Classical and Probabilistic Information Retrieval Techniques: An Audit

The vector space model of information retrieval is one of the classical and widely applied retrieval models. Paradoxically, it has been characterized by a discrepancy between its formal framework and implementable form. The underlying concepts of the vector space model are mathematical terms: linear space, vector, and inner product. However, in the vector space model, the mathematical meaning of these concepts is not preserved. They are used as mere computational constructs or metaphors. Thus, the vector space model actually does not follow formally from the mathematical concepts on which it has been claimed to rest. This problem has been recognized for more than two decades, but no proper solution has emerged so far. The present article proposes a solution to this problem. First, the concept of retrieval is defined based on the mathematical measure theory. Then, retrieval is particularized using fuzzy set theory. As a result, the retrieval function is conceived as the cardinality of the intersection of two fuzzy sets. This view makes it possible to build a connection to linear spaces. It is shown that the classical and the generalized vector space models, as well as the latent semantic indexing model, gain a correct formal background with which they are consistent. At the same time it becomes clear that the inner product is not a necessary ingredient of the vector space model, and hence of Information Retrieval (IR). The Principle of Object Invariance is introduced to handle this situation. Moreover, this view makes it possible to consistently formulate new retrieval methods: in linear space with general basis, entropy‐based, and probability‐based. It is also shown that Information Retrieval may be viewed as integral calculus, and thus it gains a very compact and elegant mathematical way of writing. Also, Information Retrieval may thus be conceived as an application of mathematical measure theory.

This study presents an overview of the research aimed at optimizing library information retrieval through the utilization of the Vector Space Model (VSM) method in a computer science context. Libraries, as publicly financed collections, provide extensive knowledge resources, eliminating the need for individual book purchases. However, the challenge lies in efficiently navigating the expanding library collections. To tackle this issue, the study employs information retrieval techniques, particularly the VSM method, which assesses term similarity by assigning weights to terms, enabling document and query representation as vectors. The relevance between documents and queries is measured through vector similarity. This approach, integrated with indexing, streamlines collection retrieval in libraries. Employing the Waterfall model for system development, the research outlines phases like analysis, design, coding, testing, and implementation. While effective, the model's rigidity in accommodating evolving requirements poses limitations. The VSM method's numerical representation of text documents facilitates precise similarity calculations, supported by TF-IDF values indicating term importance in documents relative to the corpus. The study further extends to system design using UML diagrams and a visitor interface, integrating VSM for efficient search functionality. Black-box testing confirms the robustness of the system components and interfaces. Overall, this research presents a systematic approach to enhance information retrieval in libraries, emphasizing the VSM's pivotal role in optimizing document searches within expansive collections.

QuantNet is an integrated web-based environment consisting of different types of statistics-related documents and program codes. Its goal is creating reproducibility and offering a platform for sharing validated knowledge native to the social web. To increase the information retrieval (IR) efficiency there is a need for incorporating semantic information. Three text mining models will be examined: vector space model (VSM), generalized VSM (GVSM) and latent semantic analysis (LSA). The LSA has been successfully used for IR purposes as a technique for capturing semantic relations between terms and inserting them into the similarity measure between documents. Our results show that different model configurations allow adapted similarity-based document clustering and knowledge discovery. In particular, different LSA configurations together with hierarchical clustering reveal good results under M3 evaluation. QuantNet and the corresponding Data-Driven Documents (D3) based visualization can be found and applied under quantlet. The driving technology behind it is Q3-D3-LSA, which is the combination of “GitHub API based QuantNet Mining infrastructure in R”, LSA and D3 implementation.

