What are some of the challenges and solutions for building and updating positional index?

1 Inverted index vs positional index

An inverted index is a mapping from terms to documents that contain them, usually with some additional information such as term frequency or document frequency. An inverted index can answer queries that ask for documents that contain a set of terms, regardless of their order or proximity. However, an inverted index cannot answer queries that ask for documents that contain a specific phrase or a near match, such as "information retrieval" or "positional index challenges". For that, we need a positional index.

2 Positional index structure

A positional index is an extension of an inverted index that also stores the positions of each term in each document, usually as a list of integers. For example, the term "information" might have the following posting list: [doc1: 2, 5, 9; doc2: 1, 4, 7; doc3: 3, 6, 10]. This means that the term "information" appears in doc1 at positions 2, 5, and 9, in doc2 at positions 1, 4, and 7, and in doc3 at positions 3, 6, and 10. A positional index can answer phrase queries by intersecting the posting lists of the terms in the phrase and checking if their positions are consecutive or within a certain distance.

3 Positional index size

One of the main challenges of building and updating positional index is the size. Storing the positions of every term in every document can result in a very large index that consumes a lot of disk space and memory. For example, if we have a collection of 1 million documents, each with an average of 1000 terms, and each term has an average of 10 occurrences per document, then the positional index would have 10 billion entries. One possible solution to reduce the size of the positional index is to use compression techniques, such as variable-length encoding, delta encoding, or bit vectors, to store the positions more compactly.

4 Positional index update

Another challenge of building and updating positional index is the update. When new documents are added to the collection, or existing documents are modified or deleted, the positional index needs to be updated accordingly. This can be a time-consuming and costly process, especially if the index is stored on disk and needs to be accessed frequently. One possible solution to speed up the update process is to use incremental indexing, which involves maintaining a small in-memory index for the recent changes and merging it with the main disk-based index periodically. Another possible solution is to use distributed indexing, which involves partitioning the collection and the index into multiple segments and assigning them to different servers or nodes that can handle the updates in parallel.

5 Positional index query

A final challenge of building and updating positional index is the query. Phrase queries can be more complex and diverse than simple term queries, and require more sophisticated algorithms and data structures to process them efficiently and effectively. For example, phrase queries can have different types of operators, such as exact match, proximity match, ordered match, or unordered match, and can have different scopes, such as document level, sentence level, or paragraph level. One possible solution to handle different types of phrase queries is to use generalized inverted index, which allows storing arbitrary attributes and relations of terms, such as position, order, distance, or dependency. Another possible solution is to use auxiliary structures, such as suffix arrays or permuterm index, which can facilitate finding common substrings or prefixes of terms.

6 Positional index benefits

Despite the challenges and solutions for building and updating positional index, it is a very useful and powerful tool for information retrieval. Positional index can improve the precision and recall of phrase queries, which are often more relevant and specific than term queries. Positional index can also enable more advanced and expressive query languages, such as natural language queries, regular expressions, or wildcards, which can enhance the user experience and satisfaction. Positional index can also support other applications and tasks, such as text summarization, plagiarism detection, or sentiment analysis, which rely on identifying and analyzing phrases and their contexts.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?