A. Why are inverted files the most typical structure used for search systems? How does n-grams modify the inversion list approach?
B. Discuss the similarities and differences between N-gram and PATRICIA TREE approach to indexing.
A. Inverted files are the most typical structure used for search systems because they allow for efficient retrieval of documents based on their content. In an inverted file structure, each term in a document is associated with a list of document identifiers (or positions) where that term appears. This allows for quick lookup of documents containing a specific term during search operations.
N-grams modify the inversion list approach by considering sequences of characters rather than individual terms. An n-gram is a contiguous sequence of n characters from a given text. By indexing n-grams instead of individual terms, we can capture more nuanced relationships between words and increase the search system's recall. For example, if we have the word "cat" and we use trigrams (3-grams), we would index "cat" as "<ca", "cat", "at>", capturing the context around the word.
B. Both N-gram indexing and the PATRICIA TREE approach are commonly used for indexing in search systems, but they have different characteristics.
Similarities:
1. Both approaches can handle large datasets efficiently.
2. Both approaches can support quick retrieval of documents based on their content.
3. Both approaches involve the creation of an index data structure to store the indexed information.
Differences:
1. N-gram indexing focuses on capturing relationships between characters or terms in the indexed document, using n-grams as the unit of indexing. PATRICIA TREE indexing, on the other hand, is a trie-based indexing method that organizes data based on shared prefixes of keys.
2. N-gram indexing is better suited for capturing context and relationships between words, while PATRICIA TREE indexing is useful for optimizing storage and search operations by compressing and compacting the index structure.
3. N-gram indexing can require more storage space compared to PATRICIA TREE indexing, particularly when capturing higher-order relationships.
4. The efficiency and performance of both approaches can vary depending on the specific implementation and the nature of the dataset being indexed.