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Machine learning based query term weight and synonym analyzer for query expansion. LucQE - open-source, Java. Provides a framework along with several implementations that allow to perform query expansion with the use of Apache Lucene. Xapian is an open-source search library which includes support for query expansion; ReQue open-source, Python ...
An open-source, math-aware, question answering system called MathQA, based on Ask Platypus and Wikidata, was published in 2018. [15] MathQA takes an English or Hindi natural language question as input and returns a mathematical formula retrieved from Wikidata as a succinct answer, translated into a computable form that allows the user to insert ...
The Knowledge Query and Manipulation Language, or KQML, is a language and protocol for communication among software agents and knowledge-based systems. [1] It was developed in the early 1990s as part of the DARPA knowledge Sharing Effort, which was aimed at developing techniques for building large-scale knowledge bases which are share-able and re-usable.
Do query expansion, add these terms to query, and then match the returned documents for this query and finally return the most relevant documents. Some experiments such as results from the Cornell SMART system published in (Buckley et al.1995), show improvement of retrieval systems performances using pseudo-relevance feedback in the context of ...
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
Item A is-an-instance-of Category C; Item A has-this-relation-to Item B; Examples: Niger is-a country. Chad is-a country; Niger is-next-to Chad. Agadez is-a city. Agadez is-located-in Niger. TBox statements typically (or definitions of domain categories and implied relations) such as: An entity X can be a country or a city
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
Knowledge extraction is the creation of knowledge from structured (relational databases, XML) and unstructured (text, documents, images) sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing.