SemanticTaggingAndSearching

What is Semantic Web ?

Humans are capable of finding an information on the web. We can easily understand the context of a web page. From a perspective of a machine, it is not always possible to interpret these huge amounts of data so easily. In order for machines to interpret this data, machines should have some contextual information about that data.

The Semantic Web is a collaborative movement led by international standards body the World Wide Web Consortium (W3C). It aims to let machines understand about a page’s (or some data) context and link its information with other related data by inserting machine readable metadata about pages. This will let users to share and combine information more organized. It will also let users to access to a specific information more easily.

Difference between Semantic Tagging and Traditional Subject Indexing

As defined above, semantic tagging is an agreed concept for machines to answer queries on web or about contexts. The term is also used as “semantic indexing”. For humans to get an idea over a context, indexing provides a faster way. However semantic tagging or indexing is different from normal indexing done in text level. Human indexers can discern concepts of a text and get the idea however for machines there is a need for a standard and when this is done, it allows to search documents not as a whole but also part by part.

One difference semantic tagging has rather than traditional indexing is therefore that it searches contexts not the documents as a whole. When a human is searching about a topic, normal indexing used in any text for human readers can generally provide the documents which are about the topic. However, via semantic tagging, a search about a specific topic can be done and the result can be found in a document which is not mainly related to that topic, the document just has some parts mentioning about the topic.

The other difference is about the way semantic tagging is done. Because via semantic tagging, we also aim to find topics in smaller parts of documents, semantic tagging should be done in atomic level. In other words, when there is a change in the topic in a document or an article, semantic tagging must be done. However; in normal indexing, just the page or the whole context is indexed.

The third difference can be explained in this way, semantic tagging do not rely on linguistics or processing the natural languages rather it does establish links between tags. The links can be done by adding meta-data for tags related to each other. Therefore, machines can easily find meaningful results just by analyzing these hard links.