We study the complexity of query satisfiability and entailment for the Boolean information retrieval models WP and AWV using techniques from propositional logic and computational complexity. WP and AWV can be used to represent and query textual information under the Boolean model using the concept of attribute with values of type text, the concept of word, and word proximity constraints. Variations of WP and AWP are in use in most deployed digital libraries using the Boolean model, text extenders for relational database systems (e.g., Oracle 10g), search engines, and P2P systems for information retrieval and filtering

This paper reviews the concept and goal of Information Retrieval Systems (IRSs). It also explains the synonymous concepts in Information Retrieval (IR)which include such terms as: imprecision, vagueness, uncertainty, and inconsistency. Current trends in IRSs are discussed. Fuzzy Set Theory, Fuzzy Retrieval Modelsare reviewed. The paper also discusses extensions of Fuzzy Boolean Retrieval Models including Fuzzy techniques for documents’ indexingandFlexible query languages. Fuzzy associative mechanisms were identified to include:(1)fuzzy pseudothesauri and fuzzy ontologies which can be used to contextualize the search by expanding the set of index terms of documents;(2)an alternative use of fuzzy pseudothesarui and fuzzy ontologies is to expand the query with related terms by taking into account their varying importance of an additional termand (3)fuzzy clustering techniques, where each document can be placed within several clusters with a given strength of belonging to each cluster, can be used to expand the set of the documents retrieved in response to a query.The paper concludesby recommending that in an electronic library environment, the librarians and information scientists should acquaint themselves with these terms in order to be more equipped in helping library users retrieve online documents relevant to their information needs.

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).

Ranking is likely the most important process of an information retrieval (IR) system that will be used to evaluate and measure the effectiveness of an IR system. This paper aims to produce the implementation of fuzzy logic controller of Mamdani-type fuzzy inference system for defining the ranking function by using the BM25 model in the Malay IR system that also includes the Malay Stemmer. The result of the ranking function then will be compared to the result of vector space model that is also applied in Malay IR system and be evaluated using relevant document by the hadith expert. The results showed that FBMIR has slightly outperformed vector space model on three topic set of query results such as 'Iman', 'Ilmu' and 'Wuduk' on the precision at rank 10 and the percentage of no relevant document in the top ten retrieved measures.

In 2007, the first BCS-IRSG Symposium on Future Directions in Information Access (FDIA) was established to provide a forum for early career researchers to present, share and discuss research, which is at a more formative or tentative stage. The symposium was run in conjunction with the 6th European Summer School in Information Retrieval (ESSIR) which was held in Glasgow. The second symposium was held in London, UK in September, 2008 collocated with Search Solutions, while the third symposium was again collocated with ESSIR in Padua, Italy. Now in its fourth year, the 2011 Future Directions in Information Access Symposium was held as part of the 8th European Summer School in Information Retrieval. Symposium aims The objectives of the Symposium on the Future Directions in Information Access are: to provide an accessible forum for early researchers (particularly PhD students, and researchers new to the field) to share and discuss their research; to create and foster formative and tentative research ideas; to encourage discussion and debate about new future directions. Sponsors: BCS-IRSG (Information Retrieval Specialist Group) Centre for Next Generation Localisation (CNGL) European Summer School in Information Retrieval (ESSIR) These proceedings contain papers and posters presented at the 4th Symposium on Future Directions in Information Access, which was held in Koblenz, Germany on 31 August during the 2011 European Summer School in Information Retrieval (ESSIR). This year’s programme featured an inspiring keynote by David Elsweiler entitled ‘Working at (and pushing) the boundaries of IR’, while Liadh Kelly presented ‘Nine things I wish I knew about doing a PhD’, before four of the students presented their work. To create an interactive and dynamic session, this year we introduced a speed dating session where the participants paired up for two minutes each and brainstormed on future directions for the field. After each speed date, the couples came up with a new topic that combined their research areas. An overview of the emerging topics can be seen in the tag cloud. In the final session of the day all the participants of the summer school presented posters and further discussed their research in a friendly and fun atmosphere. The organisers would like to thank: the members of the programme committee for all their hard work and effort in providing excellent feedback and reviews; David Elsweiler for delivering a stimulating and thought-provoking keynote on how research in other fields influences IR research, how exploring the boundaries of IR research opens up new problems, and how we should not be afraid to push the boundaries and tackle these new challenges. We would also like to thank ESSIR 2011 and the University of Koblenz for hosting the event, and in particular Ruth Ehrenestein for her assistance in local organisation. We would like to extend our thanks to the Centre for Next Generation Localisation (CNGL), Ireland for their financial assistance and the BCS-IRSG for sponsoring the event.

