scholarly journals Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results

2007 ◽  
Vol 4 ◽  
pp. 49-54 ◽  
Author(s):  
Nicole Lang Beebe ◽  
Jan Guynes Clark
Keyword(s):  
Information Retrieval ◽  
Search Results ◽  
Text String ◽  
Digital Forensic ◽  
Retrieval Effectiveness ◽  
String Searching ◽  
Clustering Search

A Re-Ranking Method of Search Results Based on Keyword and User Interest

2010 ◽  
pp. 108-121
Author(s):  
Ming Xu ◽  
Hong-Rong Yang ◽  
Ning Zheng
Keyword(s):  
Experimental Results ◽  
Search Process ◽  
Improved Method ◽  
User Interest ◽  
High Recall ◽  
String Match ◽  
Electronic Evidence ◽  
Search Results ◽  
Text String ◽  
Digital Forensic

It is a pivotal task for a forensic investigator to search a hard disk to find interesting evidences. Currently, most search tools in digital forensic field, which utilize text string match and index technology, produce high recall (100%) and low precision. Therefore, the investigators often waste vast time on huge irrelevant search hits. In this chapter, an improved method for ranking of search results was proposed to reduce human efforts on locating interesting hits. The K-UIH (the keyword and user interest hierarchies) was constructed by both investigator-defined keywords and user interest learnt from electronic evidence adaptive, and then the K-UIH was used to re-rank the search results. The experimental results indicated that the proposed method is feasible and valuable in digital forensic search process.

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.

Post-retrieval search hit clustering to improve information retrieval effectiveness: Two digital forensics case studies

2011 ◽  
Vol 51 (4) ◽  
pp. 732-744 ◽  
Author(s):  
Nicole Lang Beebe ◽  
Jan Guynes Clark ◽  
Glenn B. Dietrich ◽  
Myung S. Ko ◽  
Daijin Ko
Keyword(s):  
Information Retrieval ◽  
Case Studies ◽  
Digital Forensics ◽  
Retrieval Effectiveness

scholarly journals Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust

2017 ◽  
Vol 10 (2) ◽  
pp. 311-325
Author(s):  
Suruchi Chawla
Keyword(s):  
Information Retrieval ◽  
Fuzzy Set ◽  
Query Expansion ◽  
Information Need ◽  
Data Set ◽  
Search Results ◽  
Intelligent Information Retrieval ◽  
Main Challenge ◽  
Fuzzy Query ◽  
Intelligent Information

The main challenge for effective web Information Retrieval(IR) is to infer the information need from user’s query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user’s information need. Trust based web page recommendations retrieve search results according to the user’s information need. In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user’s information need and trust is used for recommendation of web pages according to the user’s information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.

scholarly journals Experion: A framework for contextualizing evidence in expert finding

2021 ◽  
Author(s):  
Rodrigo Gonçalves ◽  
Carina F. Dorneles
Keyword(s):  
Information Retrieval ◽  
Research Topic ◽  
Expert Finding ◽  
Search Results ◽  
Open Research ◽  
Academic Activities ◽  
Search Processes ◽  
Data Source ◽  
The Moment ◽  
Expertise Retrieval

Expert finding is traditionally related to a subject of research in information retrieval and, often, is taken to mean "expertise retrieval within a specific organization". The task involves finding an expert in an expertise topic. Even though there are interesting proposals in the literature, they do not consider the context in which a given expertise is bound. This Ph.D. thesis introduces the concept of a framework that chronologically contextualizes search results in expert finding. Our motivation is to provide more accurate results of search processes related to finding experts in a given topic, contextualizing the expertise on professional/academic activities, an open research topic. In this paper, we present the main concepts of the framework we are developing and a general overview of its operation. At the moment, we are using the Lattes platform as a data source, for which we developed a process to extract expertise evidence, supported by the Crossref database.

An Ontology-Based Search Tool in the Semantic Web

2013 ◽  
pp. 221-249 ◽  
Author(s):  
Constanta-Nicoleta Bodea ◽  
Adina Lipai ◽  
Maria-Iuliana Dascalu
Keyword(s):  
Semantic Web ◽  
Lexical Database ◽  
Search Results ◽  
Concept Space ◽  
Ontological Approach ◽  
Search Tool ◽  
Clustering Search ◽  
Meta Search ◽  
Specific Interests ◽  
Initial List

The chapter presents a meta-search tool developed in order to deliver search results structured according to the specific interests of users. Meta-search means that for a specific query, several search mechanisms could be simultaneously applied. Using the clustering process, thematically homogenous groups are built up from the initial list provided by the standard search mechanisms. The results are more user-oriented, thanks to the ontological approach of the clustering process. After the initial search made on multiple search engines, the results are pre-processed and transformed into vectors of words. These vectors are mapped into vectors of concepts, by calling an educational ontology and using the WordNet lexical database. The vectors of concepts are refined through concept space graphs and projection mechanisms, before applying the clustering procedure. The chapter describes the proposed solution in the framework of other existent clustering search solutions. Implementation details and early experimentation results are also provided.

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