Effective keyword search on graph data using limited root redundancy of answer trees

2018 ◽  
Vol 14 (3) ◽  
pp. 299-316 ◽  
Author(s):  
Chang-Sup Park
Keyword(s):  
Query Processing ◽  
Keyword Search ◽  
Information Need ◽  
Keyword Query ◽  
Content Type ◽  
Graph Data ◽  
Distinct Root ◽  
Path Index ◽  
Complex Graph ◽  
Efficient Query Processing

Purpose This paper aims to propose a new keyword search method on graph data to improve the relevance of search results and reduce duplication of content nodes in the answer trees obtained by previous approaches based on distinct root semantics. The previous approaches are restricted to find answer trees having different root nodes and thus often generate a result consisting of answer trees with low relevance to the query or duplicate content nodes. The method allows limited redundancy in the root nodes of top-k answer trees to produce more effective query results. Design/methodology/approach A measure for redundancy in a set of answer trees regarding their root nodes is defined, and according to the metric, a set of answer trees with limited root redundancy is proposed for the result of a keyword query on graph data. For efficient query processing, an index on the useful paths in the graph using inverted lists and a hash map is suggested. Then, based on the path index, a top-k query processing algorithm is presented to find most relevant and diverse answer trees given a maximum amount of root redundancy allowed for a set of answer trees. Findings The results of experiments using real graph datasets show that the proposed approach can produce effective query answers which are more diverse in the content nodes and more relevant to the query than the previous approach based on distinct root semantics. Originality/value This paper first takes redundancy in the root nodes of answer trees into account to improve the relevance and content nodes redundancy of query results over the previous distinct root semantics. It can satisfy the users’ various information need on a large and complex graph data using a keyword-based query.

Effective keyword query processing with an extended answer structure in large graph databases

2014 ◽  
Vol 10 (1) ◽  
pp. 65-84 ◽  
Author(s):  
Chang-Sup Park ◽  
Sungchae Lim
Keyword(s):  
Query Processing ◽  
Search Algorithm ◽  
Previous Method ◽  
Search Method ◽  
Graph Databases ◽  
Information Need ◽  
Keyword Query ◽  
Processing Scheme ◽  
Content Type ◽  
Inverted List

Purpose – The paper aims to propose an effective method to process keyword-based queries over graph-structured databases which are widely used in various applications such as XML, semantic web, and social network services. To satisfy users' information need, it proposes an extended answer structure for keyword queries, inverted list indexes on keywords and nodes, and query processing algorithms exploiting the inverted lists. The study aims to provide more effective and relevant answers to a given query than the previous approaches in an efficient way. Design/methodology/approach – A new relevance measure for nodes to a given keyword query is defined in the paper and according to the relevance metric, a new answer tree structure is proposed which has no constraint on the number of keyword nodes chosen for each query keyword. For efficient query processing, an inverted list-style index is suggested which pre-computes connectivity and relevance information on the nodes in the graph. Then, a query processing algorithm based on the pre-constructed inverted lists is designed, which aggregates list entries for each graph node relevant to given keywords and identifies top-k root nodes of answer trees most relevant to the given query. The basic search method is also enhanced by using extend inverted lists which store additional relevance information of the related entries in the lists in order to estimate the relevance score of a node more closely and to find top-k answers more efficiently. Findings – Experiments with real datasets and various test queries were conducted for evaluating effectiveness and performance of the proposed methods in comparison with one of the previous approaches. The experimental results show that the proposed methods with an extended answer structure produce more effective top-k results than the compared previous method for most of the queries, especially for those with OR semantics. An extended inverted list and enhanced search algorithm are shown to achieve much improvement on the execution performance compared to the basic search method. Originality/value – This paper proposes a new extended answer structure and query processing scheme for keyword queries on graph databases which can satisfy the users' information need represented by a keyword set having various semantics.

scholarly journals Effective keyword query structuring using NER for XML retrieval

2015 ◽  
Vol 11 (1) ◽  
pp. 33-53
Author(s):  
Abubakar Roko ◽  
Shyamala Doraisamy ◽  
Azrul Hazri Jantan ◽  
Azreen Azman
Keyword(s):  
Semantic Information ◽  
Keyword Search ◽  
Query Languages ◽  
Entity Recognition ◽  
Experimental Result ◽  
Query Interface ◽  
Search Query ◽  
Keyword Query ◽  
Xml Retrieval ◽  
Content Type

