the French institute privileged access to its research grants, which are generally conditional on passing a competition.
Up to several hundred thousand euros per project
Research projects, particularly in the fields of machine learning, semantic web and database management, can be funded over a period of one year, from 50,000 to 100,000 euros, through the “Research Awards”.
With the “Focused Research Awards”, the follow-up can be spread over several years, with envelopes of several hundred thousand euros. Financial support for individuals – doctoral students in this case – will be available through “PhD Fellowships”.
The approach is similar to sponsorship, with researchers retaining intellectual property rights to their discoveries, which are systematically published for the community.
Six projects from Inria have already received awards since 2009 and the creation of the “Google Research Awards”. “And the pace has accelerated in recent months,” say the two parties.
An initiative praised by all
Geneviève Fioraso, the French Minister of Higher Education and Research, described the agreement as “a promising one for the development of high-level teams in France that are skilled in the technologies essential to the digital economy.
She is seconded by Fleur Pellerin. The French Minister for Innovation and the Digital Economy sees this alliance as “proof of France’s attractiveness in digital technology, a field of excellence for our researchers in companies and laboratories.
And Vinton Cerf, vice-president of Google, concluded: “Inria’s history is rich with discoveries in the field of fundamental research. This institution carries with it values […] of creativity that correspond to Google’s DNA: innovation.”
Because we spend too much time every day on our mobile phone, Google has just launched three new apps: Envelope, Activity Bubbles, and Screen Stopwatch. The goal: to better manage the time dedicated to new technologies and regain control over them. These applications have been created as part of Google’s experiments for the digital well-being of its users.
The Screen Stopwatch application may be the most effective way to get you off your smartphone. A (very large) stopwatch is displayed in real-time on your device’s screen. The purpose seems obvious: it should show you how much time you spend using it each day.
The stopwatch starts as soon as you unlock your device. A constant ticking sound from your home screen even encourages you to stop touching it so that you can concentrate on other, healthier activities.
Activity Bubbles help make you aware of how you use your phone over the day. The principle is simple: each time you unlock your smartphone, a bubble will be created on your wallpaper. The bubble gets bigger, the more time you spend on your device. The more you use your phone, the more bubbles you’ll have on your screen at the end of the day.
This application is part of the Digital Wellbeing Experiments program launched by Google to share ideas and tools, which aims to find a better balance with the use of new technologies. Activity Bubbles is available on the Play Store.
Envelope is undoubtedly the most original, if not the most confusing. Google describes it as “an experimental application that temporarily transforms your phone into a simpler, quieter device to help you take a break from your digital world.”
To use it, Google asks you to print a single PDF provided by the application. Fold this sheet of paper so that it takes the shape of an envelope. Slip your phone inside the envelope.
Using your smartphone for your usual activities (chatting with your friends, checking your social networks) won’t be possible for you. Only the functions of a traditional phone will be accessible: making or receiving calls, using the keyboard buttons printed on the envelope, or the camera. You will then have to unseal the envelope to see the photo or video you have taken.
Google has thought of everything: the application is optimized for OLED screens, so it won’t drain your battery if you want to spend a whole day on your digital detox. At the end of its use, you’ll see how much time you spent without using your smartphone.
The Envelope application is available for Pixel 3a owners.
The semantic cocoon sets up a hierarchical architecture of pages linked together by contextualized links and a natural semantic universe. It reminds me of the SEO silo. The two concepts are similar and concern the organization of pages within a website to give juice to a target page using contextualized links from the lower level pages.
The semantic cocoon as developed by French SEO Laurent Bourrelly is an optimization of the internal linking, a thorough knowledge of the specific internal topic-sensitive PageRank formula but also a different starting point. While siloing consists in organizing the pages of a site around pages gathered by theme, a semantic cocoon will be set up to meet the expectations of the Internet user.
Silo SEO and semantic cocoon: what are the differences?
Let’s take an example with an e-commerce site that offers shoes for men. Siloing is an organization around a primary keyword (and often around products or services for sale), here “men’s shoes.”
If the subgroups are “sports shoes,” “boots and boots” and “street shoes,” it will be difficult to catch the Internet user who is looking for “comfortable shoes.”
Proximity between the two notions? Not really
Indeed, these two concepts have common points, and many SEO professionals reciprocally use both terms and thus maintain a certain confusion. The silo can try to insert a notion of semantics into its deployment, but a semantic cocoon is part of a real editorial strategy. The starting point of the cocoon is the definition of a persona.
