Search Results (11)

To address the computational-efficiency bottlenecks of Hybrid A* and its bidirectional variant in long-distance parking and narrow-slot scenarios, an improved bidirectional Hybrid A* algorithm is presented. First, the cohesion cost is reformulated in a vector-space representation. Distance and heading-consistency terms are evaluated using dot products, which eliminates trigonometric operations and reduces the overhead of node evaluation. Second, an RS (Reeds–Shepp) cost template is constructed on a sparse grid of key nodes. Neighborhood costs are approximated with Euclidean-distance correction. In addition, a geometry reachability-based trigger is designed for analytic RS connections to avoid redundant analytic linking and unnecessary RS curve computations. Third, a KD-tree spatial index is introduced to accelerate nearest-neighbor queries in the Voronoi potential field, and vehicle corner coordinates are updated in a vectorized manner to improve the efficiency of potential-field evaluation. Simulation results in parallel and perpendicular parking show that, compared with the baseline bidirectional Hybrid A* algorithm, RS computations are reduced by 98.7% and 97.8%, respectively, while total planning time is shortened by 63.2% and 57.5%, with stable path quality. These results indicate that the proposed method effectively mitigates the dominant computational costs of bidirectional Hybrid A* in complex parking tasks and improves the efficiency and real-time performance of automatic parking path planning. Full article

Tracert-retrieval-augmented generation (RAG) is a novel retrieval-augmented framework designed for efficient, document-level multi-hop reasoning. Unlike conventional RAG systems that retrieve top-k text segments based solely on dense similarity, Tracert-RAG predicts a semantic goal vector from the user query, constructs a local semantic graph from the document embeddings, and employs a direction-aware greedy traversal to identify reasoning paths toward the goal. This system eliminates the inflexibility of symbolic graph traversal and the inefficiency of manual query reformulation. On literary analysis tasks from Pride and Prejudice, Tracert-RAG outperforms standard RAG and graph RAG baselines in answer quality, inference speed, and interpretability. Specifically, it achieves the highest average answer quality (8.05 out of 10) while reducing indexing time by a factor of 80 compared to graph RAG methods. Full article

Query reformulation techniques optimize user queries to better align with documents, thus improving the performance of Information Retrieval (IR) systems. Previous methods have primarily focused on query expansion using techniques such as synonym replacement to improve recall. With the rapid advancement of Large Language Models (LLMs), the knowledge embedded within these models has grown. Research in prompt engineering has introduced various methods, with prompt chaining proving particularly effective for complex tasks. Directly prompting LLMs to reformulate queries has become a viable approach. However, existing LLM-based prompt methods for query reformulation often introduce irrelevant content into reformulated queries, resulting in decreased retrieval precision and misalignment with user intent. We propose a novel approach called Hierarchical Prompt Chaining for Query Reformulation (HiPC-QR). HiPC-QR employs a two-step prompt chaining technique to extract keywords from the original query and refine its structure by filtering out non-essential keywords based on the user’s query intent. This process reduces the query’s restrictiveness while simultaneously expanding essential keywords to enhance retrieval effectiveness. We evaluated the effectiveness of HiPC-QR on two benchmark retrieval datasets, namely MS MARCO and TREC Deep Learning.The experimental results show that HiPC-QR outperforms existing query reformulation methods on large-scale datasets in terms of both recall@10 and MRR@10. Full article

Knee osteoarthritis (KOA) is a prevalent condition globally, leading to significant pain and disability, particularly in individuals over the age of 40. While exercise has been shown to reduce symptoms and improve physical function and quality of life in patients with KOA, long-term adherence to exercise programs remains a challenge due to the lack of ongoing support. To address this, a chatbot was developed using large language models (LLMs) to provide evidence-based guidance and promote adherence to treatment. A systematic review conducted under the PRISMA framework identified relevant clinical guidelines that served as the foundational knowledge base for the chatbot. The Mistral 7B model, optimized with Parameter-Efficient Fine-Tuning (PEFT) and Mixture-of-Experts (MoE) techniques, was integrated to ensure computational efficiency and mitigate hallucinations, a critical concern in medical applications. Additionally, the chatbot employs Self-Reflective Retrieval-Augmented Generation (SELF-RAG) combined with Chain of Thought (CoT) reasoning, enabling dynamic query reformulation and the generation of accurate, evidence-based responses tailored to patient needs. The chatbot was evaluated by comparing pre- and post-improvement versions and against a reference model (ChatGPT), using metrics of accuracy, relevance, and consistency. The results demonstrated significant improvements in response quality and conversational coherence, emphasizing the potential of integrating advanced LLMs with retrieval and reasoning methods to address critical challenges in healthcare. This approach not only enhances treatment adherence but also strengthens patient–provider interactions in managing chronic conditions like KOA. Full article

In multi-hop question answering (MH-QA), the machine needs to infer the answer to a given question from multiple documents. Existing models usually apply entities as basic units in the reasoning path. Then they use relevant entities (in the same sentence or document) to expand the path and update the information of these entities to finish the QA. The process might add an entity irrelevant to the answer to the graph and then lead to incorrect predictions. It is further observed that state-of-the-art methods are susceptible to reasoning chains that pivot on compound entities. To make up the deficiency, we present a viable solution, i.e., incorporate phrases in the latent query reformulation method (IP-LQR), which incorporates phrases in the latent query reformulation to improve the cognitive ability of the proposed method for multi-hop question answering. Specifically, IP-LQR utilizes information from relevant contexts to reformulate the question in the semantic space. Then the updated query representations interact with contexts within which the answer is hidden. We also design a semantic-augmented fusion method based on the phrase graph, which is then used to propagate the information. IP-LQR is empirically evaluated on a popular MH-QA benchmark, HotpotQA, and the results of IP-LQR consistently outperform those of the state of the art, verifying its superiority. In summary, by incorporating phrases in the latent query reformulation and employing semantic-augmented embedding fusion, our proposed model can lead to better performance on MH-QA. Full article

