Introduction to Information Access and Usability Minitrack

There is a growing need to access and retrieve relevant information across media, across languages and across modalities. The research context concerning information access and retrieval aims at modelling, designing and implementing systems able to provide a fast and effective content-based access to a large amount of multimedia information. The aim of such systems is to estimate the relevance of documents to a user information need. This is a very hard and complex task due to several distinct reasons that a large volume of research has attempted to analyse and tackle. Information Retrieval (IR) can be considered as the first historical research area aimed at defining systems for the automatic access to huge amounts of information (together with DBMSs whose main aim is to manage and access huge amounts of data).

This research is motivated by the lack of empirical studies that discuss the existence of libraries as a fulfillment of information needs, especially in the secretariat library of the West Sumatra provincial DPRD. The purpose of this study is to describe the extent of the relationship between the Existence of Libraries and the Information Needs of Users. This type of research is quantitative with a descriptive method. The population in this study were all registered users at the Secretariat Library of the West Sumatra Provincial DPRD. Samples were taken using simple random sampling, and data were collected through observation, interviews, and questionnaires. The results of the analysis showed that the existence of the library was in the good category with an average score of 3.01. The information needs of users were in the good category with an average score of 2.93. Based on the Pearson correlation test, it showed a positive relationship between the existence of the library and the information needs of users of 0.643, including in the interval scale of 0.60-0.799. This shows that the existence of the library has a significant relationship with the information needs of users. These results emphasize that the existence of libraries has a direct influence on the information needs of users, especially institutional libraries where users are members of the institution who have jobs in certain categories.

The paper introduces a model for interactive image retrieval utilising the geometrical framework of information retrieval (IR). We tackle the problem of image retrieval based on an expressive user information need in form of a textual-visual query, where a user is attempting to find an image similar to the picture in their mind during querying. The user information need is expressed using guided visual feedback based on Information Foraging which lets the user perception embed within the model via semantic Hilbert space (SHS). This framework is based on the mathematical formalism of quantum probabilities and aims to understand the relationship between user textual and image input, where the image in the input is considered a form of visual feedback. We propose SHS, a quantum-inspired approach where the textual-visual query is regarded analogously to a physical system that allows for modelling different system states and their dynamic changes thereof based on observations (such as queries, relevance judgements). We will be able to learn the input multimodal representation and relationships between textual-image queries for retrieving images. Our experiments are conducted on the MIT States and Fashion200k datasets that demonstrate the effectiveness of finding particular images autocratically when the user inputs are semantically expressive.

In the past decade, the prosperity of the World Wide Web has led to fast explosion of information, and there is a long-standing demand on how to access such a huge volume of information effectively and efficiently. Information Retrieval (IR) aims to tackle the challenge by exploring approaches to obtain relevant information items (e.g., documents) relevant to a given information need (e.g., query) from a huge collection of textual data (e.g., the Web). Named entity (e.g., person, location, product, event, organization) is a type of term compound widely existing in textual data. Recent advances in Information Extraction make it possible to extract entities from large volume of free text efficiently, and the research community are actively exploring whether entities would contribute to the retrieval effectiveness. In this thesis, we investigate how to leverage entities to improve retrieval in several directions. We start with finding entities with certain semantic relation, which aims at retrieving entities and their associated attributes to meet user's information need directly. This is different from traditional search paradigm in which only documents are retrieved. Entity retrieval is performed by first retrieving a list of documents and then extracting entities from those documents. We propose a novel probabilistic framework which leverages supporting documents as bridge to model the relevance between query and entities and rank entities accordingly. On the other side, we also explore how to leverage entities to improve effectiveness of ad hoc document retrieval in two directions. The first direction is entity-centric query expansion. We find related entities of query, and perform query expansion using the names and relations of related entities. Significant improvements over several state-of-the-art feedback models could be observed on multiple data collections. Besides, we explore another direction: entity-centric relevance modeling. We propose a novel retrieval approach, i.e., Latent Entity Space (LES), which models the relevance by leveraging entity profiles to represent semantic content of documents and queries. Experimental results over several TREC collections show that LES is effective on capturing latent semantic content and can significantly improve the search accuracy of several state-of-the-art retrieval models for entity-bearing queries. This thesis presents a series of research efforts on entity centric information retrieval in several directions, and reveals promising potential of entities on improving the retrieval effectiveness. With the fast curation of high-quality knowledge base, more information about entities could be easily accessed and integrated into retrieval models. We hope our work could serve as guideline for future work on leveraging entities to improve information retrieval in more applications.

