scholarly journals A semantic search approach for hyper relational knowledge graphs

2021 ◽  
Author(s):  
Veronica dos Santos ◽  
Sérgio Lifschitz
Keyword(s):  
Information Retrieval ◽  
Semantic Search ◽  
Similarity Function ◽  
Search Results ◽  
Relational Knowledge ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems ◽  
Knowledge Graphs ◽  
Search Approach

Information Retrieval Systems usually employ syntactic search techniques to match a set of keywords with the indexed content to retrieve results. But pure keyword-based matching lacks on capturing user's search intention and context and suffers of natural language ambiguity and vocabulary mismatch. Considering this scenario, the hypothesis raised is that the use of embeddings in a semantic search approach will make search results more meaningfully. Embeddings allow to minimize problems arising from terminology and context mismatch. This work proposes a semantic similarity function to support semantic search based on hyper relational knowledge graphs. This function uses embeddings in order to find the most similar nodes that satisfy a user query.

scholarly journals An Art of Review on Conceptual based Information Retrieval

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 21-31
Author(s):  
P. Mahalakshmi ◽  
N. Sabiyath Fathima
Keyword(s):  
Information Retrieval ◽  
Keyword Search ◽  
Semantic Representation ◽  
Research Work ◽  
Semantic Search ◽  
Future Research ◽  
Conceptual Information ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems

Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed.

Experiments with Language-based Aids in Information Retrieval Systems

1988 ◽  
Vol 11 (1-2) ◽  
pp. 33-46 ◽  
Author(s):  
Tove Fjeldvig ◽  
Anne Golden
Keyword(s):  
Information Retrieval ◽  
Text Retrieval ◽  
Considerable Improvement ◽  
Controlled Experiments ◽  
Compound Words ◽  
Search Results ◽  
Retrieval Systems ◽  
Complete Search ◽  
Information Retrieval Systems ◽  
Search Quality

The fact that a lexeme can appear in various forms causes problems in information retrieval. As a solution to this problem, we have developed methods for automatic root lemmatization, automatic truncation and automatic splitting of compound words. All the methods have as their basis a set of rules which contain information regarding inflected and derived forms of words – and not a dictionary. The methods have been tested on several collections of texts, and have produced very good results. By controlled experiments in text retrieval, we have studied the effects on search results. These results show that both the method of automatic root lemmatization and the method of automatic truncation make a considerable improvement on search quality. The experiments with splitting of compound words did not give quite the same improvement, however, but all the same this experiment showed that such a method could contribute to a richer and more complete search request.

Spatio-Temporal Based Personalization for Mobile Search

2012 ◽  
pp. 386-409 ◽  
Author(s):  
Ourdia Bouidghaghen ◽  
Lynda Tamine
Keyword(s):  
Information Retrieval ◽  
Contextual Information ◽  
The Internet ◽  
Search Results ◽  
Search Result ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Spatio Temporal ◽  
Mobile Context

The explosion of the information available on the Internet has made traditional information retrieval systems, characterized by one size fits all approaches, less effective. Indeed, users are overwhelmed by the information delivered by such systems in response to their queries, particularly when the latter are ambiguous. In order to tackle this problem, the state-of-the-art reveals that there is a growing interest towards contextual information retrieval (CIR) which relies on various sources of evidence issued from the user’s search background and environment, in order to improve the retrieval accuracy. This chapter focuses on mobile context, highlights challenges they present for IR, and gives an overview of CIR approaches applied in this environment. Then, the authors present an approach to personalize search results for mobile users by exploiting both cognitive and spatio-temporal contexts. The experimental evaluation undertaken in front of Yahoo search shows that the approach improves the quality of top search result lists and enhances search result precision.

User Models for Adaptive Information Retrieval on the Web

2012 ◽  
Vol 3 (3) ◽  
pp. 1-19
Author(s):  
Max Chevalier ◽  
Christine Julien ◽  
Chantal Soulé-Dupuy
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
User Profile ◽  
User Model ◽  
User Models ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Adaptive Information Retrieval ◽  
The Web

Searching information can be realized thanks to specific tools called Information Retrieval Systems IRS (also called “search engines”). To provide more accurate results to users, most of such systems offer personalization features. To do this, each system models a user in order to adapt search results that will be displayed. In a multi-application context (e.g., when using several search engines for a unique query), personalization techniques can be considered as limited because the user model (also called profile) is incomplete since it does not exploit actions/queries coming from other search engines. So, sharing user models between several search engines is a challenge in order to provide more efficient personalization techniques. A semantic architecture for user profile interoperability is proposed to reach this goal. This architecture is also important because it can be used in many other contexts to share various resources models, for instance a document model, between applications. It is also ensuring the possibility for every system to keep its own representation of each resource while providing a solution to easily share it.

scholarly journals Large-scale image search with text for information retrieval

2021 ◽  
Vol 4 (1) ◽  
pp. 87-89
Author(s):  
Janardan Bhatta
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Image Feature ◽  
Image Search ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Text Features ◽  
Text Query

