Exploratory Search Systems Research Articles

A term-based and citation network-based search system for COVID-19.

The COVID-19 pandemic resulted in an unprecedented production of scientific literature spanning several fields. To facilitate navigation of the scientific literature related to various aspects of the pandemic, we developed an exploratory search system. The system is based on automatically identified technical terms, document citations, and their visualization, accelerating identification of relevant documents. It offers a multi-view interactive search and navigation interface, bringing together unsupervised approaches of term extraction and citation analysis. We conducted a user evaluation with domain experts, including epidemiologists, biochemists, medicinal chemists, and medicine students. In general, most users were satisfied with the relevance and speed of the search results. More interestingly, participants mostly agreed on the capacity of the system to enable exploration and discovery of the search space using the graph visualization and filters. The system is updated on a weekly basis and it is publicly available at http://www.nactem.ac.uk/cord/.

Chronotopic information interaction: integrating temporal and spatial structure for historical indexing and interactive search

Abstract Domain-based learning and research are important applications driving the development of exploratory search systems. A wealth of historical information about events from around the world resides within documents on the web, yet contemporary search engines do not take advantage of the closely integrated temporal and spatial information found within these web pages for indexing and design of search user interfaces. This gap limits the use of the web as a resource for historical and geohistorical information seeking. In this article, we propose chronotopic information interaction as a new interaction concept for web search that explicitly links temporal and spatial entities to keywords using a space–time grid index and a paired search user interface. The space–time grid index allows different modes of interaction between spatial, temporal, and keyword-based views in the search user interface. We demonstrate the use of the space–time grid index and chronotopic information interaction concept with the development of Pteraform, a prototype of a search engine that enables users to explore information in the English version of Wikipedia through a geohistorical lens.

Grapevine: A profile‐based exploratory search and recommendation system for finding research advisors

AbstractFinding research advisors is an important and challenging task for college students. On one hand, a research advisor that matches student interests and past preparation could fully engage the student with an exciting and productive research experience. On the other hand, students are frequently unable to formulate their interests and experience in a way that allows them to independently find the most compatible advisors using search and browsing tools. This paper reports our experience with designing and evaluating Grapevine, an exploratory search and recommender system aimed at helping students, especially those less prepared and underrepresented in certain fields, find research advisors.

A Conceptual Model for the E-Commerce Application Recommendation Framework using Exploratory Search

The users helped by the search engine for online transactions in e-commerce; nonetheless, there is still a lack of search interest from the user and online shopping intentions. To boost the user's search for product recommendations, use a search engine for the quest but not for purchasing purposes. Existing frameworks have some significant problems in the recommendation technology, such as new product problems, fewer evaluation problems, a vast amount of data, etc. Many e-commerce applications lack a better search experience like an Exploratory search system. This research work aims to create a conceptual model for recommendation systems using exploratory search, to study the behavior of users and the efficacy of exploratory search in terms of the quality of the search results produced. Several machine learning algorithms are used in this research to classify e-commerce products and evaluate the performance of these algorithms. The full-text search mechanism is used to implement an exploratory search system. The exploratory search system is evaluated by three criteria called to look up, learn and investigate. After the experiments and evaluation, it is observed that AdaBoost over decision tree performs better than the other classification algorithms implemented. The exploratory search system satisfies the three requirements that are lookup learn and investigate during the search process. Contributions to this work are to build a conceptual model for the recommendation system through the dataset of user events and finding the implementation of an exploratory search mechanism in an e-commerce application.

A Roadmap to User-Controllable Social Exploratory Search

Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial and error, and on-the-fly selections, gathers and organizes information (resources). A range of innovative interfaces with increased user control has been developed to support the exploratory search process. In this work, we present our attempt to increase the power of exploratory search interfaces by using ideas of social search—for instance, leveraging information left by past users of information systems. Social search technologies are highly popular today, especially for improving ranking. However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking. This article presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to (i) express information needs by selecting keywords and (ii) to express preferences for incorporating social wisdom based on tag matching and user similarity. The interface promotes search transparency through color-coded stacked bars and rich tooltips. This work presents the full series of evaluations conducted to, first, assess the value of the social models in contexts independent to the user interface, in terms of objective and perceived accuracy. Then, in a study with the full-fledged system, we investigated system accuracy and subjective aspects with a structural model revealing that when users actively interacted with all of its control features, the hybrid system outperformed a baseline content-based–only tool and users were more satisfied.