It is an honour to present the program for the second ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2017), held in Oslo, Norway from March 7 to March 11, 2017. CHIIR offers the opportunity to present and discuss research on user-centered approachesto information retrieval, information access and information interaction, including studies of interactive systems, searcher behaviour, novel interaction paradigms, interface design, new evaluation methods, and a range of related areas. CHIIR is an interdisciplinary event in which researchers, students and practitioners working across several areas, including information retrieval, information science and human computer interaction, come together to think creatively about the challenges and opportunities afforded by digital information systems. Welcome to all the CHIIR-ists joining us this year! The CHIIR 2017 program consists of Full Papers (10 pages), Perspectives Papers (10 pages) and Short Papers (4 pages). We received a total of 112 submissions: 48 Full Papers, 9 Perspectives Papers and 55 Short Papers. Of these, we accepted 19 Full Papers (40%), 5 Perspectives Papers (56%) and 36 Short Papers (65%). We also accepted one Full Paper as a Short Paper. We were pleased to receive so many strong Perspectives Papers, which are meant to present novel ideas or insights, and we hope these will inspire some great discussions. Each Full and Perspectives paper is allocated a 30-minute presentation slot in the program. Short papers are presented as posters during a special evening session, beginning with a short boaster presentation. In addition to contributed papers, the CHIIR program features three keynote talks by prominent academic and industry researchers, representing the breadth of interests in the CHIIR community: Norbert Fuhr (University of Duisburg-Essen, Germany), Ann Blandford (University College London, UK) and Håkon Wium Lie (Opera Software, Norway). In addition to these speakers and the main conference program, CHIIR features a Doctoral Consortium, two Tutorials, and three Workshops. We would like to acknowledge the excellent work of the Organizing Committee, including the Doctoral Consortium Chair, Katriina Byström (Oslo and Akershus University College of Applied Sciences, Norway); the Workshops and Tutorials Chair, Preben Hansen (Stockholm University, Sweden), and the Proceedings Chair, Sampath Jayarathna (California State Polytechnic University Pomona, USA). The paper reviewing and selection process was enabled by an outstanding 12 member Senior Program Committee and a diverse and international general Program Committee consisting of 77 members. Our thanks and the credits for creating a high quality technical program go to them.

The Lampung University repository system is a repository of data related to study, community service, and other scientific works, currently has 37242 documents accessible through repository.lppm.unila.ac.id. Despite the amount of data, its optimal use as an information retrieval remains unrealized, hindering the effective promotion of Lampung University's scientific publication excellence. Recognizing the limitations of existing information retrieval systems that are limited to specific methods for topic identification through clustering, this study aims to develop a retrieval system for Lampung University's repository using Vector Space Model (VSM), K-Means and Latent Semantic Analysis (LSA) that generates clusters and study expertise at the level of study program, faculty and Lampung University. The methodology includes data collection, preprocessing, modeling, evaluation and system deployment. The results show that the number of clusters obtained for the university level is 7 clusters, for the faculty level are 6, 7, 8 and 10 clusters, and for the program level are 3 to 5 clusters. In addition, the finding topic identification indicate that the expertise topics at Lampung University, which are agriculture, soil, education, plants, learning, society, Lampung. This study contributes to optimizing the information retrieval system, promoting academic excellence, and advancing the understanding of study expertise at Lampung University.

