Syntactic open domain Arabic question/answering system for factoid questions

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 development of advanced Information Retrieval (IR) applications is of a particular priority in the context of the Arabic language. In this PhD thesis, our aim is improving the performances of Arabic Question/Answering (Q/A) systems. Indeed, we propose an approach which is composed of three levels. We have showed through experiments conducted on a set of 2,264 translated CLEF and TREC questions that the accuracy, the Mean Reciprocal Rank (MRR) and the number of answered questions are enhanced using a Query Expansion (QE) module based on Arabic Wordnet (AWN) in the first level and a structure-based Passage Retrieval (PR) module in the second level. In order to evaluate the impact of the AWN coverage on the performances, we have automatically extended its content in terms of Named Entities (NE), Nouns and Verbs. The next step consists in developing a semantic reasoning process based on Conceptual Graphs (CGs) as a third level.

Conventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. The semantic approaches for QE overcome this limitation, whereby a search query is expanded with meaningful terms that accord with user information needs. This paper surveys recent approaches to semantic QE that employ different models and strategies and leverages various knowledge structures. We organize these approaches into a taxonomy that includes linguistic methods, ontology-based methods, and mixed-mode methods. We also discuss the strengths and limitations of each type of semantic QE method. In addition, we evaluate various semantic QE approaches in terms of knowledge structure utilization, corpus collection, baseline model adaptation, and retrieval performance. Finally, future directions in exploiting personalized social information and multiple ontologies for semantic QE are suggested.

Texts classification is an important field that can be used in data mining, information retrieval, machine learning. Documents classification now widely used in different domains, such as mail spam filtering, article indexing, Web searching, and Web page categorization. There are many researches in documents classification for English language, but a few research in Arabic language, while there are large community in the world that uses this language. This paper analyzes the effect of preprocessing, such as tokenization, normalization and removing stop words, stemming using Khoja stemmer, stemming using light stemmer, stemming using Khoja stemmer with tokenization, normalization and removing stop words, and stemming using light stemmer with tokenization, normalization and removing stop words on documents classification. This study uses three classification algorithms, Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM), it is applied on online Arabic corpus prepared by Diab Abuaiadh. Rapid Miner tool is used to apply the three classification models. Whereas the condition of the documents before preprocessing is compared with their condition after preprocessing to determine the extent of the effect of preprocessing on documents classification. the results demonstrate variation between document classification before preprocessing and after preprocessing, and difference between the three algorithms in terms of Accuracy, Precision, Recall, and F1-Score, whereas it will be discussed later.

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.

In this paper, we propose a novel knowledge graph completion framework to leverage a relation-specific attention mechanism integrated with an embedding translation strategy to improve the accuracy and contextual understanding of link prediction tasks. Unlike traditional models that rely on fixed transformation spaces, the proposed method dynamically captures fine-grained relational semantics by combining hierarchical candidate categorization, relation-guided entity projection, and asymmetric score functions. Specifically, the proposed model employs K-means clustering and principal component analysis (PCA) to identify semantically consistent entity sets, and integrates attention-weighted multi-attribute embeddings to construct robust relational representations. A margin-based ranking loss with normalized embedding constraints ensures effective optimization, further supported by Xavier initialization and stochastic gradient descent. Extensive experiments on two benchmark datasets, WN18 and FB15K, demonstrate the superiority of the proposed method. Specifically, on WN18, the proposed method achieves the lowest mean rank (MR) of 144, with competitive results in mean reciprocal rank (MRR) (0.902), Hits@1 (89.0%), Hits@3 (90.4%), and Hits@10 (96.3%), closely rivaling state- of-the-art models like QuatE and ComplEx. On FB15K, the proposed method again delivers the best Mean Rank of 21, along with strong scores in MRR (0.831), Hits@1 (72.2%), Hits@3 (88.4%), and the highest Hits@10 (92.5%) among all compared methods.

