Use Of Information Retrieval Research Articles

A Survey on Event-Based News Narrative Extraction

Narratives are fundamental to our understanding of the world, providing us with a natural structure for knowledge representation over time. Computational narrative extraction is a subfield of artificial intelligence that makes heavy use of information retrieval and natural language processing techniques. Despite the importance of computational narrative extraction, relatively little scholarly work exists on synthesizing previous research and strategizing future research in the area. In particular, this article focuses on extracting news narratives from an event-centric perspective. Extracting narratives from news data has multiple applications in understanding the evolving information landscape. This survey presents an extensive study of research in the area of event-based news narrative extraction. In particular, we screened more than 900 articles, which yielded 54 relevant articles. These articles are synthesized and organized by representation model, extraction criteria, and evaluation approaches. Based on the reviewed studies, we identify recent trends, open challenges, and potential research lines.

Linked Data Triples Enhance Document Relevance Classification

Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news-feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets.

Relation Between the Degree of Use of Smartphones and Negative Emotions in People With Visual Impairment.

The use of smartphones has become commonplace, even among people with disabilities. The purpose of this study was to investigate the effect of smartphone use on the negative emotions of people with visual impairment. This study analyzed data from 30 respondents with visual impairments obtained from the 2016 Internet Overdependence Survey in South Korea. The analysis was based on partial least squares regression with information search, leisure, communication, and online transactions as independent variables, and negative emotions comprising depression, anxiety, and loneliness as the dependent variables. Among people with visual impairment, the use of smartphones as a means of communication decreased negative emotions while their use for leisure or information search was related to an increase in negative emotions such as depression and loneliness. Use for information retrieval was found to be associated with a low level of anxiety, and use for online transactions was associated with low loneliness. The results of this study showed that the use of the Internet can be a means of providing interaction opportunities and reducing negation emotions for people with visual impairment.

HSP: A hybrid selection and prioritisation of regression test cases based on information retrieval and code coverage applied on an industrial case study

The usual way to guarantee quality of software products is via testing. This paper presents a novel strategy for selection and prioritisation of Test Cases (TC) for Regression testing. In the lack of code artifacts from where to derive Test Plans, this work uses information conveyed by textual documents maintained by Industry, such as Change Requests. The proposed process is based on Information Retrieval techniques combined with indirect code coverage measures to select and prioritise TCs. The aim is to provide a high coverage Test Plan which would maximise the number of bugs found. This process was implemented as a prototype tool which was used in a case study with our industrial partner (Motorola Mobility). Experiments results revealed that the combined strategy provides better results than the use of information retrieval and code coverage independently. Yet, it is worth mentioning that any of these automated options performed better than the previous manual process deployed by our industrial partner to create test plans.

Research on Information Retrieval Behavior Based on College Readers’ Cognition

This paper takes the information retrieval behaviors of college students and researchers as the research object, and discusses the reading cognition and reading behavior habits of college readers through empirical analysis. The study found that there are large differences in reading cognition and reading needs among people of different backgrounds and levels. In the reading and retrieval behavior pattern, they are different in their grasp and use of information retrieval. The main innovation of this paper is to take the central colleges and universities as an example, and analyze the information retrieval behaviors of different levels of students in colleges and universities, and summarize the regular characteristics, which has certain demonstration significance.

Detecting duplicate bug reports with software engineering domain knowledge

Bug deduplication, ie, recognizing bug reports that refer to the same problem, is a challenging task in the software‐engineering life cycle. Researchers have proposed several methods primarily relying on information‐retrieval techniques. Our work motivated by the intuition that domain knowledge can provide the relevant context to enhance effectiveness, attempts to improve the use of information retrieval by augmenting with software‐engineering knowledge. In our previous work, we proposed the software‐literature‐context method for using software‐engineering literature as a source of contextual information to detect duplicates. If bug reports relate to similar subjects, they have a better chance of being duplicates. Our method, being largely automated, has a potential to substantially decrease the level of manual effort involved in conventional techniques with a minor trade‐off in accuracy.In this study, we extend our work by demonstrating that domain‐specific features can be applied across projects than project‐specific features demonstrated previously while still maintaining performance. We also introduce a hierarchy‐of‐context to capture the software‐engineering knowledge in the realms of contextual space to produce performance gains. We also highlight the importance of domain‐specific contextual features through cross‐domain contexts: adding context improved accuracy; Kappa scores improved by at least 3.8% to 10.8% per project.

