Concept Based Representations for Ranking in Geographic Information Retrieval

Metonymically used location names (toponyms) refer to other, related entities and thus possess a meaning different from their literal, geographic sense. Metonymic uses are to be treated differently to improve the performance of geographic information retrieval (GIR). Statistics on toponym senses show that 75.06% of all location names are used in their literal sense, 17.05% are used metonymically, and 7.89% have a mixed sense. This article presents a method for disambiguating location names in texts between literal and metonymic senses, based on shallow features. The evaluation of this method is two‐fold. First, we use a memory‐based learner (TiMBL) to train a classifier and determine standard evaluation measures such as F‐score and accuracy. The classifier achieved an F‐score of 0.842 and an accuracy of 0.846 for identifying toponym senses in a subset of the CoNLL (Conference on Natural Language Learning) data. Second, we perform retrieval experiments based on the GeoCLEF data (newspaper article corpus and queries) from 2005 and 2006. We compare searching location names in a database index containing both their literal and metonymic senses with searching in an index containing their literal senses only. Evaluation results indicate that removing metonymic senses from the index yields a higher mean average precision (MAP) for GIR. In total, we observed a significant gain in MAP: an increase from 0.0704 to 0.0715 MAP for the GeoCLEF 2005 data, and an increase from 0.1944 to 0.2100 MAP for the GeoCLEF 2006 data.

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-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.

Geographic information retrieval (GIR) is concerned with returning information in response to an information need, typically expressed in terms of a thematic and spa- tial component linked by a spatial relationship. However, evaluation initiatives have of- ten failed to show significant differences between simple text baselines and more complex spatially enabled GIR approaches. We explore the effectiveness of three systems (a text baseline, spatial query expansion, and a full GIR system utilizing both text and spatial in- dexes) at retrieving documents from a corpus describing mountaineering expeditions, cen- tred around fine grained toponyms. To allow evaluation, we use user generated content (UGC) in the form of metadata associated with individual articles to build a test collec- tion of queries and judgments. The test collection allowed us to demonstrate that a GIR- based method significantly outperformed a text baseline for all but very specific queries associated with very small query radii. We argue that such approaches to test collection development have much to offer in the evaluation of GIR.

Purpose — To provide a theoretical background to understand current local search engines as an aspect of specialized search, and understand the data sources and used technologies. Design/methodology/approach — Selected local search engines are examined and compared toward their use of geographic information retrieval (GIR) technologies, data sources, available entity information, processing, and interfaces. An introduction to the field of GIR is given and its use in the selected systems is discussed. Findings — All selected commercial local search engines utilize GIR technology in varying degrees for information preparation and presentation. It is also starting to be used in regular Web search. However, major differences can be found between the different search engines. Research limitations/implications — This study is not exhaustive and only uses informal comparisons without definitive ranking. Due to the unavailability of hard data, informed guesses were made based on available public interfaces and literature. Practical implications — A source of background information for understanding the results of local search engines, their provenance, and their potential. Originality/value — An overview of GIR technology in the context of commercial search engines integrates research efforts and commercial systems and helps to understand both sides better.

Latent Semantic Indexing (LSI) and Relevance Feedback (RF) have been shown to be extremely useful in information retrieval respectively. But at the context of Web image retrieval, LSI is limited for the Single Value Decomposition (SVD) computation cost to large dataset while RF is confused with the reluctance of interaction for most Web users. In this paper, a Pseudo Relevance Feedback approach based on Local Latent Semantic Indexing (PRF-LLSI) is proposed, which integrating the LSI and RF, and making use of the benefit of them while solving the limitation of them. The Local LSI (LLSI) method performs a low-dimensional SVD on the local region of initial retrieved results. Both keywords and image contents of the Web images are computed by LLSI to re-rank the initial retrieval results automatically. The PRF-LLSI contribute to the following: 1) Local LSI resolves the heavy computation cost of LSI; 2) Pseudo Relevance Feedback doesn’t need the user’s interaction; 3) LLSI combine the textual and visual features, which improves the precision of the system. The experiments are done in our VAST (VisuAl & SemanTic image search) system, and the results show the effectiveness of the proposed method.

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.

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.

Although transportation practitioners nowadays have an unprecedented level of access to massive information, there still exist substantial gaps in their ability to efficiently and reliably find the right information, at the right time, and for the task/decision at hand. To address this gap, this paper proposes a context-aware information retrieval (IR) approach, which can capture and exploit the conceptualization of user needs, decision context, and content meanings to support the retrieval of information that is more relevant to decision making. The proposed IR approach includes three primary components: semantic annotation (SA), semantic query processing (SQP), and semantic document ranking (SDR). This paper focuses on SA for IR for supporting the Transportation Project Environmental Review (TPER) decision-making process. The paper proposes an epistemology-based SA algorithm for automatically annotating webpages in the TPER domain with contextual concepts from an epistemological model. The TPER epistemology is a semantic model for representing and reasoning about information and information retrieval in the TPER domain. In developing the proposed algorithm, a number of syntactic-based and semantic-based annotation methods/algorithms were developed and tested. For the syntactic-based algorithms, the effect of syntactic expansion and filtering was investigated. For the semantic-based algorithms, different semantic similarity calculation methods were evaluated. All the algorithms were tested on a testing data set of 1,328 webpages, which were collected from the FHWA Environmental Review Toolkit Website, and evaluated in terms of Mean Average Precision (MAP) and average precision. The final, proposed SA algorithm achieved 84.07% MAP and 90.67% average precision at top 50 documents, on the testing data.