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

Modern cities are increasingly becoming smart where a digital knowledge infrastructure is deployed by local authorities (e.g. City councils and municipalities) to better serve the information needs of their citizens, and to ensure the sustainability and efficient use of power and resources. This knowledge infrastructure consists of a wide range of systems from lowlevel physical sensors to advanced sensing devices through social sensors. The i-ASC 2014 workshop was the first international event, within the Information Retrieval (IR) community, that is dedicated to research on smart/future cities. In particular, the workshop was a venue for research on digesting the city's data streams and knowledge databases in order to serve the information needs of citizens and support decision making for local authorities. Possible use cases include helping tourists to find interesting places to go or activities to do while visiting a city, or assisting journalists in reporting local incidents. Indeed, the workshop was intended to foster the development of new information access and retrieval models that can harness effectively and efficiently the large number of heterogeneous big data streams in a city to provide a new generation of information services. The workshop was well attended, where more than 45 participants were officially registered. It featured two keynote talks from industry (IBM andWaag Society) and two invited talks from academia (Pisa and Edinburgh). In addition, seven refereed papers were presented before breakout groups considered questions and issues identified from a panel discussion.

Information retrieval (IR) approaches have proven useful in recovering traceability links between free-text documentation and source code. IR-based traceability recovery approaches produce ranked lists of traceability links between pieces of documentation and source code. These traceability links are then pruned using various strategies and, finally, validated by human experts. In this paper we propose two contributions to improve the precision and recall of traceability links and, thus, reduces the required human experts' manual validation effort. First, we propose a novel approach, Trust race, inspired by Web trust models to improve the precision and recall of traceability links: Trust race uses intractability recovery approach to obtain a set of traceability links, which rankings are then re-evaluated using a set of other traceability recovery approaches. Second, we propose a novel traceability recovery approach, His trace, to identify traceability links between requirements and source code through CVS/SVN change logs using a Vector Space Model (VSM). We combine a traditional recovery traceability approach with His trace to build Trust race in which we use Histraceas one expert adding knowledge to the traceability links extracttedfrom CVS/SVN change logs. We apply Trustrace on two case studies to compare its traceability links with those recovered using only the VSM-based approach, in terms of precision and recall. We show that Trustrace improves with statistical significance the precision of the traceability links while also improving recall but without statistical significance.

This chapter provides a perspective on a problem-based learning (PBL) approach to teaching information retrieval (IR) theory, techniques, and practice at under- and postgraduate levels. The educational philosophy of PBL is outlined, and its pertinence to addressing the challenges of teaching IR in terms of both the diverse nature of the subject and also of the students is discussed. It is argued that teaching IR as a solution to a problem (access to information) and as a problem (how can we organize information in IR systems so it can be accessed) provides an engaging and challenging way to teach it to a range of students. It also draws connections between IR theory and techniques and relates IR to other disciplines through showing how similar problems are faced in other subjects. Some specific examples of teaching exercises based on PBL are provided with a discussion and reflection on their effectiveness. Finally assessment and feedback from students are discussed.

In order to effectively handle the real-valued data sets in practice, it is valuable from theoretical and practical aspects to combine fuzzy rough set and variable precision rough set so that a powerful tool can be developed. That is, the model of fuzzy variable precision rough set, which not only can handle numerical data but also is less sensitive to misclassification and perturbation,In this paper, we propose a new variable precision rough fuzzy set by introducing the variable precision parameter to generalized rough fuzzy set, i.e., the variable precision rough fuzzy set based on general relation. We, respectively, define the variable precision rough lower and upper approximations of any fuzzy set and it level set with variable precision parameter by constructive approach. Also, we present the properties of the proposed model in detail. Meanwhile, we establish the relationship between the variable precision rough approximation of a fuzzy set and the rough approximation of the level set for a fuzzy set. Furthermore, we give a new approach to uncertainty measure for variable precision rough fuzzy set established in this paper in order to overcome the limitations of the traditional methods. Finally, some numerical example are used to illuminate the validity of the conclusions given in this paper.