scholarly journals Personalised Information Recommender Using Framework for Ontology Alignment Among Digital Libraries

2016 ◽  
Vol 36 (4) ◽  
Author(s):  
Anirban Chakrabarty ◽  
Sudipta Roy
Keyword(s):  
Digital Libraries ◽  
User Profile ◽  
Context Aware ◽  
Human Intervention ◽  
Ontology Alignment ◽  
User Interest ◽  
Search Results ◽  
Efficient Information ◽  
Alignment Model ◽  
Multiple Ontologies

In the digital erantology is considered as one of the powerful tools for knowledge representation and efficient information retrieval. Ontology alignment is a process that discovers mapping between source and target ontologies, where each mapping is a relationship based on some similarity measure. This paper, has presented a new context aware alignment approach that needs little human intervention and it can map multiple ontologies to generate user interest dynamically. The objective is to design and develop an ontology alignment model that provides more benefits to its stakeholders in sharing resources and searching across digital libraries based on priorities of users. The experimental results evidently indicate significant improvement in search results when user profile and navigational pattern ontologies are aligned with digital library ontology.

A personalized search using a semantic distance measure in a graph-based ranking model

2011 ◽  
Vol 37 (6) ◽  
pp. 614-636 ◽  
Author(s):  
Mariam Daoud ◽  
Lynda Tamine ◽  
Mohand Boughanem
Keyword(s):  
Distance Measure ◽  
Similarity Measures ◽  
User Profile ◽  
Semantic Distance ◽  
User Interest ◽  
Document Ranking ◽  
Search Results ◽  
Ranking Models ◽  
Ranking Model ◽  
Extended Graph

The goal of search personalization is to tailor search results to individual users by taking into account their profiles, which include their particular interests and preferences. As these latter are multiple and change over time, personalization becomes effective when the search process takes into account the current user interest. This article presents a search personalization approach that models a semantic user profile and focuses on a personalized document ranking model based on an extended graph-based distance measure. Documents and user profiles are both represented by graphs of concepts issued from predefined web ontology, namely, the Open Directory Project directory (ODP). Personalization is then based on reordering the search results of related queries according to a graph-based document ranking model. This former is based on using a graph-based distance measure combining the minimum common supergraph and the maximum common subgraph between the document and the user profile graphs. We extend this measure in order to take into account a semantic recovery at exact and approximate concept-level matching. Experimental results show the effectiveness of our personalized graph-based ranking model compared with Yahoo and different personalized ranking models performed using classical graph-based measures or vector-space similarity measures.

scholarly journals A Survey on Blockchain-Based Search Engines

2021 ◽  
Vol 11 (15) ◽  
pp. 7063
Author(s):  
Esmaeel Rezaee ◽  
Ali Mohammad Saghiri ◽  
Agostino Forestiero
Keyword(s):  
Search Engines ◽  
Research Field ◽  
The Internet ◽  
Context Aware ◽  
Search System ◽  
Search Results ◽  
Related Information ◽  
Research Article ◽  
Different Types ◽  
Search History

With the increasing growth of different types of data, search engines have become an essential tool on the Internet. Every day, billions of queries are run through few search engines with several privacy violations and monopoly problems. The blockchain, as a trending technology applied in various fields, including banking, IoT, education, etc., can be a beneficial alternative. Blockchain-based search engines, unlike monopolistic ones, do not have centralized controls. With a blockchain-based search system, no company can lay claims to user’s data or access search history and other related information. All these data will be encrypted and stored on a blockchain. Valuing users’ searches and paying them in return is another advantage of a blockchain-based search engine. Additionally, in smart environments, as a trending research field, blockchain-based search engines can provide context-aware and privacy-preserved search results. According to our research, few efforts have been made to develop blockchain use, which include studies generally in the early stages and few white papers. To the best of our knowledge, no research article has been published in this regard thus far. In this paper, a survey on blockchain-based search engines is provided. Additionally, we state that the blockchain is an essential paradigm for the search ecosystem by describing the advantages.