Purpose – The purpose of this paper is to propose and evaluate XKQSS, a query structuring method that relegates the task of generating structured queries from a user to a search engine while retaining the simple keyword search query interface. A more effective way for searching XML database is to use structured queries. However, using query languages to express queries prove to be difficult for most users since this requires learning a query language and knowledge of the underlying data schema. On the other hand, the success of Web search engines has made many users to be familiar with keyword search and, therefore, they prefer to use a keyword search query interface to search XML data. Design/methodology/approach – Existing query structuring approaches require users to provide structural hints in their input keyword queries even though their interface is keyword base. Other problems with existing systems include their inability to put keyword query ambiguities into consideration during query structuring and how to select the best generated structure query that best represents a given keyword query. To address these problems, this study allows users to submit a schema independent keyword query, use named entity recognition (NER) to categorize query keywords to resolve query ambiguities and compute semantic information for a node from its data content. Algorithms were proposed that find user search intentions and convert the intentions into a set of ranked structured queries. Findings – Experiments with Sigmod and IMDB datasets were conducted to evaluate the effectiveness of the method. The experimental result shows that the XKQSS is about 20 per cent more effective than XReal in terms of return nodes identification, a state-of-art systems for XML retrieval. Originality/value – Existing systems do not take keyword query ambiguities into account. XKSS consists of two guidelines based on NER that help to resolve these ambiguities before converting the submitted query. It also include a ranking function computes a score for each generated query by using both semantic information and data statistic, as opposed to data statistic only approach used by the existing approaches.

Private ethical fund investors across countries and time: a survey-based review

2015 ◽  
Vol 7 (4) ◽  
pp. 379-411 ◽  
Author(s):  
Anett Wins ◽  
Bernhard Zwergel
Keyword(s):  
Keyword Search ◽  
Relevant Literature ◽  
Future Research ◽  
Socially Responsible ◽  
Screening Criteria ◽  
Content Type ◽  
Responsible Investment ◽  
Private Investors ◽  
Ethical Funds ◽  
Fund Portfolio

Purpose – This paper aims to provide an overview of the literature to point out similarities and differences among private ethical investors across countries and time. Over the past three decades, many surveys have been conducted to advance the understanding of the demographic characteristics, motivation and morals of private ethical investors across countries and time. To date, the survey-based evidence on private investors into ethical funds is geographically rather segmented, and the research questions are fairly diverse. This permits only very temporally or regionally selective conclusions. Thereby, the authors identify interesting topics for future research. Design/methodology/approach – To identify the relevant literature for our review, the authors carried out a structured Boolean keyword search using major library services and databases. Findings – When questions about negative screening criteria are presented in a direct investment context, the consensus of private ethical investors “worldwide” (on average) is that social screening issues are most important, followed by ecological and moral topics. The percentage of ethical funds in the fund portfolio of the average private ethical investor in Europe seems to increase when the investor exhibits high degrees of pro-social attitudes and perceived consumer effectiveness. European private ethical investors are of the opinion that ethical funds perform worse but are less risky than conventional funds. Practical implications – The authors make suggestions on how investment companies should design their funds so that they can attract more socially responsible investors. Originality/value – The paper is of particular value because it focuses on private investors in the fast growing retail market of socially responsible investment funds.

An interactive query-based approach for summarizing scientific documents

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Farnoush Bayatmakou ◽  
Azadeh Mohebi ◽  
Abbas Ahmadi
Keyword(s):  
User Satisfaction ◽  
Information Needs ◽  
Specific Information ◽  
Information Need ◽  
Science Data ◽  
Data Set ◽  
Content Type ◽  
Interactive Query ◽  
Summarization System ◽  
Clear Idea