The thematic silo
It is what we most often find on the web. Sites built in thematic silos are the majority. Sub categories organize the product sheets, each subcategory classified in its parent category. Everything is in its place. Nevertheless, as an e-merchant you should ask yourself the following questions:
- Does the search engine find what it needs to understand and, above all, classify the pages?
- Does the Internet user also have the same logic as yours for the classification of products?
- Does the Internet user find the answers to the questions he or she is asking?
E-commerce sites that use this semantic silo system have taken a further step. The notion of semantic writing is part of the project. We will try to please search engines and try to make them understand without ambiguity what the page’s purpose. Some will even try to insert a notion of semantic shift between the sub-category pages and the parent category page. In the case of the semantic silo, you try to answer the first question (I try to make myself understood from the search engine). But you left out the Internet user without solving the other two questions.
The definition of a semantic cocoon
The semantic cocoon is a system for organizing textual content intended to answer Internet users’ questions on a given theme and linked together by skilfully placed hypertext links.
The semantic cocoon places the Internet user AND his or her concerns at the center of the process. This sentence is essential… meditate on it! The keyword search will come in a second step. We will not only aim at positioning on a specific request, but we will cover the entire theme.
Your product is no longer the starting point for your actions but becomes THE answer to the Internet user’s question. It will allow you to make you understand engines; your site will become the most relevant, the most remarkable on the subject. Your visitors, prospects, customers must become your primary concern. The semantic cocoon will only be used to answer their questions.
This question can be approached from 2 different angles, the evolution of web technology on one hand (web 3.0, semantic web, 3D web…) and the evolution of web usage on the other hand.
The point on which all the actors of the web agree is a simple observation: The internet has already undergone several changes since its creation and others are yet to come.
What is web 1.0, web 2.0?
Since 1995, Web 1.0 has been built in a pyramidal way. Webmasters write and layout information, Internet users are only receivers without any power and any real possibility of response except for forums and emails. In the era of Web 1.0, the Internet user is passive. The production and hosting of content is mainly carried out by companies and web agencies, the pages are static, and the updates of information are very random. Web 1.0 is, therefore, the era of the static web. At that time, we had no hébergeur wordpress and the market for CMS was not really competitive!
We then talk about Web 2.0 from 2003, gradually Internet users become active players, in the meantime, the number of individuals having access to the web is multiplied by 5 (from 500 Million in 2003 to more than 2.2 Billion in 2013).
As they navigate, Internet users add content through hypertext links and other tags, annotations or comments. Internet users create content through the emergence of blogs, wikis (Wikipedia is the largest wiki on the Web) and citizen newspapers such as Agoravox.
Web 3.0, semantics, 3d, yes, but still…
Some studies and sources allow us to date the periods of the different versions of the Web (web 1.0, 1.5, 2.0, 2.5, 2.B …, web 3.0), they sometimes appear contradictory. It is indeed more accurate to talk about the Web era (without obscuring the Marketing aspect) by considering periods as spaces of time until historians look at the subject.
What more does Web 3.0 has in store for us?
Web 3.0 is, therefore, the next significant evolution of the internet, significant trends are already making it possible to define its main outlines, others think we are already there!
The production of web 3.0 will be perfectly compatible with all devices (mobile friendly). Regarding technology, it will solve interoperability problems between online services, isolated user communities, etc. All software applications will be accessible online (Cloud Computing) and will adapt to the terminals used, which means merging the three existing Internet worlds: 3D Internet (fusion of the traditional Internet with mobile Internet and the Internet of Things: with RFID chips, QRcode, television, refrigerators, clock radio, etc.).
The 3D web, the one that consists in displaying content in 3 dimensions, already exists. We call it “interactive 3D” content, this display technology will initially become widespread for virtual tours (the Louvre), games, panoramas… before being distributed more widely.
With the Semantic Web (Data Web or Linked Data: Tim Berners-Lee from W3C) all sites will be linked in one way or another. Thus we will be “on file,” in particular through our navigation, our different profiles, our relationships and our comments on social networks; the era of king marketing in short…
The sites are invaded by contextual advertisements related to the documents consulted and our consumption habits. Search engines will become more “intelligent” and the results more targeted.
Beyond these “material and technological” aspects, our Internet environment is gradually transforming into a real information ecosystem in which we will be completely immersed.