A faster and less aggressive algorithm for correcting conservativity violations in ontology alignments

2021 ◽  
pp. 1-20
Author(s):  
Cauã Roca Antunes ◽  
Alexandre Rademaker ◽  
Mara Abel
Keyword(s):  
State Of The Art ◽  
Time Algorithm ◽  
The State ◽  
Ontology Alignment ◽  
Ontology Alignment Evaluation Initiative ◽  
Multiple Ontologies

Ontologies are computational artifacts that model consensual aspects of reality. In distributed contexts, applications often need to utilize information from several distinct ontologies. In order to integrate multiple ontologies, entities modeled in each ontology must be matched through an ontology alignment. However, imperfect alignments may introduce inconsistencies. One kind of inconsistency, which is often introduced, is the violation of the conservativity principle, that states that the alignment should not introduce new subsumption relations between entities from the same source ontology. We propose a two-step quadratic-time algorithm for automatically correcting such violations, and evaluate it against datasets from the Ontology Alignment Evaluation Initiative 2019, comparing the results to a state-of-the-art approach. The proposed algorithm was significantly faster and less aggressive; that is, it performed fewer modifications over the original alignment when compared to the state-of-the-art algorithm.

Explainable Word-Embeddings for Medical Digital Libraries - A Context-Aware Approach

2020 ◽  
Author(s):  
Janus Wawrzinek ◽  
Said Ahmad Ratib Hussaini ◽  
Oliver Wiehr ◽  
José María González Pinto ◽  
Wolf-Tilo Balke
Keyword(s):  
Digital Libraries ◽  
Context Aware ◽  
Word Embeddings

A Webpage Ranking Algorithm Based on Collaborative Recommendation

2013 ◽  
Vol 765-767 ◽  
pp. 998-1002
Author(s):  
Shao Xuan Zhang ◽  
Tian Liu
Keyword(s):  
Sorting Algorithm ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Search Results ◽  
Collaborative Recommendation ◽  
Interest Model ◽  
Sorting Efficiency ◽  
User Interest Model ◽  
Relevant Calculation ◽  
Personalized Ranking

In view of the present personalized ranking of search results user interest model construction difficult, relevant calculation imprecise problems, proposes a combination of user interest model and collaborative recommendation algorithm for personalized ranking method. The method from the user search history, including the submit query, click the relevant webpage information to train users interest model, then using collaborative recommendation algorithm to obtain with common interests and neighbor users, on the basis of these neighbors on the webpage and webpage recommendation level associated with the users to sort the search results. Experimental results show that: the algorithm the average minimum precision than general sorting algorithm was increased by about 0.1, with an increase in the number of neighbors of the user, minimum accuracy increased. Compared with other ranking algorithms, using collaborative recommendation algorithm is helpful for improving webpage with the user interest relevance precision, thereby improving the sorting efficiency, help to improve the search experience of the user.

A study on next‐generation digital library using context‐awareness technology

2013 ◽  
Vol 31 (2) ◽  
pp. 236-253 ◽  
Author(s):  
Younghee Noh
Keyword(s):  
Digital Library ◽  
Digital Libraries ◽  
Context Awareness ◽  
Theoretical Research ◽  
Context Aware ◽  
Semantic Retrieval ◽  
Next Generation ◽  
Climate Control ◽  
Content Type ◽  
Reference Services