Do PCGs need information officers?

Query performance prediction methods are usually applied to estimate the retrieval effectiveness of queries, where the evaluation is largely system sided. However, little work has been conducted to understand query performance prediction from the user's perspective. The question we consider is, whether the predictions of query performance that systems make are in line with the predictions that users make. To this aim, we compare the performance ratings users assign to queries with the performance scores estimated by a range of pre-retrieval and post-retrieval query performance predictors. Two studies are presented that explore the relationship between user ratings and system predictions on two levels: (i) the topic level, and, (ii) the query suggestions level. It is shown that when predicting the performance of query suggestions, user ratings were mostly uncorrelated with system predictions. At the topic level though, where a single query is judged for each information need, we observed moderate correlations between user ratings and a subset of system predictions. As query performance prediction methods are often based on intuitions of how users might rate queries, these findings suggest that such methods are not representative of how users actually rate query suggestions and topics. This motivates further research into understanding the rating process engaged by users, and developing models of query performance prediction in order to bridge the divide between systems and users.

Managers’ perception of information management and the role of information and knowledge managers: The Malaysian perspectives

We present an approach to develop a Question Answering (QA) system over cooking recipes that makes use of Cooking Ontology management. QA systems are designed to satisfy the user’s specific information need whereas ontology is the conceptualization of knowledge and it exhibits the hierarchical structure. The system is an Information retrieval (IR) based system where the various tasks to be handled like question classification, answer pattern recognition, indexing, final answer generation. Our proposed QA System use Apache Lucene for document retrieval. All cooking related documents are indexed using Apache Lucene. Stop words are removed from each cooking related question and formed the query words which are identified to retrieve the most relevant document using Lucene. Relevant paragraphs are selected from the retrieved documents based on the tf-idf of the matching query words along with n-gram overlap of the paragraph with the original question. This paper also presents a way to develop an ontology model in such a way that the queries can be processed with the help of the ontology knowledge base and generate the exact answer.

The Information Retrieval (IR) problem can be described as the quest to find the set of relevant information objects corresponding to a given information need, which is represented by a request. IR begins with a user who wishes to satisfy an information need. The information need is typically formulated in the form of a request, denoted by q. The intention is that the request q be as good as possible description of the information need N. The information being queried is modelled as a set of information carriers, or documents. An often used premise in IR is the following: if a given document d is about the request q, then there is a high likelihood that d is relevant with respect to the associated information need. Thus, the IR problem is reduced to deciding the aboutness relation between documents and requests. Many IR models have been developed, and there is a wide variation in how they determine aboutness. A common approach is to equate aboutness with overlap, meaning if there is sufficient overlap between the document and the query, the document is deemed to be about the query. The degree of overlap determined by the retrieval model is usually expressed as a real number between zero (no overlap) and unity (complete overlap). In most models, “sufficient” overlap is not prescribed by the model but by experimentation within a given IR setting. Such experimentation may lead to the conclusion, say, that sufficient overlap is apparent when the overlap measure is greater than α, 0 < α ≤ 1. The value α varies from setting to setting.