Comparative Analysis of Data Mining Tools and Techniques for Information Retrieval

Background/Objectives: There are a lot of information retrieval techniques available for getting information from different kinds of sources. Main aim of this paper is to improve information retrieval activities to a higher level. Different methods for information retrieval have been studied and discussed. It involves use of Fuzzy Ontology Generation framework (FOGA) framework along with Formal Concept Analysis (FCA) based clustering and keyword matching approach. Hidden Markov Model has been used for retrieval of data from search engines in an intelligent and efficient way for correct identification and retrieval from the database. Classification algorithm has been used for detection of community and conversion of large community graph to sub community graph for its better study and usage. Findings: It has been found that fuzzy ontology generation framework can automatically generate the fuzzy ontology which is very hard and time consuming task otherwise. Clustering of data can be done using the technique of formal concept analysis along with keyword matching method. There is a large amount of data available under similar words but with different meanings. So there has been a lot of problem in retrieval of exact data as required in a very short amount of time. In this case Hidden Markov Model can be used which can find the non-observable or hidden stochastic process from the observable stochastic process. Application/Improvements: Generalized Expectation-Maximization algorithm used with Hidden Markov Model can find unknown parameters. By adding frequency tracking algorithm along with Hidden Markov Model, we can also track audible data from a large database. Community detection algorithm along with Informap and Bigclam algorithms used with Hidden Markov Model will increase the modularity of data. Its applications include use of information retrieval of different types of data in an extremely faster and efficient way.

COMAT: core ontology of matter

Purpose – Matter is an important topic of science as a discipline since its inception. Nevertheless, along with the evolution of semantic web, matter has got equal importance among the ontology developers. The current work describes an ontology of matter that the authors developed in the lab. The purpose of this paper is to come up with an exhaustive list of concepts and relations to cover matter domain under one umbrella, after identifying the gaps in the present ontologies. Design/methodology/approach – Ontology was developed following faceted analytico-synthetic approach of knowledge organization. The authors followed hybrid developmental approach which includes top-down as well as bottom-up development strategy, for creating classes and subclasses. The authors modelled matter domain comprehensively considering different aspects of matter. The theories behind the modelling approach helps to maintain the consistency of further extensions. Findings – Final ontology has around 280 concepts and as many as 60 properties which include both object property and datatype property. Research limitations/implications – There exists very vague definition of concepts in different subject areas, as matter is a domain of study in physics, chemistry, material science, metallurgy, etc. Same material has been adopted differently depending upon purpose of its study/use in that field. For example aspirin is simply a chemical compound in chemistry, whereas in medicine it is also an agent. Practical implications – Present work claims to influence the ontology engineers to develop more extension to this core ontology of matter (COMAT). Also this will find its use in information retrieval, semantic annotations and in several other semantic knowledge-based systems. Originality/value – COMAT is the most recent work of the domain. Originality lies in the way matter domain has been looked up, from a very wide perspective, as well as in the approach of modelling the domain.

Improving IR‐based traceability recovery via noun‐based indexing of software artifacts

SUMMARYOne of the most successful applications of textual analysis in software engineering is the use of information retrieval (IR) methods to reconstruct traceability links between software artifacts. Unfortunately, because of the limitations of both the humans developing artifacts and the IR techniques any IR‐based traceability recovery method fails to retrieve some of the correct links, while on the other hand it also retrieves links that are not correct. This limitation has posed challenges for researchers that have proposed several methods to improve the accuracy of IR‐based traceability recovery methods by removing the ‘noise’ in the textual content of software artifacts (e.g., by removing common words or increasing the importance of critical terms). In this paper, we propose a heuristic to remove the ‘noise’ taking into account the linguistic nature of words in the software artifacts. In particular, the language used in software documents can be classified as a technical language, where the words that provide more indication on the semantics of a document are the nouns. The results of a case study conducted on five software artifact repositories indicate that characterizing the context of software artifacts considering only nouns significantly improves the accuracy of IR‐based traceability recovery methods. Copyright © 2012 John Wiley & Sons, Ltd.

The ISO 25964 data model for the structure of an information retrieval thesaurus

AbstractEditor's SummaryThe international standard for thesaurus structure, ISO 259641:2011 – Thesauri for Information Retrieval, published August 2011, presents an extensive data model more advanced than previous versions. The standard facilitates presenting a knowledge organization system in machine compatible SKOS (simple knowledge organization system) format. The model lays out the concepts and terminology applicable to single thesauri, establishing the important difference between concepts and terms. It provides strategies to go beyond simple hierarchical relationships and related concepts, clarifying the nature of relationships between concepts. The new standard addresses several details in thesaurus construction and use for information retrieval, including compound equivalence and node labels. The updated standard supports concept groups or microthesauri and provides for term notes and file versions. Mapping concepts between thesauri will be covered in Part 2 of the standard.

