What does semantic annotation mean
What is Semantic Annotation? Semantic annotation is the process of labeling documents with related concepts. These documents are enriched with metadata: references linking content to concepts, described in a knowledge graph. This makes unstructured content easier to find, interpret and reuse.
Semantic annotation or tagging is the process of attaching metadata to text documents or other unstructured content, concepts related to it (for example, people, places, organizations, products, or topics). Unlike classic text annotations for reader reference, semantic annotations can also be consumed by machines. Semantically tagged documents are easier to find, interpret, compose, and reuse.
How semantic annotation works
Semantic annotation enriches content with machine-processable information by associating background information with extracted concepts. These concepts, found in a document or another piece of content, are clearly defined and interrelated both inside and outside the content. It turns content into a better manageable data source.
A typical semantic enrichment process includes five steps: text recognition, text analysis, concept extraction, relation extraction, indexing and storage in the semantic graph database;
1. Text recognition
Step 1: We remove boilerplate from unstructured text content.
Text is extracted from non-text sources such as PDF files, videos, documents, audio recordings, etc.
2. Text Analysis
Step 2: We perform a standard set of natural language processing operations on the content – such as sentence splitting, part-of-speech tagging, and named entity recognition.
Algorithms break down sentences and identify concepts such as people, things, places, events, numbers, and more.
3. Concept extraction
Step 3: We classify and disambiguate the identified entities.
All identified concepts are categorized, meaning they are defined as people, organizations, numbers, etc. Next, they are disambiguated, that is, they are unambiguously identified against the domain-specific knowledge base. For example, Rome is classified as a city and further disambiguates that Rome, Italy, is not Rome, Iowa.
This is the most important stage of semantic annotation. It recognizes chunks of text and transforms them into machine-processable and understandable chunks of data by linking them to the wider context of existing data.
4. Relationship extraction
Step 4: We also determine the relationship between known entities and newly recognized entities.
The relations between the extracted concepts are identified and further correlated with relevant external or internal domain knowledge.
5. Index and store in semantic graph database
Step 5: Finally, the extracted knowledge (represented as a graph) is stored in our semantic database
All mentions of people, things, etc., as well as their relationships, are identified and enriched with machine-readable data, then indexed and stored in a semantic graph database for further reference and use.
Semantic Annotation Process
The result of the semantic annotation process is metadata that describes a document by referring to concepts and entities mentioned in or related to the text. These references link content to formal descriptions of these concepts in the Knowledge Graph. Typically, such metadata is represented as a set of tags or annotations that enrich a document or a specific fragment thereof with conceptual identifiers.
Semantic metadata can be stored in knowledge graphs instead of being embedded in documents. A modeling approach that supports extensive analysis is to store annotations as individual objects that refer to documents, which are also nodes in the graph. In this way, documents and annotations become first-class citizens of the knowledge graph, which can be indexed and queried alongside other types of data: ontologies, schemas, references, and master data.
Create intelligent content with a machine-processable edge
Think of semantic annotations as a highly structured digital edge (annotations made in the margins of a book or other document), usually invisible in the human-readable portion of the content. These notes are written in a machine-interpretable data-form language and serve computers to perform operations such as classification, linking, reasoning, searching, and filtering.
Examples of semantic annotation
For example, semantically labeling selected concepts in the sentence “Aristotle, the author of politics, founded the Academy” means identifying Aristotle as a person and politics as Written works of political philosophy and further index, classify and link identified concepts in the semantic graph database, also known as triples. In this case, Aristotle can be linked to his date of birth, his teachers, his writings, etc. Politics can be linked to its subject matter, date of creation, etc. Given semantic metadata about the above sentence and its links to other (external or internal) forms of knowledge, the algorithm will be able to automatically:
Find out who taught Alexander the Great;
Answer which of Plato’s students established the Academy;
Retrieve a list of political thinkers who lived between 380 BC and 310 BC;
Presents a list of Greek philosophers, including Aristotle.
Semantic annotation application field
What semantic annotation brings is intelligent data fragments that contain highly structured and informative annotations for machine reference. Solutions that include semantic annotation are widely used in risk analysis, content recommendation, content discovery, detection of compliance, and more.
Semantically annotated content opens up cost-effective opportunities to:
Search for content other than keywords;
Content aggregation beyond manual filtering;
Relational discovery beyond human research.
Semantic annotations make it easy to:
Find relevant information in piles of documents with the help of an errand machine;
extract knowledge from different sources;
Provide personalized content based on machine understandable context;
Automatically interconnect content.
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