The Internet will always be with us and why not in us? We will be constantly “geolocated,” and our consumption patterns scrutinized and even shared automatically. We will be informed on an ongoing basis according to our interests and the opportunities to be seized during all our travels.
In contrast to web 1.0, which was primarily a consultative web, a spectator web, the current world wide web is very collaborative, social. It is logically called web 2.0.
Its inventor, Tim Berners-Lee, predicted a few years ago that we were entering the 3rd phase of the web. It is called the semantic web. To sum up, people can nowadays collaborate, but machines still do not have the standards to do so. Web 3.0 allows, thanks to rules currently being finalized, communication between databases and their intelligent processing. The network will be semantic because the Internet offers a particularly powerful playing field for standards that have existed for a long time. Today, these systems are becoming more powerful thanks to the mass of data stored on the web.
Technically, how does it work?
The basic notion of semantic web is an ontology, a representation of the properties of what exists in the real world in a formalism that allows automatic processing. There are ontologies in all fields. If we take cinema as an example, we will integrate into the system that the director of the film “For a handful of dollars” is “Sergio Leone” and that Clint Eastwood is the leading actor. If we extrapolate this example to the web, which is made up of millions of data, it can give deep connections.
How to make your site more semantic?
The semantic web will be useful for a large number of applications:
- Make search engines more intelligent,
- Describe and process multimedia documents,
- Building multilingual and multicultural solutions
- Enable the fusion of very diverse information
In general, the semantic web is still in its infancy. It is always complicated to develop your site with this type of functionality. Nevertheless, it is necessary to get into the habit of thinking “semantically” by, for example, installing a system of tag clouds on your site or by structuring your data as much as possible.
There is, therefore, the data web, the “Giant Global Graph,” the “Linked Open Data,” the web 3.0, etc. To understand them well independently of each other, it is necessary to start from the internet of data. The web is characterized by pages linked to each other; we remain in the documentary field. With web data, on the contrary, works directly with databases. The data are also connected via links. We are therefore no longer working only on documents but raw data. This vision gives birth to Giant Global Graph when millions of users will be able to link and exchange data with each other. Linked Open Data is a set of data that can be put online and linked. This includes government data, academic data, etc.
Finally, web semantics consists of giving meaning to data by explaining their schema. For example, when an Internet user searches for a report, it will be possible to link the story to a document, which will allow him/her to be presented with not only reports but also documents. These will be classified into subtypes. So we create data classes.
As you will have understood, the semantic web is a model that allows data to be shared and reused between several applications. The objective is to enable users to find, share and combine information more simply without intermediaries.
The World Wide Web, the invention of Tim Berners-Lee in 1989, has been a phenomenal success.In just under 30 years, more than 3.81 billion people worldwide have used it, and the Web has grown more prominent over the years with a vast amount of information.
Fortunately, solutions exist to find relevant information in all this content.
Today, search engines, thanks to their crawlers, can recursively browse through the links of billions of web pages and index their content in massive databases. Thus a user performing a search will obtain a list of results classified in order of relevance corresponding to criteria specific to the search engine such as the frequency of keywords, density index, etc.
The solution: the Semantic Web!
The Semantic Web is a concept designed to enable machines to understand the meaning of information on the Web.
The aim is thus to set up, in addition to the network of hyperlinks between traditional web pages, a network of links between structured data. Tim Berners-Lee, director of the W3C, coined the term. He oversees the development of Semantic Web standards proposals.
Resource Description Framework (RDF)
Created in 1999, RDF is a data exchange format on the Web and is the primary language of the Semantic Web. RDF adopts a graph model whose objective is to describe resources on the Internet (Companies, Books, Articles, etc….).
Three characteristics define an RDF data:
- its subject: the address of the targeted resource
- its predicate: the property assigned to the targeted resource
- the object: the value related to the property of the targeted resource
In computer science, an ontology represents a structured set of terms and concepts representing the meaning of an information field. The purpose of ontologies is to express the world around us in such a way that it is understandable by a machine and then to be able to make deductions from it.
There are particular languages to create these ontologies. Among them, we have for example OWL (Web Ontology Language) which is a knowledge representation language built on RDF.
FOAF (Friend Of A Friend) is a project whose aim is to create a network of web documents that can be understood by machines describing individuals and the relationships between them. Without the need for a centralized directory, FOAF allows people to be linked to each other as if everything was described in a single database.
