A Comparison of Methods and Techniques for Ontological Query Expansion

The internet has been inundated with an ocean of information, and hence, information retrieval systems are failing to provide optimal results to the user. In order to meet the challenge, query expansion techniques have emerged as a game-changer and are improving the results of information retrieval significantly. Of late, semantic query expansion techniques have attracted increased interest among researchers since these techniques offer more pertinent and practical results to the users. These allow the user to retrieve more meaningful and useful information from the web. Currently, few research works provide a comprehensive review on semantic query expansion; usually, they cannot provide a full view on recent advances, diversified data application, and practical challenges. Therefore, it is imperative to go deep in review in order to explain these advances and assist researchers with concrete insights for future development. This article represents the comprehensive review of the query expansion methods, with a particular emphasis on semantic approaches. It overviews the recent frameworks that have been developed within a period of 2015-2024 and reviews the limitations of each approach. Further, it discusses challenges that are inherent in the semantic query expansion field and identifies some future research directions. This article emphasizes that the linguistic approach is the most effective and flexible direction for researchers to follow, while the ontology approach better suits domain-specific search applications. This, in turn, means that development of the ontology field may further open new perspectives for semantic query expansion. Moreover, by employing artificial intelligence (AI) and making most of the query context without relying on user intervention, improvements toward the optimal expanded query can be achieved.

The aim of this paper is to retrieve the most relevant expansion words for expanding the initial query of the user in order to enhance the outcomes of web search results. Query expansion plays a major role in reformulating a user’s initial query to a one more pertinent to the user’s intended meaning. The reformulated query is then used to obtain more appropriate outcomes from a large amount of information on the web. The proposed semantic query expansion technique uses Wikipedia and WordNet as data sources. Wikipedia is taken as a base for all query expansions because it is one of the most diversified and relevant databases available on the web. To further improve the proposed query expansion technique,WordNet—a lexical database—is used as the as another data source because the synonyms (synsets) of the query term provided by it can be quite useful for query expansion. The proposed expansion technique successfully combines the two data sources to retrieve the most relevant expansion terms from the data sources in response to the user’s original query. The proposed work has been divided into four phases: (1) extraction of relevant words from Wikipedia (2) extraction of relevant words from WordNet (3) merging of the expansion terms obtained from Wikipedia and WordNet, and (4) query formulation by combining the expansion terms using Boolean operators. This reformulated query is then fired on the web to find the desired result. The Experimental result shows a significant improvement in information retrieval using query expansion.

Purpose The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords. Design/methodology/approach In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model. Findings The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce. Originality/value This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.

This paper presents a framework which combines data and text retrieval techniques to exercise and evaluate ontology based query expansions. We prepare by using linguistic techniques to identify query and document concepts, locating them in a ontologically defined semantic space. Expansions originate from the identified query concepts, with success determined by matching in the relevant document set. We identify three orthogonal dimensions that can affect query expansion success; relationship source, success measure technique, and query expansion technique. Expansion technique is further divided into six different categories: simple pruning, complex probability, voting, directional, semantic propagation, and multiple source concept. We describe each technique and show examples where they would be useful. The system architecture used facilitates plugging in of various expansion and evaluation routines, and flowing results from one method to the next. The system is useful for microanalysis of query expansion, discovering which components of ontological derived knowledge most influence query expansion success. In this work, we apply our framework to the medical domain.

A digital library is a type of information retrieval (IR) system. The existing information retrieval methodologies generally have problems on keyword-searching. We proposed a model to solve the problem by using concept-based approach (ontology) and metadata case base. This model consists of identifying domain concepts in user’s query and applying expansion to them. The system aims at contributing to an improved relevance of results retrieved from digital libraries by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to import the concept of ontology, making use of its advantage of abundant semantics and standard concept. Domain specific ontology can be used to improve information retrieval from traditional level based on keyword to the lay based on knowledge (or concept) and change the process of retrieval from traditional keyword matching to semantics matching. One approach is query expansion techniques using domain ontology and the other would be introducing a case based similarity measure for metadata information retrieval using Case Based Reasoning (CBR) approach. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.

