Semantik Arama İçin Bilgi Çerçevesi Tasarlanması

Year 2021, Issue: 32, 682 - 689, 31.12.2021

İsmail Burak Parlak Alper Mıtıncık

https://doi.org/10.31590/ejosat.1043441

Abstract

Yeni nesil bilgi arama prosedürleri, arama motorlarının tasarımını yeniden şekillendirmede karmaşık araçlar sağlamaktadır. Anlam tabanlı analiz profesyonel uygulamalarda kademeli olarak benimsense dahi, bilginin arkasındaki karmaşık birikimin davranışı, kademeli veri öğrenme modellerini gerektirmektedir. Metin modelleri sözlük tabanlı özelliklere dayalı olarak kullanılmaktadır. Sözlüksel yöntemlere sahip arama motorları, bağlamsal ve anlamsal bilgilerden yoksundur. Bu engel derin öğrenme yöntemlerinin geliştirilmesiyle aşılmaktadır. Metin, resim, video gibi farklı içerik türlerinin bağlamsal bilgileri sinir ağı modelleriyle elde edilerek daha doğru sonuçlara ulaşılabilir. Bu çalışmada, sözlüksel ve anlamsal özellikler üzerinden arama motorlarına geniş bir perspektiften bakılmıştır. Anlamsal arama yöntemleri denenmiş ve bilimsel dokümanlardan oluşan veri setlerinde sözlüksel yöntemlerle karşılaştırılmıştır. Bilimsel belgeler nispeten iyi biçimlendirilmiş veri kümeleri olduğundan bağlam dışı veriler ve anlamsal çatışmalarla uğraşmadan, çalışma boyunca anlamsal arama yöntemlerini ve sinir modellerini karşılaştırmaya odaklanıldı. Böylelikle, anlamsal aramanın sözcüksel aramadan daha iyi performans gösterdiği gözlenmektedir. Mevcut bilgi arama-bulma görevlerinin, çok modlu veri kümelerinin derin öğrenme stratejileriyle işlendiği anlambilimde yeni bakış açıları gerektirdiği sonucuna varılmıştır.

Keywords

Bilgi çıkarımı, Semantik arama, Derin öğrenme, Tekrar sıralama, Yoğun çıkarım

Designing An Information Framework For Semantic Search

Abstract

New generation information retrieval procedures provide complex tools to remodel the design of search engines. Even though semantic analysis is gradually adopted by corporations, complex behavior of knowledge behind the information entails subsequent data learning models. Text models are currently in use through lexical features. Search engines with lexical methods lack contextual and semantic information. This barrier has been overcome with the development of deep learning methods. More accurate results can be retrieved by obtaining contextual information of different types of content such as text, image, video with neural models. In this study, a broad perspective of search engines was considered through lexical and semantic features. Semantic search methods were experimented then compared with lexical methods in data sets consisting of scientific documents. Since scientific documents are relatively well-formatted datasets and do not contain irrelevant content, the focus was on comparing semantic search methods and neural models throughout the study, without dealing with out-of-context data and semantic conflicts. As a result, semantic search methods performed better than lexical search. We conclude that current search-retrieval tasks require new perspectives in semantics where multimodal information is handled with deep learning strategies.

Keywords

Information retrieval, Semantic search, Deep learning, Re-ranking, Dense retrieval

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