How does semantic technology work?�

To know how semantic technology works, the first thing you should know is that it is responsible for helping artificial intelligence systems to understand language and process information as humans do, being able to manage, store and retrieve information based on meaning and logical relationships.

Semantic technology processes the logical structure of sentences to identify the most relevant elements of the text and to understand the subject matter (the story). Understanding the exact meaning of words is essential to understand the content and perform proper text mining. 

In this way, the real meaning of the text (the story) is captured. For example, you understand that the main theme of a text is "sport" and "health", although these words do not appear in the text, but related concepts such as "wellness", "stretching", "yoga", "endorphin" do appear.

Although we must keep in mind that not all data tells stories. Big Data provides companies with information about the volume of data collected or the types of sources used, but this data needs to be interpreted. The interpretation of the data is known as Smart Data. And the goal is clear: to elaborate the story from the filtered useful data and relate them semantically so that they can be interpreted by the machine. Obviously, as the amount of digital information increases and, above all, unstructured information, it becomes much more complicated to filter the data effectively.

Advantages of semantic technology on the web

  • Allows for better organization of information on the network by incorporating semantic content into pages that are uploaded to the Internet. Therefore, it makes it easier to obtain knowledge and decreases search time.
  • Allows machines access to knowledge managementThe system uses an artificial intelligence that processes natural language to provide a coherent and satisfactory response.

Disadvantages of semantic technology on the Web

  • Its implementation is a slow and complicated process to restructure all the information available on the network. and adapt it to the format proposed by the semantic web. It is necessary to unify semantic standards and provide equivalence relations between concepts. Its implementation can take years, apart from the high economic investment in infrastructure and specialized personnel (programmers and experts in digital semantics).
  • As each language has different semantics, semantic search engines must be implemented for each language.

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Technologies to develop the semantic web

The technologies used for the development of the semantic web are mainly based on four languages that facilitate the interoperability of information. Below, we will define their main characteristics:

  1. XML (Extensive Markup Language). It is a W3C specification as a general purpose markup language. It comes from the SGML language and allows correct and precise encodings for the distribution of complex and similar documents over the Internet. It is a fairly simple technology that is compatible with other similar technologies for standardizing and sharing information. 
  2. RDF (Resource Description Framework). Language recommended by the W3C that describes information resources so that they can be processed by machines. It is in charge of establishing relationships between data, while the PICS (Platform for the Internet Content Selection) determine what type of content should be displayed based on what is requested in the search. Both have been geared more towards the creation of internet search filters and privacy protection.
  3. OWL (Web Ontology Language). A tool built on RDF and coded in XML that allows the development of specific vocabularies that are able to relate to other resources. It also allows sharing data using ontologies in the semantic web, i.e., establishing relationships between concepts and the logical rules that are necessary to understand them.
  4. SPARQL (Protocol and RDF Query Language). It is a standardized language for querying RDF graphs. It uses different data sources and allows searches on semantic web resources. 

More and more companies are aware of the growing importance of their presence and prestige on the Internet. There is no doubt that for many companies the web has become a fundamental ally in expanding communication and sales channels. To do this, companies need to be able to analyze the meaning of the huge amount of unstructured data they receive. Semantic technology can be the solution, because there are huge amounts of disorganized, duplicated or outdated resources to find the right information. Therefore, they need digital tools to manage information more efficiently.


Knowledge Graphs and Artificial Intelligence

Knowledge graphs supported by artificial intelligence allow storing data and providing them with structure and context, so that the machine can learn and "reason" by exploiting the data and their interrelationships. Optimization of knowledge graphs is what will enable the development of conversational artificial intelligence products. that "mimic" the way human beings think and relate to information and knowledge.

But, what is a knowledge network and what does it consist of?

A "knowledge network"refers to the storage of knowledge in a graph structure, which is a structure composed of a set of nodes and edges.

  • The nodes correspond to the entities.
  • The edges correspond to the relationships between the different entities.

Information can be stored in both nodes and relations. In both cases, the stored knowledge is accompanied by "semantic information", that is, the meaning of the entities as words coming from a language.

This information includes both semantic information of the entity itself (e.g., the entity 'person' has the attribute 'date of birth'), and semantic information related to semantic relationships between two entities (e.g., the entity 'dog' has a direct relationship with the entity 'animal').

Establishing a graphic simile, the structure of graphs is similar to the one our brain uses to structure knowledge.

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