In semantic information retrieval, we first need to build domain ontology and second, we need to convert the users’ search keywords into a standard query such as SPARQL. In this paper, we propose a method that can automatically convert the users’ search keywords into the SPARQL queries. Furthermore, our method can ensure effective performance in a specific domain such as law. Our method constructs the keyword history ontology by associating each keyword with a series of information when there are multiple keywords. The constructed ontology will convert keyword history ontology into SPARQL query. The automatic transformation method of SPARQL query proposed in the paper is converted into the query statement that is deemed the most appropriate by the user’s intended keywords. Our study is based on the existing legal ontology constructions that supplement and reconstruct schema and use it as experiment. In addition, design and implementation of a semantic search tool based on legal domain and conduct experiments. Based on the method proposed in this paper, the semantic information retrieval based on the keyword is made possible in a legal domain. And, such a method can be applied to the other domains.

This thesis investigates the question whether and how ontologies such as the ones currently evolving in the Semantic Web can serve as knowledge structures for the generation of query expansion terms in information retrieval systems. This issue is examined using a specific example domain, namely educational research. Initial results support the already well-researched finding that query expansion can increase recall. Subsequent experiments will focus on comparing the effectiveness of thesaurus and ontology-based query expansion, on identifying ontological relationships which are especially useful for the generation of query expansion terms in the domain of educational research, and on evaluating the usefulness of different ontological relationships as expansion terms for different types of queries, as well as for different query expansion modes.

A semantic framework for enhancing pseudo-relevance feedback with soft negative sampling and contrastive learning

This paper presents a method of improving the accuracy of document image retrieval focusing on the application to a camera-pen system. In a camera-pen system, document image retrieval is employed for locating the pen-tip position on a page. A serious problem is that since the camera is mounted close to the pen-tip, the camera captures only a tiny portion of the page and the resultant image is under severe perspective distortion, resulting in lowering the retrieval accuracy. To solve this problem, we propose new geometrically invariant features as well as expansion techniques which increase the number of index features of either the database or the query images. From the experimental results, it has been found that the query expansion technique with features by combining affine and perspective invariants allows us the best performance that improves the accuracy of a baseline method more than 27%.

Vocabulary mismatch in Information retrieval can be solved by Query Expansion (QE) techniques. Relevance feedback is a prominent solution to improve recall of retrieval system. Sometimes user may be reluctant and novice in providing feedback to improve the retrieval performance. Pseudo Relevance Feedback (PRF) automates the process. PRF treats top ranked resultant items are relevant and uses them to expand the query, which is not always correct. PRF by local analysis does not give guarantee to feedback positive terms to the system. Use of global analysis to capture the positive feedback is a regular practice in information retrieval process. This paper addresses the limitations of local analysis and global analysis alone by a novel approach that integrates both PRF and WordNet to select good expansion terms. The proposed solution filters the expansion terms and optimizes the expanded query. The proposed work is carried out on a huge Telugu text corpus collected from Wikipedia and other Telugu daily news portals.