Purpose Query-based summarization approaches might not be able to provide summaries compatible with the user’s information need, as they mostly rely on a limited source of information, usually represented as a single query by the user. This issue becomes even more challenging when dealing with scientific documents, as they contain more specific subject-related terms, while the user may not be able to express his/her specific information need in a query with limited terms. This study aims to propose an interactive multi-document text summarization approach that generates an eligible summary that is more compatible with the user’s information need. This approach allows the user to interactively specify the composition of a multi-document summary. Design/methodology/approach This approach exploits the user’s opinion in two stages. The initial query is refined by user-selected keywords/keyphrases and complete sentences extracted from the set of retrieved documents. It is followed by a novel method for sentence expansion using the genetic algorithm, and ranking the final set of sentences using the maximal marginal relevance method. Basically, for implementation, the Web of Science data set in the artificial intelligence (AI) category is considered. Findings The proposed approach receives feedback from the user in terms of favorable keywords and sentences. The feedback eventually improves the summary as the end. To assess the performance of the proposed system, this paper has asked 45 users who were graduate students in the field of AI to fill out a questionnaire. The quality of the final summary has been also evaluated from the user’s perspective and information redundancy. It has been investigated that the proposed approach leads to higher degrees of user satisfaction compared to the ones with no or only one step of the interaction. Originality/value The interactive summarization approach goes beyond the initial user’s query, while it includes the user’s preferred keywords/keyphrases and sentences through a systematic interaction. With respect to these interactions, the system gives the user a more clear idea of the information he/she is looking for and consequently adjusting the final result to the ultimate information need. Such interaction allows the summarization system to achieve a comprehensive understanding of the user’s information needs while expanding context-based knowledge and guiding the user toward his/her information journey.

Information Seeking Motivation Scale development: a self-determination perspective

2017 ◽  
Vol 73 (5) ◽  
pp. 1034-1052 ◽  
Author(s):  
Ana Dubnjakovic
Keyword(s):  
Higher Education ◽  
Information Seeking ◽  
Self Determination ◽  
Information Need ◽  
Measurement Instruments ◽  
Content Type ◽  
Motivation Scale ◽  
Current Sample ◽  
Situational Motivation ◽  
Domain Level

Purpose Using self-determination motivation theory as a theoretical framework, the purpose of this paper is to examine information seeking motivation at the domain level in higher education setting. Design/methodology/approach Confirmatory factor analysis was used to validate the Information Seeking Motivation Scale – College Version (ISMS – C). Findings ISMS – C was validated in the information seeking context. Consistent with self-determination theory (SDT), the results imply that students approach research tasks for both controlled and autonomous reasons. Research limitations/implications All constructs representing extrinsic and intrinsic motivation on a continuum were confirmed. However, amotivation proved difficult to define with the current sample. Additional studies need to be conducted in higher education setting in order to confirm its existence. Practical implications Given that the situational motivation is contingent on domain-level motivation, the ISMS – C scale can be helpful in promoting lasting intrinsic information seeking motivation at that level. Originality/value Consistent with the subjectivist orientation in information sciences which aims to account for cognitive and affective forces behind information need, ISMS constructed in the current study is one of the first measurement instruments to account for a spectrum of information seeking motivations at the domain level.

Top-Down XML Keyword Query Processing

2016 ◽  
Vol 28 (5) ◽  
pp. 1340-1353 ◽  
Author(s):  
Junfeng Zhou ◽  
Wei Wang ◽  
Ziyang Chen ◽  
Jeffrey Xu Yu ◽  
Xian Tang ◽  
...  
Keyword(s):  
Query Processing ◽  
Top Down ◽  
Keyword Query ◽  
Xml Keyword Query

Innovation growth from knowledge transfer in international strategic alliances

2018 ◽  
Vol 11 (4) ◽  
pp. 483-496 ◽  
Author(s):  
Vijita Aggarwal ◽  
Madhavi Kapoor
Keyword(s):  
Knowledge Transfer ◽  
Strategic Alliances ◽  
Dynamic Capabilities ◽  
Keyword Search ◽  
Journal Ranking ◽  
Content Type ◽  
Research Framework ◽  
International Strategic Alliances ◽  
Research Gap ◽  
Highly Cited

Purpose The purpose of this paper is to conduct a literature review on knowledge transfer and international strategic alliances to propose a research framework based on the theory of dynamic capabilities. A qualitative and quantitative review has been conducted to find out the past research patterns, emerging trends, and research gaps. Design/methodology/approach The qualitative review of more than 300 articles identified by keyword search, reference, and citation search has resulted in 130 most relevant articles. Citation analysis is performed on these studies, their journals, and authors by leveraging the international platforms of SCImago Journal Ranking, Google Scholar, and ResearchGate. Findings The study enlists the highly cited studies, their journals, and authors with possible explanations for being highly cited. Criticisms of dynamic capabilities theory have been explained, and a research framework for the application of this theory in the context of international strategic alliances to fill the research gap has been proposed. Originality/value Currently, various bibliometric studies are growing in number. This study is not only a review study, but also proposes a research framework to fill the identified research gap.

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