Traditional Keyword-based Search Engines Research Articles

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research.

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-based search engines often fall short in assisting users who may not be familiar with specific terminologies. To address this, we present a knowledge graph based paper search engine for biomedical research to enhance the user experience in discovering relevant queries and articles. The system, dubbed DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS) tagging to extract terminologies and relationships from article abstracts to create a KG. To reduce information overload, DiscoverPath presents users with a focused subgraph containing the queried entity and its neighboring nodes and incorporates a query recommendation system enabling users to iteratively refine their queries. The system is equipped with an accessible Graphical User Interface that provides an intuitive visualization of the KG, query recommendations, and detailed article information, enabling efficient article retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath is open-sourced at https://github.com/ynchuang/DiscoverPath with a demo video at Youtube.

A Systematic and Comparative Analysis of Semantic Search Algorithms

Users often struggle to discover the information they need online because of the massive volume of data that is readily available as well as being generated every day in the today’s digital age. Traditional keyword-based search engines may not be able to handle complex queries, which could result in irrelevant or insufficient search results. This issue can be solved by semantic search, which utilises machine learning and natural language processing to interpret the meaning and context of a user's query. In this paper we focus on analyzing the BM-25 algorithm, Mean of Word Vectors approach, Universal Sentence Encoder model, and Sentence-BERT model on the CISI Dataset for Semantic Search Task. The results indicate that, the Finetuned SBERT model performs the best.

Semantic Search Engine

Background/Objectives: Traditional keyword-based search engines retrieve web pages by matching the exact tokens or words in the user query with the tokens or words in the web documents. This approach has many drawbacks. Synonyms or terms similar to tokens or words in the user query are not taken into consideration to search web pages. The keyword based search engine gives equal importance to all keywords whereas when user will enter query, he may have different levels of importance for different keywords in his opinion. To get the correct relevant result, users may need to enter several synonyms on his own to get the desired information which may otherwise result into the omission of many valuable web pages. Another problem is of information overloading. The traditional keyword based search engines make it very tedious for end user to locate the really useful information from a huge list of search results. Existing web is dominated by keyword based Search Engines which does not provide an appropriate mechanism to classify and locate the relevant search results. This leads to wastage of precious time of end user if he does not know the key terms which are utilized to index preferred correct pages. To resolve the above mentioned issues that the users face, in this paper we have proposed search techniques to develop Ontology based semantic search engine. Methods/Statistical Analysis: Ontology based Semantic Search Engine for the tourism domain is developed that understands the meaning of the user query and relatively provides the direct, precise and relevant result. Not only the user entered keyword based pages would be returned but also the pages that are appropriate enough with the meaning of the user entered keyword were also be returned by using the Ontological synonym dataset developed by using WordNet. Findings: Firstly, the Ontology Synonym set is constructed using WordNet and then the ontology synonym set parser is used to map the user defined query with the query prototype. By comparing the Query Similarity for every prototype, the service/sub domain with maximum query similarity is identified and the respective service is invoked. Also if the similarity is 100% the extra keywords are also considered to provide the relevant and precise results to the end user. Meta-processor will provide meta information about the URL. Application/ Improvements: The proposed Ontology based semantic search engine in tourism domain is an enhanced model. This model can be used by search engines, tourism industry professional and customers in getting better results of the searches undertaken. After testing in real world, the improvements can be worked out.

Fuzzy Semantic Search Engine

Almost all the search engines that exist retrieve web pages by finding the exact keywords. The traditional keyword-based search engines suffer many problems, like synonyms and terms similar to keywords are not taken into account to search web pages, they treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. Synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to think of and input several similar keywords individually to complete a search. The restriction of exact keywords makes it inconvenient for users to search web pages. Many valuable web pages would be omitted if users did not search for several similar keywords individually. While users input several keywords to search web pages, different keywords may have different degrees of importance in their opinions. Traditional search engines treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. The problem of information overload makes it difficult for users to find really useful information from a large amount of search results. Traditional search engines lack an applicable classification mechanism to reduce the search space and improve the search results. To alleviate the mentioned problems that the users face, in this paper we have proposed and applied the fuzzy logic theory and the semantic search techniques to develop a fuzzy semantic search engine.

Finding story chains in newswire articles using random walks

Massive amounts of information about news events are published on the Internet every day in online newspapers, blogs, and social network messages. While search engines like Google help retrieve information using keywords, the large volumes of unstructured search results returned by search engines make it hard to track the evolution of an event. A story chain is composed of a set of news articles that reveal hidden relationships among different events. Traditional keyword-based search engines provide limited support for finding story chains. In this paper, we propose a random walk based algorithm to find story chains. When breaking news happens, many media outlets report the same event. We have two pruning mechanisms in the algorithm to automatically exclude redundant articles from the story chain and to ensure efficiency of the algorithm. We further explore how named entities and word relevance can help find relevant news articles and improve algorithm efficiency by creating a co-clustering based correlation graph. Experimental results show that our proposed algorithm can generate coherent story chains without redundancy. The efficiency of the algorithm is significantly improved on the correlation graph.

Study of E-learning Information Retrieval Model based on Ontology

Research work in the field of E-learning is represented by a broad area of applications ranged from web learning to virtual courses. Web-based courses offer obvious advantages for learners by making access to educational resource very fast and relevance, at any time or place. The objective of this paper is to create an ontology based domain specific representation for e-learning domain. This paper analyzes the drawbacks of traditional keyword based search engines and proposes the need for semantic based intelligent information retrieval systems. This paper presents ontology based information retrieval (IR) for e-learning domain which is developed using Protege tool. This discusses about the Elearning information retrieval model which consists Resource collection module, Semantic procession module, Resource collection module, resource integration system. General Terms Computer Science, Information Retrieval.

Ontology based Information Retrieval: A Case Study for Sports and Eminent Personalities Domain

The objective of this paper is to develop software framework to improve information creation, maintenance and retrieval by introducing semantic technologies. This paper analyzes the drawbacks of traditional keyword based search engines and proposes the need for semantic based intelligent information retrieval systems. It also analyzes technologies specified by W3C, procedure for the creation of ontology from scratch, the evolution of ontology. This paper presents Ontology Based Information Retrieval (IR) for Sports and Eminent Personalities Domain which is developed using Protege tool.