Abstract--This paper details the development of a novel semantic search algorithm utilizing Word2Vec and Annoy Index to efficiently process and retrieve information from large datasets. Addressing traditional search algorithms' limitations, our proposed method demonstrates significant improvements in speed, accuracy, and scalability, validated by rigorous testing on datasets up to 100GB. In the era of big data, efficiently retrieving relevant information from vast, unstructured datasets is crucial across numerous domains such as e-commerce, healthcare, research, and public administration. Traditional search engines, which rely primarily on keyword matching, often struggle with the inherent complexity and ambiguity of natural language. These systems lack the ability to understand the semantic meaning and context of queries, leading to inaccurate results and suboptimal user experiences. The evolution of semantic search technologies aims to address these limitations by focusing on understanding the in high-dimensional space.

The interactive theorem prover, Lean, enables the verification of formal mathematical proofs and is backed by an expanding community. Central to this ecosystem is its mathematical library, mathlib4, which lays the groundwork for the formalization of an expanding range of mathematical theories. However, searching for theorems in mathlib4 can be challenging. To successfully search in mathlib4, users often need to be familiar with its naming conventions or documentation strings. Therefore, creating a semantic search engine that can be used easily by individuals with varying familiarity with mathlib4 is very important. In this paper, we present a semantic search engine for mathlib4 that accepts informal queries and finds the relevant theorems. We also establish a benchmark for assessing the performance of various search engines for mathlib4.

Health misinformation on search engines is a significant problem that could negatively affect individuals or public health. To mitigate the problem, TREC organizes a health misinformation track. This paper presents our submissions to this track. We use a BM25 and a domain-specific semantic search engine for retrieving initial documents. Later, we examine a health news schema for quality assessment and apply it to re-rank documents. We merge the scores from the different components by using reciprocal rank fusion. Finally, we discuss the results and conclude with future works.

Enterprise knowledge is a key asset in the competing and fast-changing corporate landscape. The ability to learn, store and distribute implicit and explicit knowledge can be the difference between success and failure. While enterprise knowledge management is a well-defined research domain, current implementations lack orientation towards small and medium enterprise. We propose a semantic search engine for relevant documents in an enterprise, based on automatic generated domain ontologies. In this paper we focus on the component for ontology learning and population.

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The word semantic is a Linguistic term. It means something related to meaning in a language or logic. In a natural language, semantic analysis is relating the structures and occurrences of the words, phrases, clauses, paragraphs etc and understanding the idea of what's written in particular text. Does the formation of the sentences, occurrencSemantic Analysis, Semantic Search,Domain Ontology, Natural Language Processinges of the words make any sense? The challenge we face in the technologically advanced world is to make the computer understand the language or logic as much as the human does.

I recently had the opportunity to learn more about Inbenta, a provider of Natural Language Search technology for intelligent assistant and web self-service technologies. I spoke with global marketing director Julie Casson and Kelly Foster, linguist, to gain insight into a company I didn't know much about. Inbenta originated in Barcelona, and now has offices in the United States, France, Singapore, Brazil and the Netherlands. Casson and Foster are located at the office in Sunnyvale, California. Prior to our conversation, I knew that Inbenta offers intelligent assistant technology and an extremely innovative 3D avatar, called Victoria.

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role.

In this paper we describe the production and consumption of linked data in the scenario of the Italian news agency ANSA portal. The goal of the use-case is to provide viewers of a news item with background information and links to related news articles contained on the portal. This information enrichment process is entity-based: ANSA news archive is analyzed using Name Entity Recognition, and each detected entity is annotated with a unique identifier. These identifiers are obtained using the Entity Name Server developed within the scope of the OKKAM European project. Subsequently the news are published on the portal using RDFa and linked to a semantic search engine that provides background information harvested from sources such as DBpedia and links to additional news sources. The presented project has the potential to contribute to Linked Data by creating and publishing a large quantity of entities and assertions about them coming from the ANSA news archive.