Learn-As-You-Go: Feedback-Driven Result Ranking and Query Refinement for Interactive Data Exploration

Search has become a fundamental life activity. An individual looks for meaningful information not only for work-related requests but also for psychological and social satisfaction. A traditional query-result paradigm is able to deliver sensibly for short, navigational and close information requests. It is not always adequate, particularly when user is not familiar with the syntax and semantics of the data, and worse when the user is uncertain of his information needs. Such as the case in discovery-oriented applications e.g. scientific data, genomics, health data etc.. User’s intention additionally helps to navigate through the unknown data, formulate queries and find desired information. Also, user’s initial search aims and intentions evolve as new information is encountered. To guide users in navigating large datasets in discovery-related applications, we proposed an interactive exploratory search system with visualizations and interactive features to guide the user in recognizing the relationship between query and results retrieved. Proposed system incorporates explicit user feedback to re-rank results according to the user’s intent, contrary to traditional systems predefined relevance norms. The usage pattern and usability of various interactions are measured with the help of a suggested assessment framework. Proposed ESS outperforms present list based systems in lowering the cognitive burden, swift interactions, rapid and improved convergence towards search goals.

Designing a Comic Exploration System Using a Hierarchical Topic Classification of Reviews

The purpose of our research is information access support based on the contents of a comic book. For this purpose, it is necessary to obtain information related to the story and the characters. In our previous research, we extracted the information using a review sentence and built a comic search system based on the extraction results. This system determined the relationship between comics using the TF-IDF method. However, the TF-IDF method cannot take into account the meaning of each word included in the review sentence. Therefore, this system may not be able to provide accurate results based on comic relationships. In this study, we analyze the review sentence using a hierarchical topic classification. On the other hand, the extracted topics often contain words that hinder the user from guessing the contents of the comic. Especially, the named entities defined in the comic tend to cause the problem. The amount of information obtained by the user varies greatly based on the user's knowledge about the named entities. In this study, we investigated the influence of the named entity extracted from the comic reviews as the feature words. Our experiment revealed that the user's understandability was improved when the proportion of the named entities was decreased. Furthermore, we build an exploratory search system based on the topic.

State of the Art Exploration Systems for Linked Data: A Review

The ever-increasing amount of data available on the web is the result of the simplicity of sharing data over the current Web. To retrieve relevant information efficiently from this huge dataspace, a sophisticated search technology, which is further complicated due to the various data formats used, is crucial. Semantic Web (SW) technology has a prominent role in search engines to alleviate this issue by providing a way to understand the contextual meaning of data so as to retrieve relevant, high-quality results. An Exploratory Search System (ESS), is a featured data looking and search approach which helps searchers learn and explore their unclear topics and seeking goals through a set of actions. To retrieve high-quality retrievals for ESSs, Linked Open Data (LOD) is the optimal choice. In this paper, SW technology is reviewed, an overview of the search strategies is provided, and followed by a survey of the state of the art Linked Data Browsers (LDBs) and ESSs based on LOD. Finally, each of the LDBs and ESSs is compared with respect to several features such as algorithms, data presentations, and explanations.

Exploratory activity search

Real world activities can be found all around the web. In this paper, we present our work on an exploratory activity search system that is utilizing common-sense data to enrich the search for activities in the web. Two algorithms are proposed. One is the weighted query expansion algorithm using ConceptNet, and the other is the ranking algorithm using query expansion result whose weights are tuned by the genetic algorithms. Finally, a detailed double-blinded user evaluation of the system follows, where we simultaneously evaluate the activity search system from the viewpoint of novelty, serendipity, usefulness and interest. There were 20 participants in this evaluation, and the proposed system has shown a 13.4% increase in novelty and 11.6% increase in serendipity when compared with Bing search.

Cognitive styles within an exploratory search system for digital libraries

Purpose – The purpose of this paper is to investigate the effects of cognitive style on navigating a large digital library of cultural heritage information; specifically, the paper focus on the wholist/analytic dimension as experienced in the field of educational informatics. The hypothesis is that wholist and analytic users have characteristically different approaches when they explore, search and interact with digital libraries, which may have implications for system design. Design/methodology/approach – A detailed interactive IR evaluation of a large cultural heritage digital library was undertaken, along with the Riding CSA test. Participants carried out a range of information tasks, and the authors analysed their task performance, interactions and attitudes. Findings – The hypothesis on the differences in performance and behaviour between wholist and analytic users is supported. However, the authors also find that user attitudes towards the system are opposite to expectations and that users give positive feedback for functionality that supports activities in which they are cognitively weaker. Research limitations/implications – There is scope for testing results in a larger scale study, and/or with different systems. In particular, the findings on user attitudes warrant further investigation. Practical implications – Findings on user attitudes suggest that systems which support areas of weakness in users’ cognitive abilities are valued, indicating an opportunity to offer diverse functionality to support different cognitive weaknesses. Originality/value – A model is proposed suggesting a converse relationship between behaviour and attitudes; to support individual users displaying search/navigation behaviour mapped onto the strengths of their cognitive style, but placing greater value on interface features that support aspects in which they are weaker.

MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

Adaptive visualization for exploratory information retrieval

As the volume and breadth of online information is rapidly increasing, ad hoc search systems become less and less efficient to answer information needs of modern users. To support the growing complexity of search tasks, researchers in the field of information developed and explored a range of approaches that extend the traditional ad hoc retrieval paradigm. Among these approaches, personalized search systems and exploratory search systems attracted many followers. Personalized search explored the power of artificial intelligence techniques to provide tailored search results according to different user interests, contexts, and tasks. In contrast, exploratory search capitalized on the power of human intelligence by providing users with more powerful interfaces to support the search process. As these approaches are not contradictory, we believe that they can re-enforce each other. We argue that the effectiveness of personalized search systems may be increased by allowing users to interact with the system and learn/investigate the problem in order to reach the final goal. We also suggest that an interactive visualization approach could offer a good ground to combine the strong sides of personalized and exploratory search approaches. This paper proposes a specific way to integrate interactive visualization and personalized search and introduces an adaptive visualization based search system Adaptive VIBE that implements it. We tested the effectiveness of Adaptive VIBE and investigated its strengths and weaknesses by conducting a full-scale user study. The results show that Adaptive VIBE can improve the precision and the productivity of the personalized search system while helping users to discover more diverse sets of information.

Why did you pick that? Visualising relevance criteria in exploratory search

In this article, we present a set of approaches in analysing data gathered during experimentation with exploratory search systems and users’ acts of judging the relevance of the information retrieved by the system. We present three tools for quantitatively analysing encoded qualitative data: relevance-criteria profile, relevance-judgement complexity and session visualisation. Relevance-criteria profiles capture the prominence of each criterion usage with respect to the search sessions of individuals or selected user groups (e.g. groups defined by the users affiliations and/or level of research experience). Relevance-judgement complexity, on the other hand, reflects the number of criteria involved in a single judgment process. Finally, session visualisation brings these results together in a sequential representation of criteria usage and relevance judgements throughout a session, potentially allowing the researcher to quickly detect emerging patterns with respect to interactions, relevance criteria usage and complexity. The use of these tools is demonstrated using results from a pilot-user study that was conducted at the Robert Gordon University in 2008. We conclude by highlighting how these tools might be used to support the improvement of end-user services in digital libraries.

Exploratory Search: Beyond the Query-Response Paradigm

As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model support d by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world. Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks

Model-driven formative evaluation of exploratory search: A study under a sensemaking framework

The evaluation of exploratory search relies on the ongoing paradigm shift from focusing on the search algorithm to focusing on the interactive process. This paper proposes a model-driven formative evaluation approach, in which the goal is not the evaluation of a specific system, per se, but the exploration of new design possibilities. This paper gives an example of this approach where a model of sensemaking was used to inform the evaluation of a basic exploratory search system(s) in the context of a sensemaking task. The model suggested that, rather than just looking at simple search performance measures, we should examine closely the interwoven, interactive processes of both representation construction and information seeking. Participants were asked to make sense of an unfamiliar topic using an augmented query-based search system. The processes of representation construction and information seeking were captured and analyzed using data from experiment notes, interviews, and a system log. The data analysis revealed users’ sources of ideas for structuring representations and a tightly coupled relationship between search and representation construction in their exploratory searches. For example, users strategically used search to find useful structure ideas instead of just accumulating information facts. Implications for improving current search systems and designing new systems are discussed.

Report on ACM SIGIR 2006 workshop on evaluating exploratory search systems

Exploratory search systems (ESS) are designed to help users move beyond simply finding information toward using that information to support learning, analysis, and decision-making. The evaluation of the interactive systems designed specifically to help exploratory searchers is a challenging area, worthy of further discussion in the research community. In this article we report on a workshop conducted in conjunction with the ACM SIGIR Conference in Seattle, USA, in August 2006. The workshop involved researchers, academics, and practitioners discussing the formative and summative evaluation of ESS.