The development of digital and online information repositories is creating many opportunities and also new challenges in information retrieval. The availability of online documents in many different languages makes it possible for users around the world to directly access previously unimagined sources of information. However in conventional information retrieval systems the user must enter a search query in the language of the documents in order to retrieve it. This requires that users can express their queries in those languages in which the information is available and can understand the documents returned by the retrieval process. This restriction clearly limits the amount and type of information that an individual user really has access to. Cross Lingual Information Access is concerned with technologies that let users express their query in their native language, and irrespective of the language in which the information is available, present their information in the user-preferred language or set of languages, in a manner that satisfies the user's information needs. The additional processing may take the form of machine translation of snippets, summarization and subsequent translation of summaries and/or information extraction. In recent times, research in Cross Lingual Information Access has been vigorously pursued through several international fora, such as, the Cross-Language Evaluation Forum (CLEF), NTCIR Asian Language Retrieval, Question-answering Workshop and such other fora. A workshop geared towards cross language information retrieval in Indian languages (FIRE) was organized in December 2008. In addition to CLIR, significant results have been obtained in multilingual summarization workshops and cross-language named entity extraction challenges by the ACL (Association for Computational Linguistics) and the Geographic Information retrieval (GeoCLEF) track of CLEF. The previous two issues of this workshop were held in January 2007, during IJCAI 2007 in Hyderabad, India (http://search.iiit.ac.in/CLIA2007/) and subsequently during IJCNLP 2008 in Hyderabad, India (http://search.iiit.ac.in/CLIA2008/). Both the previous workshops attracted an encouraging number of submissions, and a large number of registered participants. This third international workshop on Cross Lingual Information Access aims to bring together various trends in multi-source, cross and multilingual information retrieval and access, and provide a venue for researchers and practitioners from academia, government, and industry to interact and share a broad spectrum of ideas, views and applications. The present workshop includes an invited keynote talk, presentations of technical papers selected after peer review followed by a panel discussion.

Relevance Feedback (RF) is an important technique to improve information retrieval and has emerged as one of the hottest topics for both the industry and academic researchers. The performance of RF depends on feedback information. As the volume of feedback information gradually increases, Explicit Relevance Feedback (ERF) is attractive. In this paper, we focus on the ERF in which Feedback information is given. With regards to the lack of information in ERF, we apply Pseudo Relevance Feedback (PRF) to enhance retrieval effectiveness. However, the instability of PRF can result in a negative impact on retrieval performance. We use feedback information to define features and propose a classification model to predict which query can benefit from PRF. Then, we form relevance feedback fusion by the prediction of PRF performance. This method is designed to exploit the strengths of PRF and ERF while avoiding some weaknesses of these approaches. Experiment results show that our approach is feasible and effective.

For our participation in the second edition of GeoCLEF, we have researched the incorporation of geographic knowledge into Geographic Information Retrieval (GIR). Our system is made up of an IR module (IR-n) and a Geographic Knowledge module (Geonames). The results show that the addition of geographic knowledge has a negative impact on the precision. However, the fact that for some topics the obtained results are better, makes us conclude that the addition of this knowledge could be useful but further research is needed in order to determine how.

This paper describes the GEOUJA System, a Geographical Information Retrieval (GIR) system submitted by the SINAI group of the University of Jaén in GeoCLEF 2007. The objective of our system is to filter the documents retrieved from an information retrieval (IR) subsystem, given a multilingual statement describing a spatial user need. The results of the experiments show that the new heuristics and rules applied in the geo-relation validator module improve the general precision of our system. The increasing of the number of documents retrieved by the information retrieval subsystem also improves the final results.KeywordsInformation RetrievalSpatial RelationQuery ExpansionInformation Retrieval SystemBilingual TaskThese 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.

The current practice of users searching for different types of geo-resources in a geographic area and wishing to identify the most convenient routes for visiting the most relevant ones, requires the iterative formulation of several queries: first to identify the more interesting resources and then to select the best route to visit them. In order to simplify this process, in this paper a novel functionality for a geographic information retrieval (GIR) system is proposed, which retrieves and ranks several routes for visiting a number of relevant georeferenced resources as a result of a single query, named flexible trip-planning query. An original retrieval model is defined to identify the relevant resources and to rank the most convenient routes by taking into account personal user preferences. To this end, a graph-based algorithm is defined, exploiting prioritized aggregation to optimize the routes’ identification and ranking. The proposed algorithm is applied in the proof-of-concept of a Smart cOmmunity-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) designed to strengthen local communities: it collects and manages open data from regional authorities describing categories of authoritative territorial resources and services, such as schools, hospitals, etc., and from volunteered geographic services (VGSs) created by citizens to offer services in their neighbourhood.

Humans use natural language to describe places, relying on mental processes to perceive, interpret, and communicate information about spatial relationships, environments, and navigation. Computational location-based systems strive to replicate this capability by enabling the retrieval of geographic locations from textual descriptions or queries. However, progress in this domain remains constrained by the limited availability of extensive, linguistically diverse textual datasets, which are essential for developing and evaluating robust geographic information retrieval methodologies. In this study, we conducted a review of existing geocoding datasets used for textual geolocation. Our objectives were to systematically compare these datasets, characterize their attributes, and assess their impact on retrieval performance as reported in the literature. A critical challenge we identified was the inconsistency in evaluation practices across studies, which complicates direct comparisons and underscores the need for standardized benchmarks. This review synthesizes the current landscape of geocoding datasets, offering insights for informed dataset development and fosters more consistent evaluation practices. By addressing the imperative of dataset standardization and availability, we aim to support the creation of more effective geographic information retrieval systems and establish a solid foundation for future research in this field.