In Memoriam

Madeline M. Henderson, 88, of Frederick, died on July 17, 2011, at Frederick Memorial Hospital. She was a longtime member and pioneer of ASIS&T. She was also a member of the American Chemical Society and the American Association for Advancement of Science. Madeline was predeceased by her husband Richard who died in 2008. She is survived by her children, Anne DeRito and husband Frank of Florida; Matthew R. Henderson of Boonsboro; Katherine DiLima of Germantown; and Laura Hicks and husband Brian of Frederick, and by ten grandchildren and one great grandchild. Those desiring may make memorial contributions in her name to the Homewood Foundation, P.O. Box 250, Williamsport, MD 21795. Our thanks to Robert V. Williams for the following look at Madeline's career. by Robert V. Williams Madeline M. (Berry) Henderson was an active contributor to and innovator in the field of documentation/information science. Her professional life provides multiple examples of the phrase “present at the creation” during the early development of information science in the period 1950–75. Her first significant contribution was in chemical information science, where she was a close associate and co-worker of JamesW. Perry, one of the foremost developers of the early ideas about information retrieval and chemical information science. With Perry and others, includingAllen Kent, she coedited the first two significant publications on the use of punched cards for information retrieval systems and was the leader in the first efforts to evaluate chemical notation systems for use in information retrieval. These efforts brought national and international attention as well as numerous citations to her work. Three years after the 1952 establishment of the National Science Foundation's Office of Scientific Information, Henderson was evaluating grant proposals for the emerging field of documentation/information science and in the process of crafting two of the most significant publications that both tracked and influenced the development of the field. She not only was the creator of these two publications but also did the “legwork” investigating the projects, thus likely becoming the most knowledgeable person in the field about research and development work during this period. Her work at the National Bureau of Standards on copyright, standards and fostering cooperation among the libraries of U.S. federal agencies was an important factor in their success, particularly in library automation. Adapted from Libraries & the Cultural Record, Vol. 45, No. 2, 2010 ©2010 by the University of Texas Press, P.O. Box 7819, Austin, TX 78713-7819

Revisiting the importance of cognition in information science

For a considerable amount of time the field of information science has employed its own, as well as the knowledge bases and methods of other fields (with productive results). One field that has been appropriated from has been cognitive science. Cognitive science, however, has been in flux over the last few decades, with different conceptual frameworks assuming ascendance at various times. That dynamic implies that information science should pay close attention to what is occurring in cognitive science in order to investigate the most complex of challenges in information retrieval use, behaviour, and other phenomena. This paper includes a review of the frameworks of cognitive science and suggests that some of the most recent work in that field holds promise for development of thought and inquiry in information science. Understanding of the complex individual processes within human brains, the relationships among thinking communicators, and the relationship of brain and mind, is one of the areas where particular attention should be paid.

Evaluation of information retrieval for E-discovery

The effectiveness of information retrieval technology in electronic discovery (E-discovery) has become the subject of judicial rulings and practitioner controversy. The scale and nature of E-discovery tasks, however, has pushed traditional information retrieval evaluation approaches to their limits. This paper reviews the legal and operational context of E-discovery and the approaches to evaluating search technology that have evolved in the research community. It then describes a multi-year effort carried out as part of the Text Retrieval Conference to develop evaluation methods for responsive review tasks in E-discovery. This work has led to new approaches to measuring effectiveness in both batch and interactive frameworks, large data sets, and some surprising results for the recall and precision of Boolean and statistical information retrieval methods. The paper concludes by offering some thoughts about future research in both the legal and technical communities toward the goal of reliable, effective use of information retrieval in E-discovery.

Roadcast

Content sharing through vehicle-to-vehicle communication can help people find their interested content on the road. In VANETs, due to limited contact duration time and the unreliable wireless connection, a vehicle can only get the useful data when it meets the vehicle which has the exactly matching data. However, the probability of such cases is very low. To improve the performance of content sharing in intermittently connected VANETs, we propose a novel P2P content sharing scheme called Roadcast. Roadcast relaxes user's query requirement a little bit so that each user can have more chances to get the requested content quickly. Furthermore, Roadcast ensures popular data is more likely to be shared with other vehicles so that the performance of overall query delay can be improved. Roadcast consists of two components called popularity aware content retrieval and popularity aware data replacement. The popularity aware content retrieval scheme makes use of Information Retrieval (IR) techniques to find the most relevant data towards user's query, but significantly different from IR techniques by taking the data popularity factor into consideration. The popularity aware data replacement algorithm ensures that the density of different data is proportional to the square-root of their popularity in the system steady state, which firmly obeys the optimal "square-root" replication rule [6]. Results based on real city map and real traffic model show that Roadcast outperforms other content sharing schemes in VANETs.

