Fuzzy Set Techniques in Information Retrieval

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.

Application of configurational approach and fuzzy set theory on social and behavioral science variable models needs more attention. Few social science researches have employed configurational methods of research to empirically investigate a research question. On the front of exploring unique and innovative ways of investigation, this paper discusses the application of fuzzy set theory in social sciences. The study primarily takes a model of emotional labor and employee well‑being as a case example to depict how configurational models are developed. The study examines emotional labor and well‑being, not as predictor or criterion variables, but rather as conditions and outcome. The study also distinguishes between correlational and configurational method of research and adopts a descriptive approach by taking five research papers that examine social and behavioral science constructs from a configurational approach. Scarcity of literature in the crossover between the mathematical models and social sciences, particularly for the respective variables, call for a study that can clarify the initial queries surrounding fuzzy set models and pave the way for future empirical studies. The research concludes that utilizing mathematical models like fuzzy set theory, configuration and qualitative comparative analysis in social sciences can break monotony and bring newness in approaching as well as addressing a research query. This paper draws attention towards exploring new avenues in research methodologies. Application of newer methods would not only increase the rigor and precision of research findings but also assist in approaching a research area from multiple angles thereby enhancing the research quality.

Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of documents and queries. Typically, good IR model should consider distinct but semantically similar words in the matching process. In this paper, we propose a method to incorporate word embedding (WE) semantic similarities into existing probabilistic IR models for Arabic in order to deal with term mismatch. Experiments are performed on the standard Arabic TREC collection using three neural word embedding models. The results show that extending the existing IR models improves significantly baseline bag-of-words models. Although the proposed extensions significantly outperform their baseline bag-of-words, the difference between the evaluated neural word embedding models is not statistically significant. Moreover, the overall comparison results show that our extensions significantly improve the Arabic WordNet based semantic indexing approach and three recent WE-based IR language models.

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.,

We report data fusion experiments carried out on the four best‐performing retrieval models from TREC 5. Three were conceptually/algorithmically very different from one another; one was algorithmically similar to one of the former. The objective of the test was to observe the performance of the 11 logical data fusion combinations compared to the performance of the four individual models and their intermediate fusions when following the principle of polyrepresentation. This principle is based on cognitive IR perspective (Ingwersen & Järvelin, 2005) and implies that each retrieval model is regarded as a representation of a unique interpretation of information retrieval (IR). It predicts that only fusions of very different, but equally good, IR models may outperform each constituent as well as their intermediate fusions. Two kinds of experiments were carried out. One tested restricted fusions, which entails that only the inner disjoint overlap documents between fused models are ranked. The second set of experiments was based on traditional data fusion methods. The experiments involved the 30 TREC 5 topics that contain more than 44 relevant documents. In all tests, the Borda and CombSUM scoring methods were used. Performance was measured by precision and recall, with document cutoff values (DCVs) at 100 and 15 documents, respectively. Results show that restricted fusions made of two, three, or four cognitively/algorithmically very different retrieval models perform significantly better than do the individual models at DCV100. At DCV15, however, the results of polyrepresentative fusion were less predictable. The traditional fusion method based on polyrepresentation principles demonstrates a clear picture of performance at both DCV levels and verifies the polyrepresentation predictions for data fusion in IR. Data fusion improves retrieval performance over their constituent IR models only if the models all are quite conceptually/algorithmically dissimilar and equally and well performing, in that order of importance.

Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information.

Assessment of the potential applicability of fuzzy set theory to accident progression event trees with phenomenological uncertainties

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.

A common limitation of many information retrieval (IR) models is that relevance scores are solely based on exact (i.e., syntactic) matching of words in queries and documents under the simple Bag-of-Words (BoW) representation. This not only leads to the well-known vocabulary mismatch problem, but also does not allow semantically related words to contribute to the relevance score. Recent advances in word embedding have shown that semantic representations for words can be efficiently learned by distributional models. A natural generalization is then to represent both queries and documents as Bag-of-Word-Embeddings (BoWE), which provides a better foundation for semantic matching than BoW. Based on this representation, we introduce a novel retrieval model by viewing the matching between queries and documents as a non-linear word transportation (NWT) problem. With this formulation, we define the capacity and profit of a transportation model designed for the IR task. We show that this transportation problem can be efficiently solved via pruning and indexing strategies. Experimental results on several representative benchmark datasets show that our model can outperform many state-of-the-art retrieval models as well as recently introduced word embedding-based models. We also conducted extensive experiments to analyze the effect of different settings on our semantic matching model.

Fuzzy multiset is applicable as a model of information retrieval because it has the mathematical structure that expresses the number and the degree of attribution of an element simultaneously. Ther...

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.

No abstract available.

Technology advances in the areas of image processing (IP) and information retrieval (IR) have evolved separately for a long time. However, successful content-based image retrieval systems require the integration of the two. There is an urgent need to develop integration mechanisms to link the image retrieval model to text retrieval model, such that the well established text retrieval techniques can be utilized. Approaches of converting image feature vectors (IF domain) to weighted-term vectors (IR domain) are proposed in this paper. Furthermore, the relevance feedback technique from the IR domain is used in content-based image retrieval to demonstrate the effectiveness of this conversion. Experimental results show that the image retrieval precision increases considerably by using the proposed integration approach.

Over the past decade, information retrieval has emerged as an active research area in the application of fuzzy set theory. Fuzzy information retrieval utilizes fuzzy sets to represent documents, membership degrees for query term relevance, fuzzy logical operators to define queries, and fuzzy compatibility measures to assess the retrieval status value of a document. This paper presents an overview of fuzzy relational databases and fuzzy information retrieval. A general description of the main components of fuzzy information retrieval are given: document representation, query representation, computer-aided query formulation, document retrieval status, and performance measures. Examples of areas currently being researched are provided. The relation between fuzzy information retrieval and fuzzy relational databases is examined.

BK-FIRM (Bandler-Kohout fuzzy information retrieval model) suggested by Kohout et al. (1984) has been expanded from Boolean information retrieval model. It uses the concept of fuzzy relation which represents relative containments between documents and terms, and it is able to retrieve documents in the way based not on morphology but on semantics, dissimilar to traditional information retrieval theories. It also has merits such that it builds thesaurus automatically and it provides the system with fuzzy ranked documents. BK-FIRM, however, is not able to accept so many domains because its operations have very high time complexity. A-FIRM (advanced Bandler-Kohout fuzzy information retrieval model) improves time complexity of BK-FIRM by using reduced set which is the subset of the term set. In this paper, we suggest A-FIRM which improves time complexity of BK-FIRM's operations.