It is contended that knowledge management is directed towards finding out how and why information users think, what they know about what they know, the knowledge and attitudes they have and the decisions they make when they interact with others. At the heart lies the mutation of information into knowledge, a process best understood through seeing, knowing and information retrieval as features common to cognitive psychology and information management. The knowledge we have of knowledge, and changes to knowledge, can be monitored in negotiations like knowledge interviews for trainees. Such knowledge and belief systems can also be translated into managerial strategies, both qualitative, as when we emphasise value and benefit in the marketing approach to information, and quantitative, as when we devise ways of assessing probabilities with which desired outcomes will occur. Knowledge management is as much the management of meaning as management of entities and people, for in meaning lies the key to our understanding of what we decide to do as information managers. It is a multi‐disciplinary field offering a semantics and pragmatics for the evaluating and self‐evaluating manager.

Personalized Web search is an effective means of providing specific results to different users when they submit the same query. How to obtain user's real-time information need is a key issue in personalized search. Existing methods concentrate more on the building user profile based on Web pages/documents which affects the efficiency of search engine. In addition, dynamics of user profile is often ignored. To address this problem, we introduce an approach that captures the user context to accurately provide preferences of users for effective personalized search in this paper. First, short-term query context is generated from Web-snippets to play a role of semantic background of user's search behavior, identifying related concepts of the query. Second, user context snap is built based on query context according to user's interactive search behavior. Finally, evolution of user context is considered by introducing forgetting factor to merge the independent user context snap in a user session. The experimental results fully demonstrate that our approach can successfully build user context according to individual user information need.

As the Web has become one of the biggest repository of knowledge easily accessible for everyone, information retrieval has stopped to be an exclusive field of information sciences specialists, and it has become a field related with any person. Although users do not have to know information retrieval techniques, the proposal of this work is to improve query results by using a that implements these techniques. For this, a semantic refiner is proposed. This refiner acts as a specialist in information sciences and prepares, using linguistic resources, an appropriate search strategy that represents the user's information need. The semantic refinement consists on three steps. First, it guides the user for sense disambiguation of the words submitted by him. Then, it allows the user to select concepts hierarchically related to reduce the amount of documents to retrieve. Finally, it expands semantically concepts to increase the amount of documents to be retrieved. A prototype is implemented using WordNet as linguistic resource, and experimental results are analyzed.

The explosive growth of information on the web demands effective intelligent search and filtering methods. Consequently, techniques have been developed that extract conceptual information from the document and use the conceptual information in the user profile to form part of the user's information intent from his/her query. In a similar vein, we build the profile without user interaction, automatically monitoring the user's browsing habits. These profiles, in turn, are used to automatically learn the semantic context of user's information need. These sets of categories can serve as a context to disambiguate the words in the user's query. In this paper, we present a framework for assisting the user in one of the most difficult information retrieval tasks, i.e., that of formulating an effective search query. Our experimental results show that implicit measurements of user interests, combined with the semantic knowledge embedded in a concept hierarchy, can be used effectively to infer the user context and to improve the results of information retrieval.

As a method for information retrieval (IR) on the Web, search engines have become the tool of choice for most online users. However, despite the variety of next generation approaches to Web search we have seen recently (e.g. [1,2]), the problems of information overload, vague user queries and spam still have the effect that many search sessions end in user frustration. Generally search engines are criticised for returning result lists that have low precision, where the user's information need is not satisfied by any of the returned result pages.

The main goal of information retrieval (IR) is to satisfy information need (IN). IN refers to a complex concept: at the very initial state of the phenomenon (that is, at a visceral level), even the searcher may not be aware of its existence. Thus, despite advances in the past few decades in both the IR and relevant scientific communities, we do not fully understand how an IN emerges and how it is physically manifested. In this article we aim to inform a holistic view of the realization of IN using functional magnetic resonance imaging. We collected new data of brain activity of 24 participants while they formulated and stated a realization of IN in a Question Answering task, focusing on a distributed set of brain regions associated with activities related to IN, found in our previous study. Results of a functional connectivity analysis led us to propose a neuropsychological model of the realization of IN. Our model consists of three components: (a) a successful memory retrieval component, (b) an information flow regulation component, and (c) a high‐level perception component. We believe this study constitutes an important step in unraveling the nature of IN and how to better satisfy IN.