A new approach for query expansion using Wikipedia and WordNet

This paper describes and evaluates the use of Geographical Knowledge Re-Ranking, Linguistic Processing, and Query Expansion techniques to improve Geographical Information Retrieval effectiveness. Geographical Knowledge Re-Ranking is performed with Geographical Gazetteers and conservative Toponym Disambiguation techniques that boost the ranking of the geographically relevant documents retrieved by standard state-of-the-art Information Retrieval algorithms. Linguistic Processing is performed in two ways: 1) Part-of-Speech tagging and Named Entity Recognition and Classification are applied to analyze the text collections and topics to detect toponyms, 2) Stemming (Porter’s algorithm) and Lemmatization are also applied in combination with default stopwords filtering. The Query Expansion methods tested are the Bose-Einstein (Bo1) and Kullback-Leibler term weighting models. The experiments have been performed with the English Monolingual test collections of the GeoCLEF evaluations (from years 2005, 2006, 2007, and 2008) using the TF-IDF, BM25, and InL2 Information Retrieval algorithms over unprocessed texts as baselines. The experiments have been performed with each GeoCLEF test collection (25 topics per evaluation) separately and with the fusion of all these collections (100 topics). The results of evaluating separately Geographical Knowledge Re-Ranking, Linguistic Processing (lemmatization, stemming, and the combination of both), and Query Expansion with the fusion of all the topics show that all these processes improve the Mean Average Precision (MAP) and RPrecision effectiveness measures in all the experiments and show statistical significance over the baselines in most of them. The best results in MAP and RPrecision are obtained with the InL2 algorithm using the following techniques: Geographical Knowledge Re-Ranking, Lemmatization with Stemming, and Kullback-Leibler Query Expansion. Some configurations with Geographical Knowledge Re-Ranking, Linguistic Processing and Query Expansion have improved the MAP of the best official results at GeoCLEF evaluations of 2005, 2006, and 2007.KeywordsInformation retrievalGeographical gazetteersNatural language processingToponym disambiguationQuery expansionEfectiveness measures

OSIRIS is a web platform dedicated to the development of Ontology-based System for Semantic Information Retrieval and Indexation of multimedia resources which are shared within communautary and open web Spaces. Based on the use of both heavyweight ontologies and thesaurii, OSIRIS allows the end-user (1) to describe the semantic content of its resources by using an intuitive natural-language based model of annotation which is founded on the triple (Subject, Verb, Object), and (2) to formally represent these annotations by using Conceptual Graphs. Moreover, each resource can be described by adopting multiple points of view, which usually correspond to different end-users. These different points of view can be defined by using multiple ontologies which can be related to connected (or not-connected) domains. Developed from the integration of Semantic Web technologies and Web 2.0 technologies, OSIRIS aims at facilitating the deployment of semantic, collaborative, communautary and open web spaces.Keywordsontologyheavyweight ontologythesaurussemantic annotationsemantic informa-tion retrievalconceptual graphssemantic webintelligent multimedia systemcollaborative annotationsocial taggingsemantic web 2.0

The steady growth in the size of textual document collections is a key progress-driver for modern information retrieval techniques whose effectiveness and efficiency are constantly challenged. Given a user query, the number of retrieved documents can be overwhelmingly large, hampering their efficient exploitation by the user. In addition, retaining only relevant documents in a query answer is of paramount importance for an effective meeting of the user needs. In this situation, the query expansion technique offers an interesting solution for obtaining a complete answer while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. Interestingly enough, query expansion takes advantage of large text volumes by extracting statistical information about index terms co-occurrences and using it to make user queries better fit the real information needs. In this respect, a promising track consists in the application of data mining methods to the extraction of dependencies between terms. In this paper, we present a novel approach for mining knowledge supporting query expansion that is based on association rules. The key feature of our approach is a better trade-off between the size of the mining result and the conveyed knowledge. Thus, our association rules mining method implements results from Galois connection theory and compact representations of rules sets in order to reduce the huge number of potentially useful associations. An experimental study has examined the application of our approach to some real collections, whereby automatic query expansion has been performed. The results of the study show a significant improvement in the performances of the information retrieval system, both in terms of recall and precision, as highlighted by the carried out significance testing using the Wilcoxon test.

The purpose of the Information Retrieval (IR) is to find a set of documents that are relevant for a specific information need of a user. Traditional Information Retrieval model commonly used in commercial search engine is based on keyword indexing system and Boolean logic queries. One big drawback of traditional information retrieval is that they typically retrieve information without an explicitly defined domain of interest to the users so that a lot of no relevance information returns to users, which burden the user to pick up useful answer from these no relevance results. In order to tackle this issue, many semantic web information retrieval models have been proposed recently. The main advantage of Semantic Web is to enhance search mechanisms with the use of Ontology’s mechanisms. In this paper, we present our approach to personalize web search engine based on ontology. In addition, key techniques are also discussed in our paper. Compared to previous research, our works concentrate on the semantic similarity and the whole process including query submission and information annotation.