PurposeThis study seeks to examine the concepts of context, context‐awareness, and context‐awareness technology needed for applying context‐awareness technology to the next‐generation of digital libraries, and proposed context‐aware services that can be applied to any situation by illustrating some library contexts.Design/methodology/approachThe paper investigated both theoretical research and case analysis studies before suggesting a service model for context‐awareness‐based libraries by examining the context, context‐awareness, and context‐awareness technology in depth.FindingsThis paper derived possible library services which could be provided if context‐awareness services are implemented by examining and analyzing case studies and systems constructed in other fields. A library‐applied context‐aware system could recognize users entering the library and provide optimal services tailored to each situation for both new and existing users. In addition, the context‐awareness‐based library could provide context‐awareness‐based reference services, context‐awareness‐based loan services, and cater to other user needs in the stacks, research space, and a variety of other information spaces. The context‐awareness‐based library could also recognize users in need of emergency assistance by detecting the user's behavior, movement path, and temperature, etc. Comfort or climate‐control services could provide the user with control of the temperature, humidity, illumination and other environmental elements to fit the circumstances of users, books, and instruments through context‐aware technology.Practical implicationsNext‐generation digital libraries apply new concepts such as semantic retrieval, real‐time web, cloud computing, mobile web, linked data, and context‐awareness. Context‐awareness‐based libraries can provide applied context‐awareness access service, reactive space according to the user's access, applied context‐awareness lobbies, applied context‐awareness reference services, and applied context‐awareness safety services, context‐awareness‐based comfort services and so on.Originality/valueReal instances of libraries applying context‐aware technology are few, according to the investigative results of this study. The study finds that the next‐generation digital library using context‐awareness technology can provide the best possible service for the convenience of its users.

User-Adapted Information Services

2009 ◽  
pp. 336-343
Author(s):  
Thomas Mandl
Keyword(s):  
Digital Libraries ◽  
Success Factors ◽  
Common Knowledge ◽  
Library Services ◽  
User Interest ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Digital Library Services ◽  
User Interest Model ◽  
Search History

This chapter describes personalization strategies adopted in digital libraries. Personalization and individualization are introduced as means to improve the usability of digital library services. The goal of personalization for digital libraries is mainly the presentation of individual results to the user. This can be modelled based on a user interest model which is applied during the search process. Two users with the same query can receive different results based on their interest profile maintained by the system. Typical approaches and systems for individualizing the results of information retrieval systems are presented. The retrieval process is described. Knowledge sources and common knowledge representation for personalization are elaborated. Most common, the search history and documents accessed in the past are exploited for modelling the user interest. Finally, the chapter mentions drawbacks and success factors for personalization and individualization systems.

Context-Aware Applications for the Web

2011 ◽  
pp. 417-440
Author(s):  
Florian Daniel
Keyword(s):  
Context Awareness ◽  
Web Applications ◽  
User Profile ◽  
Expressive Power ◽  
Context Aware ◽  
Web Engineering ◽  
Runtime Adaptation ◽  
Model Driven ◽  
Context Data ◽  
Development Approach

Adaptivity (the runtime adaptation to user profile data) and context-awareness (the runtime adaptation to generic context data) have been gaining momentum in the field of Web engineering over the last years, especially in response to the ever growing demand for highly personalized services and applications coming from end users. Developing context-aware and adaptive Web applications requires addressing a few design concerns that are proper of such kind of applications and independent of the chosen modeling paradigm or programming language. In this chapter we characterize the design of context-aware Web applications, the authors describe a conceptual, model-driven development approach, and they show how the peculiarities of context-awareness require augmenting the expressive power of conceptual models in order to be able to express adaptive application behaviors.

Recommender Systems in Digital Libraries Using Artificial Intelligence and Machine Learning

2021 ◽  
pp. 162-178
Author(s):  
Namik Delilovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Libraries ◽  
Full Text ◽  
Text Search ◽  
Full Text Search ◽  
Artificial Intelligence Techniques ◽  
Search Results ◽  
Advanced Search ◽  
Search Field

Searching for contents in present digital libraries is still very primitive; most websites provide a search field where users can enter information such as book title, author name, or terms they expect to be found in the book. Some platforms provide advanced search options, which allow the users to narrow the search results by specific parameters such as year, author name, publisher, and similar. Currently, when users find a book which might be of interest to them, this search process ends; only a full-text search or references at the end of the book may provide some additional pointers. In this chapter, the author is going to give an example of how a user could permanently get recommendations for additional contents even while reading the article, using present machine learning and artificial intelligence techniques.

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.

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