Sparque Desk Documentation

Getting Started
Information that will help you to get started with SPARQUE.
Datasets
Information on creating knowledge graphs from data sources, defining mappings with SPARQUE Language, and loading triples into Sparque's graph database.
Strategies
Information on managing strategies using the Strategy Editor, connecting and configuring building blocks, setting API outputs, adding parameters, and creating stacked strategies for complex search tasks.
- Text Transformations for Search Term Matching
- Building Blocks
APIs
Information on managing APIs in SPARQUE Desk to connect strategies to your application.
Evaluation
Information on assessing strategy performance in SPARQUE Desk using testsets and evaluations.
Users
Information on managing user permissions and roles.
Settings
Information on managing your account settings.
Query API
Information on using the Query API. Learn about endpoints, faceted search, response types, and request options.
Miscellaneous
Information on Tuple-notation, encoding, error messages and short-hand notation.
Shopware Extension
The SPARQUE Shopware extension allows you to seamlessly integrate our powerful tools into your Shopware store, providing advanced data analytics and insights.
SPARQUE API Wrapper
Information on the SPARQUE API Wrapper, which streamlines interactions between clients, internal users, and SPARQUE Desk.
SPARQUE Hub
Information on SPARQUE Hub, the front-end component designed to simplify the interactions of clients and internal users with SPARQUE Desk.
Project Tasks
Information that will help you perform SPARQUE project tasks.
Glossary
Discover important terms and concepts used throughout SPARQUE Desk.

Home
Strategies
Information on managing strategies using the Strategy Editor, connecting and configuring building blocks, setting API outputs, adding parameters, and creating stacked strategies for complex search tasks.
Building Blocks
Match
Match by BM25

Getting Started
Information that will help you to get started with SPARQUE.
Datasets
Information on creating knowledge graphs from data sources, defining mappings with SPARQUE Language, and loading triples into Sparque's graph database.
Strategies
Information on managing strategies using the Strategy Editor, connecting and configuring building blocks, setting API outputs, adding parameters, and creating stacked strategies for complex search tasks.
- Text Transformations for Search Term Matching
- Building Blocks
APIs
Information on managing APIs in SPARQUE Desk to connect strategies to your application.
Evaluation
Information on assessing strategy performance in SPARQUE Desk using testsets and evaluations.
Users
Information on managing user permissions and roles.
Settings
Information on managing your account settings.
Query API
Information on using the Query API. Learn about endpoints, faceted search, response types, and request options.
Miscellaneous
Information on Tuple-notation, encoding, error messages and short-hand notation.
Shopware Extension
The SPARQUE Shopware extension allows you to seamlessly integrate our powerful tools into your Shopware store, providing advanced data analytics and insights.
SPARQUE API Wrapper
Information on the SPARQUE API Wrapper, which streamlines interactions between clients, internal users, and SPARQUE Desk.
SPARQUE Hub
Information on SPARQUE Hub, the front-end component designed to simplify the interactions of clients and internal users with SPARQUE Desk.
Project Tasks
Information that will help you perform SPARQUE project tasks.
Glossary
Discover important terms and concepts used throughout SPARQUE Desk.

Match by BM25

Description

This is a multi-query BM25 block, multiple lists of query keywords instead of a single one. It is in fact equivalent to a matching operation. It finds matches between the STRING-columns in the inputs by calculating the BM25 relevance score.

Input

Because this is originally a retrieval block, the notation SOURCE / QTERMS will be used, instead of A / B as in other matching blocks.

SOURCE [OBJ,STRING]: a list of candidates, in which the STRING-column will be used for comparison and the OBJ-column will be the result
QTERMS [OBJ,STRING]: a list of candidates, in which the STRING-column will be used for comparison and the OBJ-column will be the result

Output

RESULT [OBJ,OBJ]: the matched objects from SOURCE and QTERMS

Parameters

Stemming: tokens can be stemmed for a specific language or left as they are
Case-sensitive: if set to false, upper/lower case is ignored
Normalize diacritics: transliterates non-ASCII characters into their closest ASCII form
Tokenization: the method to tokenize the input strings.
- None: perform no tokenization
- Spaces: all valid Unicode space characters
- Spaces/Punctuation: Spaces + all valid Unicode punctuation characters
- Spaces/Punctuation/Digits: Spaces/Punctuation + all valid Unicode digit characters
- Spaces/Punctuation/Digits/Symbols: Spaces/Punctuation/Digits + all valid Unicode symbol characters
- Custom Regular Expression: any regular expression
Min token length: tokens whose character length is shorter than this value are discarded
Gram type:
- Word (default): each token is composed by UTF-8 word n-grams
- Character: each token is composed by UTF-8 character n-grams
Grams: allows to extract n-gram tokens (default is 1)
All query terms must match: if set to true, only candidates where all tokens in QTERMS match a string in SOURCE are considered a match
k1: controls non-linear term frequency normalization (saturation). Lower value = quicker saturation (term frequency is more quickly less important)
b: degree of document-length normalization applied. 0=no normalization, 1=full normalization