Evaluation metrics for Information Retrieval (IR) are quantitative measures used to assess how effectively a search or retrieval system ranks documents in response to a query. The most common metrics include: Precision – proportion of retrieved documents that are relevant. ...

Language models (LMs) like GPT, LLaMA, and PaLM are only as powerful as the data that shapes them. Among the most important training resources are Wikipedia and Wikidata. Wikipedia provides rich, multilingual, and well-structured text with hyperlinks that act as ...

Entity disambiguation forms the backbone of knowledge graphs and semantic search. While traditional Named Entity Recognition (NER) and Named Entity Linking (NEL) detect mentions and assign them to knowledge bases, modern search engines require more advanced strategies. This evolution reflects ...

As the web expands into a web of entities and knowledge graphs, one of the biggest challenges is semantic interoperability. Organizations, domains, and industries all model their data differently—using diverse vocabularies, schemas, and ontologies. The solution is ontology alignment and ...

As the web expands into a web of entities and knowledge graphs, one of the biggest challenges is semantic interoperability. Organizations, domains, and industries all model their data differently—using diverse vocabularies, schemas, and ontologies. The solution is ontology alignment and ...

Google no longer measures quality by keywords alone. Instead, it uses E-E-A-T — Experience, Expertise, Authoritativeness, and Trust — as the interpretive lens for determining reliable, people-first content. While E-E-A-T itself is not an algorithm, its principles are embedded in ...

Tokenization is the process of splitting raw text into smaller units called tokens, which can be words, subwords, or characters. It is the first step in NLP preprocessing and directly impacts how models interpret meaning. Word tokenization: splits text by ...

When machines process language, they must normalize words to a standard form for consistency. A single concept often appears in multiple inflected forms—running, ran, runs—but semantically, they all point to the base concept run. Lemmatization solves this by reducing words ...

Stopwords are high-frequency words in a language that contribute syntactic structure but limited semantic value on their own. Common examples include: English: the, is, at, for, of, and Urdu: کیا, ہے, سے Traditionally, stopwords were identified via: Predefined lists: e.g., ...

One-Hot Encoding is a technique that converts categorical data into a binary vector representation. Each unique category or token is assigned an index, and instances of that category are represented as vectors with a single “hot” (1) at the assigned ...