Searching images in a large database is a major requirement in Information Retrieval Systems. Expecting image search results based on a text query is a challenging task. In this paper, we leverage the power of Computer Vision and Natural Language Processing in Distributed Machines to lower the latency of search results. Image pixel features are computed based on contrastive loss function for image search. Text features are computed based on the Attention Mechanism for text search. These features are aligned together preserving the information in each text and image feature. Previously, the approach was tested only in multilingual models. However, we have tested it in image-text dataset and it enabled us to search in any form of text or images with high accuracy.

scholarly journals Onto Semantic Tourism Information Retrieval

2018 ◽  
Vol 7 (4.7) ◽  
pp. 148 ◽  
Author(s):  
Shilpa S. Laddha ◽  
Dr. Pradip M. Jawandhiya
Keyword(s):  
Information Retrieval ◽  
Significant Role ◽  
Semantic Search ◽  
Efficient Search ◽  
Domain Specific ◽  
Search Results ◽  
User Query ◽  
Tourism Information ◽  
Relationship Of ◽  
The Relationship

Semantic Search is an area of research which focuses on meaning of terms used in user query. Ontology plays significant role to define the concept and the relationship of terms in domain. Since the understanding of concepts is domain specific, Ontology creation is also domain specific. According to this argument, query interpreted in Tourism domain can have different meaning in some other domain. This paper presents a prototype of information retrieval interface using ontology which can save users time by rendering relevant, precise and efficient search results as compared to traditional search interfaces.  

Web Resources on Medical Tourism

2019 ◽  
pp. 174-195
Author(s):  
S. Naseehath
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Medical Tourism ◽  
Engine Performance ◽  
Link Analysis ◽  
Search Results ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
General Search

Webometric research has fallen into two main categories, namely link analysis and search engine evaluation. Search engines are also used to collect data for link analysis. A set of measurements is proposed for evaluating web search engine performance. Some measurements are adapted from the concepts of recall and precision, which are commonly used in evaluating traditional information retrieval systems. Others are newly developed to evaluate search engine stability, which is unique to web information retrieval systems. Overlapping of search results, annual growth of search results on each search engines, variation of results on search using synonyms are also used to evaluate the relative efficiency of search engines. In this study, the investigator attempts to conduct a webometric study on the topic medical tourism in Kerala using six search engines; these include three general search engines, namely Bing, Google, and Lycos, and three metasearch engines, namely Dogpile, ixquick, and WebCrawler.

On The Reuse of Past Searches in Information Retrieval

2015 ◽  
Vol 6 (2) ◽  
pp. 72-92 ◽  
Author(s):  
Claudio Gutiérrez-Soto ◽  
Gilles Hubert
Keyword(s):  
Monte Carlo ◽  
Information Retrieval ◽  
Search Results ◽  
Monte Carlo Algorithms ◽  
Log Files ◽  
Retrieval Systems ◽  
Cosine Measure ◽  
Information Retrieval Systems ◽  
Single User ◽  
Baseline Approach

When using information retrieval systems, information related to searches is typically stored in files, which are well known as log files. By contrast, past search results of previously submitted queries are ignored most of the time. Nevertheless, past search results can be profitable for new searches. Some approaches in Information Retrieval exploit the previous searches in a customizable way for a single user. On the contrary, approaches that deal with past searches collectively are less common. This paper deals with such an approach, by using past results of similar past queries submitted by other users, to build the answers for new submitted queries. It proposes two Monte Carlo algorithms to build the result for a new query by selecting relevant documents associated to the most similar past query. Experiments were carried out to evaluate the effectiveness of the proposed algorithms using several dataset variants. These algorithms were also compared with the baseline approach based on the cosine measure, from which they reuse past results. Simulated datasets were designed for the experiments, following the Cranfield paradigm, well established in the Information Retrieval domain. The empirical results show the interest of our approach.

On The Reuse of Past Searches in Information Retrieval

2016 ◽  
pp. 1117-1137 ◽  
Author(s):  
Claudio Gutiérrez-Soto ◽  
Gilles Hubert
Keyword(s):  
Monte Carlo ◽  
Information Retrieval ◽  
Search Results ◽  
Monte Carlo Algorithms ◽  
Log Files ◽  
Retrieval Systems ◽  
Cosine Measure ◽  
Information Retrieval Systems ◽  
Single User ◽  
Baseline Approach

When using information retrieval systems, information related to searches is typically stored in files, which are well known as log files. By contrast, past search results of previously submitted queries are ignored most of the time. Nevertheless, past search results can be profitable for new searches. Some approaches in Information Retrieval exploit the previous searches in a customizable way for a single user. On the contrary, approaches that deal with past searches collectively are less common. This paper deals with such an approach, by using past results of similar past queries submitted by other users, to build the answers for new submitted queries. It proposes two Monte Carlo algorithms to build the result for a new query by selecting relevant documents associated to the most similar past query. Experiments were carried out to evaluate the effectiveness of the proposed algorithms using several dataset variants. These algorithms were also compared with the baseline approach based on the cosine measure, from which they reuse past results. Simulated datasets were designed for the experiments, following the Cranfield paradigm, well established in the Information Retrieval domain. The empirical results show the interest of our approach.

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