2. Features of IR Systems
3. Scope of IR System
- Unstructured Information: These informations either does not have a pre-defined data model or is not organized in a pre-defined order. Unstructured information is typically text-heavy, but may contain datasets such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional computer programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents (Wikipedia). Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files, and unstructured text such as the body of an e-mail message, Web page, or word-processor document. While the primary content being conveyed does not possess a defined structure, it generally comes packaged in objects (e.g. in files or folders or documents, ...) that themselves have some metadata and are thus a combination of structured and unstructured data, but normally it is referred to as "unstructured data". For example, if we consider an HTML web page it is tagged, but HTML mark-up typically serves the purpose of presentation. It is not being able to capture the significance or function of tagged elements in order to assist automated processing of the information content of the page. XHTML tagging does allow machine processing of elements, although it typically does not capture or convey the semantic meaning of tagged terms. There are several techniques such as data mining and text analytics and noisy-text analytics, information visualization which give different methods to search for patterns in, or otherwise interpret from the available unstructured information. The most popular technique for providing structure to several unstructured resources usually involve manual tagging with metadata or part-of-speech tagging for further text mining-based structuring. Unstructured Information Management Architecture (UIMA) provides a common model for processing this information to extract meaning and create structured data about the information .
- Structured Information: It is information that is already structured in fields, such as “name”, “age”, “gender”, “hobby”, “address”, “profession”, “salary”. This is the typical example of what we find in a record of a relational database table. When information is organized in a structured form, it is usually relatively easy to search it, since one can directly query the database : give me the list of names whose profession is student in the table PERSON, where age is greater than 25 and name starts with the letter B. Structured data first depends on creating a data model – a model of the types of business data that will be recorded and how they will be stored, processed and accessed. This includes defining what fields of data will be stored and how that data will be stored: data type (string, integer, etc) and any restrictions on the data input (number of characters; restricted to certain terms such as Mr., Ms. or Dr.; M or F).
4. Types of IR System
5. Functioning of IR System
6. Basic components involved in IR process
7. Purpose and Function of IR System
- available contents are to be analyzed in the information sources as well as the users’ queries, and then
- then the user queries are matched with the available document in-order to retrieve the relevant resources.
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