Fuzzy Approaches to Extend Boolean Information Retrieval

An extended fuzzy linguistic approach to generalize boolean information retrieval

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.

Relevance feedback techniques have demonstrated to be a powerful means to improve the results obtained when a user submits a query to an information retrieval system as the world wide web search engines. These kinds of techniques modify the user original query taking into account the relevance judgements provided by him on the retrieved documents, making it more similar to those he judged as relevant. This way, the new generated query permits to get new relevant documents thus improving the retrieval process by increasing recall. However, although powerful relevance feedback techniques have been developed for the vector space information retrieval model and some of them have been translated to the classical Boolean model, there is a lack of these tools in more advanced and powerful information retrieval models such as the fuzzy one. In this contribution we introduce a relevance feedback process for extended Boolean (fuzzy) information retrieval systems based on a hybrid evolutionary algorithm combining simulated annealing and genetic programming components. The performance of the proposed technique will be compared with the only previous existing approach to perform this task, Kraft et al.'s method, showing how our proposal outperforms the latter in terms of accuracy and sometimes also in time consumption. Moreover, it will be showed how the adaptation of the retrieval threshold by the relevance feedback mechanism allows the system effectiveness to be increased.

Both phrases and Boolean queries have a long history in information retrieval, particularly in commercial systems. In previous work, Boolean queries have been used as a source of phrases for a statistical retrieval model, This work, like the majority of research on phrases, resulted in little improvement in retrieval effectiveness, In this paper, we describe an approach where phrases identified in natural language queries are used to build structured queries for a probabilistic retrieval model. Our results show that using phrases in this way can improve performance, and that phrases that are automatically extracted from a natural language query perform nearly as well as manually selected phrases.

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.

this paper provides an in-depth survey of challenges in the design of intelligent information retrieval systems, pointing out some similarities and differences in the core data mining and web based search operations. The procedures for evaluation of search engine performance and implicit feedback of the user with respect to a search result are studied with references to the different algorithms. We have proposed a novel neural satisfaction based feedback vector in contrast to the existing activity pattern based feedback as a future research direction in Intelligent IR. We addressed the select research work in the area of soft information retrieval using fuzzy sets, artificial neural networks, genetic algorithms and probabilistic information retrieval. As an instance of information retrieval, web mining is a set of operations to retrieve relevant documents from preprocessed, crawled and indexed web, and it can be categorized into more specialized tasks of web content mining, web structure mining and web usage mining. We have given a survey of the important reviews on topic of web mining and its associated tasks. On the basis of Identified challenges in information retrieval in general, and web mining in particular, we have concentrated on applicability of soft computing techniques and their hybrids in web mining, the performance related issues of select solutions, future of web mining and the next generation user's expectations from a search engine.

The composition of a painting consists of colors and shapes, and is explained with impression words and location of objects. These cause our impression or feeling. Especially from the viewpoint of KANSEI information retrieval, the data structure should be designed to be consistent with the attributes of color, shape, and impression words. Also, in the information retrieval of paintings, bibliographic data are important factors, as well as color and shape. The authors discuss the design and structure of keywords and key images as attributes of paintings for information retrieval. Each attribute of a painting is described as a fuzzy set and its membership function that represents the grade of the keywords or key images. We propose an estimation method for retrieved objects with the membership function. Furthermore, the information retrieval of paintings is formulated based on fuzzy set theory as an extension of the traditional crisp model.

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

Information retrieval is acquiring particular information from large resources and presenting it according to the user’s need. The incredible increase in information resources on the Internet formulates the information retrieval procedure, a monotonous and complicated task for users. Due to over access of information, better methodology is required to retrieve the most appropriate information from different sources. The most important information retrieval methods include the probabilistic, fuzzy set, vector space, and boolean models. Each of these models usually are used for evaluating the connection between the question and the retrievable documents. These methods are based on the keyword and use lists of keywords to evaluate the information material. In this paper, we present a survey of these models so that their working methodology and limitations are discussed. This is an important understanding because it makes possible to select an information retrieval technique based on the basic requirements. The survey results showed that the existing model for knowledge recovery is somewhere short of what was planned. We have also discussed different areas of IR application where these models could be used.

Not many XML information retrieval (IR) systems exist that allow easy addition of and switching between different IR models. Especially for the scientific environment where building a system takes a lot of time and keeps researchers away from the real work, i.e., investigating what is the most effective IR model, a platform that would provide this functionality would be ideal. For this reason we developed such an XML IR system. It is centered around a logical algebra, named score region algebra (SRA), that enables transparent specification of IR models for XML databases (see [1] for more details). The transparency is achieved by a possibility to instantiate various retrieval models, using abstract score functions within algebra operators, while logical query plan and operator definitions remain unchanged. Our algebra operators model three important aspects of XML IR: element relevance score computation, element score propagation, and element score combination. To implement a new IR model, one only needs to provide definitions for these abstract function classes. To illustrate the usefulness of our algebra our demo system supports several, well known IR scoring models (e.g., Language Models, Okapi, and tf.idf), combined with different score propagation and combination functions. The user can select which model to use at run time. Following good practice in database systems design, our prototype system has a typical three-layered architecture. (1) The conceptual layer takes a NEXI [3] query expression as input, e.g.,

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.

Gradual concepts abound in many web-related domains, ranging from the notion of relevance in information retrieval, to the strength of connection in online social networks. As a result, fuzzy set theory is often a natural choice in implementing web systems. In this chapter, we give an overview of applications of fuzzy set theory in this area, focusing in particular on information retrieval, the semantic web, and recommender systems. In each case, we contrast fuzzy methods with other approaches, analyzing why and how the ideas of fuzzy set theory may be beneficial.KeywordsFuzzy LogicInformation RetrievalRecommender SystemDescription LogicMembership DegreeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Information Retrieval meets Large Language Models: A strategic report from Chinese IR community

Recent work have shown the potential of collaboration for solving complex or exploratory search tasks allowing to achieve synergic effects with respect to individual search, which is the prevalent information retrieval (IR) setting this last decade. This interactive multiuser context gives rise to several challenges in IR. One main challenge relies on the adaptation of IR techniques or models [8] in order to build algo-rithmic supports of collaboration distributing documents among users. The second challenge is related to the design of Collaborative Information Retrieval (CIR) models and their effectiveness evaluation since individual IR frameworks and measures do not totally fit with the collaboration paradigms. In this tutorial, we address the second challenge and present first a general overview of collaborative search introducing the main underlying notions. Then, we focus on related work dealing with collaborative ranking models and their effectiveness evaluation. Our primary objective is to introduce these notions by highlighting how and why they should be different from individual IR in order to give participants the main clues for investigating new research directions in this domain with a deep understanding of current CIR work.

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.