In exploratory search, users sometimes combine two or more issued queries into new queries. We present such a kind of search behavior as query combination behavior. We find that the queries after combination usually can better meet users’ information needs. We also observe that users combine queries for different motivations, which leads to different types of query combination behaviors. Previous work on understanding user exploratory search behaviors has focused on how people reformulate queries, but not on how and why they combine queries. Being able to answer these questions is important for exploring how users search and learn during information retrieval processes and further developing support to assist searchers. In this paper, we first describe a two-layer hierarchical structure for understanding the space of query combination behavior types. We manually classify query combination behavior sessions from AOL and Sogou search engines and explain the relationship from combining queries to success. We then characterize some key aspects of this behavior and propose a classifier that can automatically classify types of query combination behavior using behavioral features. Finally, we summarize our findings and show how search engines can better assist searchers. Full article

Information retrieval (IR) aims to obtain relevant information according to a certain user need and involves a great diversity of data such as texts, images, or videos. Query expansion techniques, as part of information retrieval (IR), are used to obtain more items, particularly documents, that are relevant to the user requirements. The user initial query is reformulated, adding meaningful terms with similar significance. In this study, a supervised query expansion technique based on an innovative use of the Multinomial Naive Bayes to extract relevant terms from the first documents retrieved by the initial query is presented. The proposed method was evaluated using MAP and R-prec on the first 5, 10, 15, and 100 retrieved documents. The improved performance of the expanded queries increased the number of relevant retrieved documents in comparison to the baseline method. We achieved more accurate document retrieval results (MAP 0.335, R-prec 0.369, P5 0.579, P10 0.469, P15 0.393, P100 0.175) as compared to the top performers in TREC2017 Precision Medicine Track. Full article

Evidence-based healthcare integrates the best research evidence with clinical expertise in order to make decisions based on the best practices available. In this context, the task of collecting all the relevant information, a recall oriented task, in order to take the right decision within a reasonable time frame has become an important issue. In this paper, we investigate the problem of building effective Consumer Health Search (CHS) systems that use query variations to achieve high recall and fulfill the information needs of health consumers. In particular, we study an intent-aware gain metric used to estimate the amount of missing information and make a prediction about the achievable recall for each query reformulation during a search session. We evaluate and propose alternative formulations of this metric using standard test collections of the CLEF 2018 eHealth Evaluation Lab CHS. Full article

In Information Retrieval (IR), the semantic gap represents the mismatch between users’ queries and how retrieval models answer to these queries. In this paper, we explore how to use external knowledge resources to enhance bag-of-words representations and reduce the effect of the semantic gap between queries and documents. In this regard, we propose several simple but effective knowledge-based query expansion and reduction techniques, and we evaluate them for the medical domain. The query reformulations proposed are used to increase the probability of retrieving relevant documents through the addition to, or the removal from, the original query of highly specific terms. The experimental analyses on different test collections for Precision Medicine IR show the effectiveness of the developed techniques. In particular, a specific subset of query reformulations allow retrieval models to achieve top performing results in all the considered test collections. Full article

Regulations in the Building Industry are becoming increasingly complex and involve more than one technical area, covering products, components and project implementations. They also play an important role in ensuring the quality of a building, and to minimize its environmental impact. Control or conformance checking are becoming more complex every day, not only for industrials, but also for organizations charged with assessing the conformity of new products or processes. This paper will detail the approach taken by the CSTB (Centre Scientifique et Technique du Bâtiment) in order to simplify this conformance control task. The approach and the proposed solutions are based on semantic web technologies. For this purpose, we first establish a domain-ontology, which defines the main concepts involved and the relationships, including one based on OWL (Web Ontology Language) [1]. We rely on SBVR (Semantics of Business Vocabulary and Business Rules) [2] and SPARQL (SPARQL Protocol and RDF Query Language) [3] to reformulate the regulatory requirements written in natural language, respectively, in a controlled and formal language. We then structure our control process based on expert practices. Each elementary control step is defined as a SPARQL query and assembled into complex control processes “on demand”, according to the component tested and its semantic definition. Finally, we represent in RDF (Resource Description Framework) [4] the association between the SBVR rules and SPARQL queries representing the same regulatory constraints. Full article

Web Search Engines provide a huge number of answers in response to a user query, many of which are not relevant, whereas some of the most relevant ones may not be found. In the literature several approaches have been proposed in order to help a user to find the information relevant to his/her real needs on the Web. To achieve this goal the individual Information Behavior can been analyzed to ’keep’ track of the user’s interests. Keeping information is a type of Information Behavior, and in several works researchers have referred to it as the study on what people do during a search on the Web. Generally, the user’s actions (e.g., how the user moves from one Web page to another, or her/his download of a document, etc.) are recorded in Web logs. This paper reports on research activities which aim to exploit the information extracted from Web logs (or query logs) in personalized user ontologies, with the objective to support the user in the process of discovering Web information relevant to her/his information needs. Personalized ontologies are used to improve the quality of Web search by applying two main techniques: query reformulation and re-ranking of query evaluation results. In this paper we analyze various methodologies presented in the literature aimed at using personalized ontologies, defined on the basis of the observation of Information Behaviour to help the user in finding relevant information. Full article