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

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.

Query expansion is a classical topic in the field of information retrieval, which is proposed to bridge the gap between searchers' information intents and their queries. Previous researches usually expand queries based on document collections, or some external resources such as WordNet and Wikipedia [1, 2, 3, 4, 5]. However, it seems that independently using one of these resources has some defects, document collections lack semantic information of words, while WordNet and Wikipedia may not include domain-specific knowledge in certain document collection. Our work aims to combine these two kinds of resources to establish an expansion model which represents not only domain-specific information but also semantic information. In our preliminary experiments, we construct a two-layer word graph and use Random-Walk algorithm to calculate the weights of each term in pseudo-relevance feedback documents, then select the highest weighted term to expand original query. The first layer of the word graph contains terms in related documents, while the second layer contains semantic senses corresponding to these terms. These terms and semantic senses are treated as vertices of the graph and connected with each other by all possible relationships, such as mutual information and semantic similarities. We utilized mutual information, semantic similarity and uniform distribution as the weight of term-term relation, sense-sense relation and word-sense relation respectively. Though these experiments show that our expansion outperform original queries, we are troubled with some difficult problems. Given the framework of semantic graph model, we need more effort to find out an optimal graph to represent the relationships between terms and their semantic senses. We utilized a two-layer graph model in our preliminary research, where terms from different documents are treated equally. Maybe we can introduce the document as a third layer in future work, where we can differ the same terms in different documents according to document relevance and context. Then we need appropriately represent initial weights of this words, senses and relationships. Various measures for weights of terms and term relations have been proved effective in other information retrieval tasks, such as TFIDF, mutual information (MI), but there is little research on weights for semantic senses and their relations. For polysemous words, we add all of their semantic senses to the graph and assume that these senses are uniformly distributed. Actually, it is not precise for a word in a special document and query. As we know, a polysemous word may have only one or two senses in a document, and they are not uniformly distributed. Give a word, what we should do is to determine its word senses in a relevant document and estimate the distribution of these senses. Word sense disambiguation may help us in this problem. Then, there are many methods to compute word similarity according to WordNet, which we use to represent the weights of relationships between word senses. Varelas et al implemented some popular methods to compute semantic similarity by mapping terms to an ontology and examining their relationships in that ontology [4]. We also need to know which algorithm for semantic similarity is most suitable for our model. Additional, WordNet is suitable to calculate word similarity but not suitable to measure word relevance. The inner hyperlinks of Wikipedia could help us to calculate word relevance. We wish to find an effective way to combine the similarity measure from WordNet and relevance measure from Wikipedia, which may completely reflect word relationships.

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.

Query-document vocabulary mismatch, the lack of query expressiveness for user needs and the phenomenon of short queries are the main issues associated with information retrieval systems. Query Expansion (QE) is one of the well-known alternative for overcoming these problems. It mainly involves finding synonyms or related words for the query terms. There are several approaches in the query expansion field such as statistical and semantic approaches; they focus on expanding the individual query terms rather than the entire query during the expansion process. An other category of approaches deals with the whole query by using a neural approach based on Pseudo Relevance feedback (PRF) documents. In this work, we carried out an ablation study to measure the impact of the classical and semantic (word embedding, order, context) based query expansion on the retrieval performance. The experiments conducted on the Arabic EveTAR dataset reveal that our hybrid proposed approach combining classical (PRF) and transformer (AraBERT) is competitive with the state-of-the-art methods. In fact, the obtained result in terms of the Mean Average Precision (MAP) is up to 0.72.

Research and experimentation using Arabic WordNet in the field of information retrieval are relatively new. It is limited compared to the research that has been done using Princeton WordNet. This work attempts to study the impact of Arabic WordNet on the performance of Arabic information retrieval. We extend Lucene with Arabic WordNet to expand user's queries. The major contribution of this study is to propose an interactive query expansion (IQE) methodology using the word's part-of-speech, according to the part it plays in a query. First, the user selects the appropriate part of speech for each term in the original query, and then he reselects the appropriate synonyms. Experimental results show that our IQE strategy produces a good Mean Average Precision (MAP), it is able to improve MAP by 12.6%, but no variant of automatic query expansion (AQE) strategies did. Nevertheless, the experiments allow us to conclude that with an appropriate use of Arabic WordNet as a source of linguistic information for AQE can improve effectiveness for Arabic information retrieval.

Query Expansion using Pseudo Relevance Feedback is a useful and a popular technique for reformulating the query. In our proposed query expansion method, we assume that relevant information can be found within a document near the central idea. The document is normally divided into sections, paragraphs and lines. The proposed method tries to extract keywords that are closer to the central theme of the document. The expansion terms are obtained by equi-frequency partition of the documents obtained from pseudo relevance feedback and by using tf-idf scores. The idf factor is calculated for number of partitions in documents. The group of words for query expansion is selected using the following approaches: the highest score, average score and a group of words that has maximum number of keywords. As each query behaved differently for different methods, the effect of these methods in selecting the words for query expansion is investigated. From this initial study, we extend the experiment to develop a rule-based statistical model that automatically selects the best group of words incorporating the tf-idf scoring and the 3 approaches explained here, in the future. The experiments were performed on FIRE 2011 Adhoc Hindi and English test collections on 50 queries each, using Terrier as retrieval engine.