Information retrieval has become an important field of study and research under computer science due to explosive growth of information available in the form of full text, hypertext, administrative text, directory, numeric or bibliographic text. The research work is going on various aspects of information retrieval systems so as to improve its efficiency and reliability. This paper presents a comprehensive study, which discusses not only emergence and evolution of information retrieval but also includes different information retrieval models and some important aspects such as document representation, similarity measure and query expansion. Keywords—Information Retrieval, query expansion, similarity measure, query expansion, vector space model.

Ontology is the basis of sharing and reusing knowledge on the Semantic Web, and ontology-based semantic retrieval is a hotspot of current research. Fuzzy ontology is an extension of domain ontology for solving the uncertainty problems. To represent fuzzy knowledge more effectively, this paper presents a new series of fuzzy ontology models that consists of fuzzy domain ontology and fuzzy linguistic variable ontologies, considering semantic relationships of concepts, including set relation, order relation, equivalence relation and semantic association relation etc. The process to construct linguistic variables ontology is discussed. Using ontology and RDFS, the knowledge model for product information is created. To achieve semantic retrieval, the semantic query expansion in SeRQL is constructed by semantic relations between fuzzy concepts. The application shows that these models can overcome the localization of other fuzzy ontology models, and this research facilitates the fuzzy knowledge sharing and semantic retrieval on the Semantic Web.

When confronted with a query, question answering systems endeavor to extract the most exact answers possible by determining the answer type that fits with the query and the key terms used in the query. However, the efficacy of such systems is limited by the fact that the terms used in a query may be in a syntactic form different to that of the same words in a document. In this paper, we present an efficient semantic query expansion methodology based on a question category concept list comprised of terms that are semantically close to terms used in a query. The semantically close terms of a term in a query may be hypernyms, synonyms, or terms in a different syntactic category. The proposed system first constructs a concept list for each question type and then builds the concept list for each question category using a learning algorithm. When a new query is given, the question is classified into the node in question category, and the query is expanded using the concept list of the classified category. In the question answering experiments on 42,654 Wall Street Journal documents of the TREC collection, the traditional system showed in 0.223 in MRR and the proposed system showed 0.50 superior to the traditional question answering system. The results of the present experiments suggest the promise of the proposed method.

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.

Computing knowledge graph (KG) embeddings is a technique to learn distributional representations for components of a knowledge graph while preserving structural information. The learned embeddings can be used in multiple downstream tasks such as question answering, information extraction, query expansion, semantic similarity, and information retrieval. Over the past years, multiple embedding techniques have been proposed based on different underlying assumptions. The most actively researched models are translation-based which treat relations as translation operations in a shared (or relation-specific) space. Interestingly, almost all KG embedding models treat each triple equally, regardless of the fact that the contribution of each triple to the global information content differs substantially. Many triples can be inferred from others, while some triples are the foundational (basis) statements that constitute a knowledge graph, thereby supporting other triples. Hence, in order to learn a suitable embedding model, each triple should be treated differently with respect to its information content. Here, we propose a data-driven approach to measure the information content of each triple with respect to the whole knowledge graph by using rule mining and PageRank. We show how to compute triple-specific weights to improve the performance of three KG embedding models (TransE, TransR and HolE). Link prediction tasks on two standard datasets, FB15K and WN18, show the effectiveness of our weighted KG embedding model over other more complex models. In fact, for FB15K our TransE-RW embeddings model outperforms models such as TransE, TransM, TransH, and TransR by at least 12.98% for measuring the Mean Rank and at least 1.45% for HIT@10. Our HolE-RW model also outperforms HolE and ComplEx by at least 14.3% for MRR and about 30.4% for HIT@1 on FB15K. Finally, TransR-RW show an improvement over TransR by 3.90% for Mean Rank and 0.87% for HIT@10.

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.

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

In this paper, we present FOAFRealm, a user management library based on social networking and FOAF metadata, and its deployment in JeromeDL, a digital library with semantics. We will demonstrate the most important features of FOAFRealm as applied to JeromeDL - semantically-enhanced search features, semantic social collaborative filtering and semantic query expansion - to exemplify the advantages of semantic technologies in digital libraries.

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.