Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · cs/0609144

The Management and Integration of Biomedical Knowledge: Application in the Health-e-Child Project (Position Paper)

E. Jimenez-Ruiz·R. Berlanga·I. Sanz·R. McClatchey·R. Danger·D. Manset·J. Paraire·A. Rios
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Health-e-Child project aims to develop an integrated healthcare platform for European paediatrics. In order to achieve a comprehensive view of childrens health, a complex integration of biomedical data, information, and knowledge is necessary. Ontologies will be used to formally define this domain knowledge and will form the basis for the medical knowledge management system. This paper introduces an innovative methodology for the vertical integration of biomedical knowledge. This approach will be largely clinician-centered and will enable the definition of ontology fragments, connections between them (semantic bridges) and enriched ontology fragments (views). The strategy for the specification and capture of fragments, bridges and views is outlined with preliminary examples demonstrated in the collection of biomedical information from hospital databases, biomedical ontologies, and biomedical public databases.

Explore related subjects

cs.DB

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

E. Jimenez-Ruiz, R. Berlanga, I. Sanz, R. McClatchey, R. Danger, D. Manset, J. Paraire, A. Rios. 2006-09-26. The Management and Integration of Biomedical Knowledge: Application in the Health-e-Child Project (Position Paper). https://arxiv.org/abs/cs/0609144

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Example-Driven Intent Synthesis for Constrained Data Bundle Retrieval: Focused Text Snippet Extraction and Beyond

Selecting a bundle of items that collectively satisfies constraints is a fundamental task across databases, recommender systems, and text summarization. Unlike traditional retrieval that returns individual or top-k items, bundle retrieval is inherently combinatorial and, in general, NP-hard. Although package queries can efficiently retrieve bundles given a well-formed query, two key user-centric challenges remain: (1) expressing and tuning multi-dimensional bundle intent through a user-friendly interface, and (2) ensuring feasibility when the query yields empty results. We introduce Ex2Bundle, an Example-driven Bundle retrieval framework that enables users to specify their intent through example bundles and automatically synthesizes package queries that capture the intent implicit in those example bundles via aggregate constraints. Ex2Bundle also addresses a challenge unique to bundle retrieval: when inferred aggregate constraints are infeasible over the target data, our data-aware constraint relaxation minimally adjusts the constraint bounds while preserving alignment with user intent. We instantiate a specific application of focused text snippet extraction by example to demonstrate the efficacy of the Ex2Bundle framework. Extensive experiments over real-world datasets and a user study demonstrate that Ex2Bundle improves usability and consistently returns intent-aligned bundles even under distributional shifts of the target database.

cs.DB↗
arXiv2026

EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updates

Filtering approximate nearest neighbor search (Filtering ANNS) retrieves approximate nearest neighbors subject to attribute constraints in applications such as recommendation, retrieval-augmented generation (RAG), and LLM agent memory. However, predicate-specialized methods target either range or label filtering and can incur substantial index construction time, whereas predicate-agnostic methods support arbitrary predicates but struggle to sustain throughput and recall across a wide range of selectivities. In this paper, we propose EMA, a Filtering ANNS algorithm that supports multi-predicate queries over mixed numerical and categorical attributes as well as efficient dynamic updates. EMA attaches compact summaries, called Markers, to graph edges to record the attributes of candidates removed by geometric pruning. During query processing, EMA performs Marker-guided joint search with bounded edge recovery, enabling efficient filtering while preserving graph navigability. On multi-predicate workloads, EMA achieves 1.20x-28.18x the query throughput of the best competing baseline at comparable recall. In our complete-record replacement experiment, EMA retains approximately 98% of its initial query throughput without rebuilding the graph.

cs.DB↗
arXiv2026

DiagDBench: Evaluating Agentic LLMs on Interactive Database Diagnosis and Anomaly Remediation

Database diagnosis and anomaly remediation are essential to reliable database management system (DBMS) operation. Despite advances in large language model (LLM)-based database agents, constructing live DBMS environments that reproduce diverse anomalies at scale remains challenging, limiting the availability of executable samples for database agent training and evaluation. To this end, we introduce DiagDBench, a large-scale benchmark for training and evaluating database agents on interactive diagnosis, anomaly remediation, and optional workload performance optimization across diverse, realistic anomaly scenarios. Each sample pairs a user request, comprising an issue description and an anomaly-verified workload under which the issue was observed, with a self-contained, executable DBMS environment that supports real-time monitoring, interactive diagnosis, remediation, and workload execution. To construct these samples at scale, we propose an LLM-based framework with two complementary pipelines: Manual-Guided Anomaly Synthesis, which sequentially synthesizes user requests, database modifications, and anomaly-injection scripts grounded in DBA manuals and official DBMS documentation; and Incident-Guided Anomaly Reproduction, which reconstructs anomaly samples end to end from real-world enterprise online diagnosis incidents. Built using this framework with sample filtering, expert review, and refinement, DiagDBench comprises 6,940 synthesized training samples and a held-out test set of 454 expert-annotated samples, spanning three hardware configurations, two DBMSs, and three database schemas. Extensive evaluations of database agents powered by multiple LLMs demonstrate that even the best-performing agent, ReAct with GPT-6 Astra, scores only 56.24, whereas most agents score below 45, underscoring the difficulty of DiagDBench.

cs.DB↗