Speeding-up information retrieval with the employment of a multi-agent system

This paper focuses on Mobile Agents (MAs) and their use for information retrieval. A multi-agent system is considered; a number of agents are involved in a cooperative effort to retrieve distributed data from network nodes. Our objective is to find the routes of the agents so that the total completion time is minimised. The system design goal of producing an 'optimal' answer with minimal use of communication and processing resources is the driving reason for testing three heuristic algorithms. Our endmost goal is to achieve an 'acceptable' answer using a 'reasonable' amount of communication resources.

Using information retrieval based coupling measures for impact analysis

Coupling is an important property of software systems, which directly impacts program comprehension. In addition, the strength of coupling measured between modules in software is often used as a predictor of external software quality attributes such as changeability, ripple effects of changes and fault-proneness. This paper presents a new set of coupling measures for Object-Oriented (OO) software systems measuring conceptual coupling of classes. Conceptual coupling is based on measuring the degree to which the identifiers and comments from different classes relate to each other. This type of relationship, called conceptual coupling, is measured through the use of Information Retrieval (IR) techniques. The proposed measures are different from existing coupling measures and they capture new dimensions of coupling, which are not captured by the existing coupling measures. The paper investigates the use of the conceptual coupling measures during change impact analysis. The paper reports the findings of a case study in the source code of the Mozilla web browser, where the conceptual coupling metrics were compared to nine existing structural coupling metrics and proved to be better predictors for classes impacted by changes.

Route planning for agent-based information retrieval

This article focuses on Mobile Agents and their use for information retrieval. A multi-agent system is considered; a number of agents are involved in a collective effort to retrieve distributed data from network nodes. Increasing the number of agents may speed-up information retrieval but is burdensome to performance. Initiating with a given number of agents, our objective is to determine the routes of the agents so that the task completion time is minimized. Two known and one new polynomial-time algorithms are tested that produce near-optimal solutions. Simulation results show the cases for which each one is most effective. Additionally, we study the influence of various parameters on the solution. By parametrically varying the number of agents, our method can be used to determine the minimum number that satisfies the desired trade-off between time and performance.

Semantic clustering: Identifying topics in source code

Many of the existing approaches in Software Comprehension focus on program structure or external documentation. However, by analyzing formal information the informal semantics contained in the vocabulary of source code are overlooked. To understand software as a whole, we need to enrich software analysis with the developer knowledge hidden in the code naming. This paper proposes the use of information retrieval to exploit linguistic information found in source code, such as identifier names and comments. We introduce Semantic Clustering, a technique based on Latent Semantic Indexing and clustering to group source artifacts that use similar vocabulary. We call these groups semantic clusters and we interpret them as linguistic topics that reveal the intention of the code. We compare the topics to each other, identify links between them, provide automatically retrieved labels, and use a visualization to illustrate how they are distributed over the system. Our approach is language independent as it works at the level of identifier names. To validate our approach we applied it on several case studies, two of which we present in this paper. Note: Some of the visualizations presented make heavy use of colors. Please obtain a color copy of the article for better understanding.

Adjectives as indicators of subjectivity in documents

AbstractThe goal of this research is to automatically predict human judgments of document qualities such as subjectivity, verbosity and depth. In this paper, we explore the behavior of adjectives as indicators of subjectivity in documents. Specifically, we test whether a subset of automatically derived subjective adjectives (Wiebe, 2000b), selected a priori, behaves differently than other adjectives. 3,200 documents were ranked by 100 subjects as being high or low in nine document qualities (Tang, Ng, Strzalkowski, & Kantor, 2003). We report a statistically significant correlation between the occurrence of adjectives in documents and human judgments of subjectivity. More importantly, we find that the subset of subjective adjectives is more strongly correlated with subjectivity than adjectives in general. These results can be used to identify document qualities for use in information retrieval and question‐answering systems.

Information Retrieval Using Association Lists

The paper presents the main models of information retrieval in text. The idea of association list and its use for information retrieval is introduced. The paper contains the evaluation results of the algorithms under discussion.