Thanks to these technologies, the machines will be able to understand questions like the one asked earlier.
Various Semantic Web applications:
Different application areas use the Semantic Web technologies.
In social networks where the Semantic Web makes it possible to increase search possibilities and connect members. For bibliographic/documentary classification, the semantic web is also present in companies to collect and analyze large volumes of data.
Even in the E-commerce industry, to describe in a structure the products, prices, and information related to the company, it allows search engines to exploit this essential data better to restore them in their search context.
To say the internet is in a state of flux is an understatement. The internet is always changing, evolving and adjusting. And one of the changes that we could start to see in the next few years is the development of the semantic web.
But what is the semantic web? Why is it useful? And what purpose does it serve?
Understanding the Semantic Web
Before we go too deep into the semantic web, let us break down what it means. The word semantic implies that it has something to do with language. After all, semantics is the concept of properly arranging variables such as letters, numbers, symbols and spaces so words and phrases can be understood.
The same concept is true of the semantic web. It is about arranging information that is located online so that it can be easily understood. But the key aspect to the semantic web is that information should be organized so that it is better understood by machines!
People are already able to understand information online. Whether you are searching for a recipe, the price of a book or the latest television show episode, information is laid out in a way that you would understand. It is laid out so that you can understand and interpret that information to your liking.
But the problem is that our way of arranging this information online, which is done through HTML and other computer languages, is not applicable to machines. The machines are not able to understand or interpret enough of this information accurately enough or quickly enough.
The semantic web is the idea that machines should be able to do what we are doing today. That machines should be able to seek out and understand the information that is listed online.
The idea of a machine being able to “go online” and seek out information, understand it and then interpret it is scary. But the truth is that everyone would benefit. Not only would machines have an easier time understanding and interpreting the information, but they would also be able to determine if it is accurate. They would be able to distinguish between random information and details that come from a proper source. And that would lead to a lot less misleading and downright false information that we find online today.
Major Organizations Benefit Too
There is so much information on the web. Being able to quickly search through that information for specific details is a hard job. While there are search engines and other tools that can serve this purpose, they are still not perfect.
Having the semantic web in place would mean that sifting through data would be even faster and more accurate than it is right now. Such a concept would be useful for big companies, educational institutes, medical facilities, law offices and more!
Adding Meaning to Data
The problem is that right now machines are able to sift through data based on parameters we set. However, machines are not sure what the data means. There is a huge difference between a machine looking up a phrase and regurgitating the first result, compared to the machine understanding the query and the resulting information that it is receiving.
And with online assistants, AI and other tech on the rise, it will be interesting to see how the semantic web plays into everything. Even future tech such as driverless cars, which may become mainstream in a decade, will be linked to the semantic web. It is all about leveraging the power of machines and the vast information that is available online, so that machines are able to interpret that information in a more accurate and productive way.
There is a lot of buzz around online assistants in the past couple years. If you had asked most people five or six years ago about having a personal online assistant in their home, they would find it absurd. But now we have devices from Apple, Amazon, Google and other companies in many first world homes.
People have embraced technology such as the Echo from Amazon or Google Home. Why? Because these devices are designed to make our life easier. Want to know the weather? Ask Alexa. Want to set an alarm or set aside time for a meeting next week? Tell the online assistant and it will do the relevant bookings for you.
But what if online assistants could do so much more? And not just for people, but for companies and educational institutes too.
Most people have heard about the semantic web in passing, but not in any great deal. The concept of the semantic web is to create a web where information is easily accessible and understandable by machines.
Why is the semantic web an important concept? Because machines have a hard time understanding and interpreting information when it is written out for humans.
For instance, many of the sites that we use to gather information have words formatted around our way of understanding details. Amazon will have the title of a book and its price listed in a way that you can understand. But it does not necessarily mean that a machine could understand that information. Many times, machines cannot understand that information.
It is why online assistants are so limited in what they can do. Setting meetings and finding basic details are easy, repeatable tasks that online assistants are programmed to do. But it is still very hard for machines to gather complex information, determine its accuracy and interpret that information.
That is why so many experts believe the semantic web is a crucial concept.
Future of AI
In many ways, the future of AI will be determined by the success of establishing the semantic web. If there is a version of the web where machines are able to easily read, understand and interpret information, it can only benefit people, organizations and businesses.
It is complicated work to develop languages and concepts so that information we consume is processable by machines. But it is vital work that is going to shape the coming decade of innovation.