The field of information retrieval has witnessed tangible progress over the past decades in response to the expanded usage of the internet and the dire need of users to search for massive amounts of digital information. Given the steady increase of Arabic e-content, excellent information retrieval systems must be devised to suit the nature and requirements of the Arabic language. This paper sheds light on the current progress in the field of Arabic information retrieval, identifies the challenges that hinder the progress of this science, and proposes suggestions for further research. This paper uses the descriptive analytical method to examine the reality of Arabic studies in the field of information retrieval and to study the problems that are being faced in this area. Specifically, the previous literature on information retrieval is reviewed by searching the related databases and websites.

The diffusion of automated information retrieval (IR) ap plications and of applications which share many of the peculiarities of IR applications is increasing. The demand for these types of applications draws attention to the lack of methodologies available for IR data design. The data modell ing of an IR application, that is, the data modelling of the IR part of a complete information system, has not yet been approached and studied as a complete process and in a struc tured way. The first part of this paper introduces the motivations and the scope of the DIRD (Design of Information Retrieval Data) project, which is devoted to the development of an environ ment for the design of advanced applications of IR. The second part of the paper addresses the design of the IR part (or IR application) of an information system. The concep tual paradigm necessary for the design of advanced IR applica tions is investigated. The Entity Relationship (ER) approach is compared with that conceptual modelling paradigm and is examined as a candidate data model for the conceptual design of IR data. A new ER approach is then introduced: this new approach extends the constructs of the ER model to manage the complexity of IR data. Two design examples are given to present the use of the new ER approach in designing IR data.

Microblog retrieval has received much attention in recent years due to the wide spread of social microblogging platforms such as Twitter. Many research studies investigated different approaches for microblog retrieval. Query expansion is one of the approaches that showed stable performance for improving microblog retrieval effectiveness. In this paper we comprehensively investigate query expansion for microblog retrieval using pseudo relevance feedback (PRF) with different configurations. In addition, we propose a hyperlink-extended PRF by expanding queries using embedded hyperlinks in retrieved microblogs. Our experimental results on TREC microblog data showed that PRF alone could outperform many retrieval approaches if configured properly. Results also showed that utilizing hyperlinked documents in tweets for query expansion improves results over traditional PRF. Our best results achieved using hyperlink-extended PRF outperformed most of the reported results using other approaches.

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

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.

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 document-length normalization problem has been widely studied in the field of information retrieval. The cosine normalization (Baeza-Yates and Ribeiro-Neto, 1999), the maximum if normalization (Allan et al., 1997) and the byte length normalization (Robertson et al., 1992) are the most commonly used normalization techniques. In (Singhal et al., 1996), authors studied the retrieval probability of documents w.r.t. their size, using different similarity measures. They have shown that none of existing measures retrieve the documents of different lengths with the same probability. We first show here that the document and query sizes are indeed very influent on the similarity score expectation. Therefore, we propose to realize a statistical regression of the similarity scores distribution w. r. t. document and query sizes in order to normalize them. Experimental results appear to indicate that our approach, as well in the field of classical Information Retrieval as when applied to a document clustering process, allows to judge similarities really more fairly.

These research studies compare two methods for ontology creation and their corresponding tools. The main objective of this work is the building of a graphical editor for ontology construction, based on software usability criteria. This comparison shows which methods used in previous tools will remains in the new editor, and what new tools can be developed for enhancing the ontology management made using the old one. The use of graphics and diagrams helps in the discovery of new knowledge, from information received from various sources, being the Internet or databases. This new type of editor improves the visualization of relations between the terms of ontologies and thereby improves the efficiency of its construction for commercial application systems. In addition, this form of information representation, based on these diagrams, will allow information systems based on ontologies to identify - automatically -concepts, hidden elements, the flow time between the events of cause and consequences and the meaning of relations between the captured information. The Ontojuris project has the purpose given above, and it is described on this paper. The project consists of a multilingual information system for legal information retrieval associated with an ontology editor, called Knowledge Engineering Suite. The system accepts queries in one language, and allows information retrieval based on similarity of documents, written in one of several languages. The multilingual ontology editor works with the concept of Universal Words (UW's). The UWs are universal representations of things and objects, and it is developed inside the UNL Project (being UNL an acronym for Universal Networking Language) and allows the query expansion in a multilingual way. Query expansion can improve searching process, required by user, by including additional terms which have similar meaning to the original query. In this study, we proposed a new expansion method which is based on domain ontology and UWs, to achieve better performance for a multilingual information retrieval. So, the Ontojuris project aims to facilitate access to information on legislation in the area of Intellectual Property Law, Consumer Rights and Electronic Law in the consortium formed by researches in Brazil, Chile, Spain and Argentina.

Pseudo-relevance feedback (PRF) is a very effective query expansion approach, which reformulates queries by selecting expansion terms from top k pseudo-relevant documents. Although standard PRF models have been proven effective to deal with vocabulary mismatch between users’ queries and relevant documents, expansion terms are selected without considering their similarity to the original query terms. In this article, we propose a method to incorporate word embedding (WE) similarity into PRF models for Arabic information retrieval (IR). The main idea is to select expansion terms using their distribution in the set of top pseudo-relevant documents along with their similarity to the original query terms. Experiments are conducted on the standard Arabic TREC 2001/2002 collection using three neural WE models. The obtained results show that our PRF extensions significantly outperform their baseline PRF models. Moreover, they enhanced the baseline IR model by 22% and 68% for the mean average precision (MAP) and the robustness index (RI), respectively.