Semantic Representation of a Geo-Social User Profile for a Personalised Information Retrieval

2021 ◽  
pp. 2150044
Author(s):  
Tahar Rafa ◽  
Samir Kechid
Keyword(s):  
Information Retrieval ◽  
Information Search ◽  
Semantic Representation ◽  
User Profile ◽  
Search Process ◽  
Search System ◽  
User Interactions ◽  
User Interests ◽  
Situational Contexts ◽  
User Query

The user-centred information retrieval needs to introduce semantics into the user modelling for a meaningful representation of user interests. The semantic representation of the user interests helps to improve the identification of the user’s future cognitive needs. In this paper, we present a semantic-based approach for a personalised information retrieval. This approach is based on the design and the exploitation of a user profile to represent the user and his interests. In this user profile, we combine an ontological semantics issued from WordNet ontology, and a personal semantics issued from the different user interactions with the search system and with his social and situational contexts of his previous searches. The personal semantics considers the co-occurrence relations between relevant components of the user profile as semantic links. The user profile is used to improve two important phases of the information search process: (i) expansion of the initial user query and (ii) adaptation of the search results to the user interests.

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.

Personalization Information Retrieval Based on Topic Directory

2013 ◽  
Vol 712-715 ◽  
pp. 2659-2663
Author(s):  
Yang Xin Yu ◽  
Yi Zhou Zhang
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Retrieval System ◽  
User Profile ◽  
Information Retrieval System ◽  
Tree Structure ◽  
Experimental Results ◽  
User Profiles ◽  
User Interests ◽  
Computing Process

Personalization information retrieval is very useful in information retrieval system, the user profile can be used to represent the favorites or interests of user. This paper introduces how to automatically learn user interests, build user profiles and re-rank search results.A topic directory method is proposed to calculate the semantic similarity, which takes multi-inheritance into consideration, and then optimize the computing process based on the tree structure of inheritance relationship. Experiments are conducted to compare our method with the popular directory based search methods (e.g., Google Directory Search). Experimental results show that the proposed method in this paper can effectively capture personalization and improve the accuracy of personalized search over existing approaches.

scholarly journals Models of Digital Educational Resources Indexing and Dynamic User Profile Evolution

2016 ◽  
Vol 11 (01) ◽  
pp. 26 ◽  
Author(s):  
Hamid Slimani ◽  
N. El Faddouli ◽  
S. Bennani ◽  
N. Amrous
Keyword(s):  
Bayesian Network ◽  
Information Search ◽  
User Profile ◽  
Educational Resources ◽  
Search Process ◽  
Search Activity ◽  
Personalized Search ◽  
Digital Resources ◽  
Dynamic Development

The modelling of the user profile and its integration into the search process is an effective way in personalized information search within a repository of educational digital resources. Therefore, it raises gradually the issue concerning the dynamic development of this profile so as the information requester sets up queries. In our approach presented in this paper, we propose two models for personalized search on digital educational resources. The first is to establish an index of repository resources while the second is to build the user profile and boost its evolution after each query submitted by the user based on a classical Bayesian network representing a search activity.

scholarly journals Integration of User Profile in Search Process according to the Bayesian Approach

2018 ◽  
Vol 6 (4) ◽  
pp. 32
Author(s):  
Farida Achemoukh ◽  
Rachid Ahmed-Ouamer
Keyword(s):  
Information Retrieval ◽  
Information Needs ◽  
Ad Hoc ◽  
Retrieval System ◽  
User Profile ◽  
Search Process ◽  
Specific Information ◽  
Information Process ◽  
Search Approach ◽  
Search Information

Most information retrieval system (IRS) rely on the so called system-centred approach,<br /> behaves as a black box, which produces the same answer to the same query, independently on the<br /> user’s specific information needs. Without considering the user, it is hard to know which sense<br /> refers to in a query. To satisfy user needs, personalization is an appropriate solution to improve the<br /> IRS usability. Modeling the user profile can be the first step towards personalization of information<br /> search. The user profile refers to his/her interests built across his/her interactions with the retrieval<br /> system. In this paper, we present a personalized information retrieval approach for building and<br /> exploiting the user profile in search process, based on Bayesian network. The theoretical<br /> framework provided by these networks allows better capturing the relationships between different<br /> information. Experiments carried out on TREC-1 ad hoc and TREC 2011 Track collections show<br /> that our approach achieves significant improvements over a personalized search approach described<br /> in the state of the art and also to a baseline search information process that do not consider the user<br /> profile

scholarly journals Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
S. Sadesh ◽  
R. C. Suganthe
Keyword(s):  
Data Mining ◽  
Information Search ◽  
Regime Switching ◽  
Web Mining ◽  
User Behavior ◽  
Research Work ◽  
User Profile ◽  
Query Result ◽  
User Query ◽  
Filtering Efficiency

Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP) framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches) regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio.

Designing a Chat-Bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

2020 ◽  
Vol 01 ◽  
Author(s):  
Radha Guha
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Text Summarization ◽  
The Internet ◽  
Specific Domain ◽  
User Query ◽  
College Information ◽  
Chat Bot

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

Information Search Process in Science Education

1994 ◽  
Vol 20 (44) ◽  
pp. 53-60 ◽  
Author(s):  
Mary Jane McNally ◽  
Carol C. Kuhlthau
Keyword(s):  
Science Education ◽  
Information Search ◽  
Search Process
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