Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· pp. 5350-5353· 0 citations· 28 references
Computer Science
TL;DR
This tutorial provides a structured and in-depth overview of the complete temporal information access pipeline: Temporal Information Extraction (TIE), Temporal Information Retrieval (TIR), and Temporal Question Answering (TQA).
Abstract
Information continuously evolves over time. Because of this dynamic nature, time becomes a fundamental dimension that shapes how we extract, retrieve, interpret, and reason about knowledge. As information systems are constantly updated, models must determine not only what is relevant, but also when that information is valid. This tutorial provides a structured and in-depth overview of the complete temporal information access pipeline: Temporal Information Extraction (TIE), Temporal Information Retrieval (TIR), and Temporal Question Answering (TQA). We examine the progression of temporal methods from early rule-based extraction and probabilistic retrieval to contemporary transformer-based and large language model (LLM) architectures. Participants gain a solid understanding of the core principles underlying the identification and normalization of time expressions, time-aware document ranking, and temporal reasoning in retrieval-augmented generation (RAG). The tutorial concludes with a discussion of open challenges and future research directions aimed at building AI systems that are temporally aware, robust, and adaptive. By connecting classical extraction and IR foundations with modern LLM-based reasoning, this tutorial presents a cohesive and up-to-date perspective on temporal information systems.
Large language models (LLMs) have transformed AI, yet they remain fundamentally limited by hallucination, unverifiable reasoning, and shallow evidence grounding. We argue that structure mining-rooted in decades of KDD research on taxonomy induction, ontology design, entity typing, and knowledge graph construction-is the key to overcoming these limitations. This tutorial presents a unified vision in which structuring serves as the enabling foundation for three pillars of next-generation LLM systems: (1) Structured Retrieval, where organizing corpora into ontology-guided multidimensional representations enables SQL-like queries that achieve substantially more precise and complete retrieval than similarity-based approaches; (2) Structured Reasoning, where grounding each inference step in typed, graph-structured evidence transforms opaque generation into auditable, verifiable reasoning chains; and (3) Structured Agent Memory, where multi-dimensional memory architectures bridge external corpus knowledge and experiential agent knowledge through a mutually enriching dual-memory design. Across all three pillars, we highlight how the cooperative interplay between classical KDD techniques and modern LLMs-where KDD defines structural schemas and quality constraints while LLMs execute flexible extraction and reasoning-creates systems that are more reliable, interpretable, and faithful. The tutorial covers both foundational methods and the latest advances (2024--2026), and concludes with open problems and future research directions at the intersection of data mining and LLMs.
Pengcheng Jiang, Jiashuo Sun, Wonbin Kweon et al.· Proceedings of the 32nd ACM...· 0 citations
A methodical investigation of temporal prompting techniques for LLM-based EL is presented, and it is demonstrated that explicit temporal prompting can reduce drift mistakes by up to 40% using a dataset of temporally-sensitive mentions linked with Wikidata snapshots.
This paper introduces a semi-symbolic framework that integrates word-spotting techniques for post-OCR correction with a knowledge graph representation that enables the agent to access information through synthesized queries that are robust to misinterpretation and hallucination.
S. Nicolau, Adrià Molina, Oriol Ramos Terrades et al.· IEEE International Conferenc...· 0 citations
This work introduces a trust based adaptive reranking model- ATM (Adaptive Trust Model) that allocates computational resources according to file level uncertainty, instead of assigning a fixed number of reranker calls per query, which focuses computation only where ranking confidence is low.
Jenny Kalaiarasi.S· Journal of Intelligent Decis...· 0 citations
For decades, search and recommendation systems have been optimized as distinct components within large-scale discovery platforms. The rise of generative AI is beginning to blur this boundary. At Spotify, we are exploring how large language models can evolve from tools that retrieve content into systems that reason over users, catalogs, and intent, while remaining steerable through natural language and user interaction. This talk presents lessons from deploying and studying generative retrieval and recommendation systems across Spotify's content ecosystem. I will describe how semantic identifiers enable language models to operate directly over large, heterogeneous catalogs, allowing search, recommendation, retrieval, explanation, and user understanding to be expressed within a common generative framework. I will discuss recent work on production-scale podcast discovery, language-steerable recommendation, and the NEO framework for unifying search, recommendation, and reasoning across multiple content types. These systems demonstrate how grounding language models in catalog entities and user behavior can improve discovery while preserving the flexibility of natural-language interaction. More broadly, they suggest a path toward discovery systems in which retrieval, recommendation, and reasoning are no longer separate stages, but capabilities of a shared generative model. Beyond model frameworks, I will discuss the emerging challenges of alignment and evaluation in discovery systems. Unlike traditional retrieval problems, generative recommendation often has many valid answers. I will present approaches for learning from large-scale behavioral signals, preference-aware optimization, and profile-aware LLM-as-a-judge evaluation, along with lessons from online experimentation at Spotify. These experiences suggest that future discovery systems will require new forms of personalization, controllability, and evaluation that extend beyond conventional ranking metrics. I will conclude with a research agenda for generative discovery systems, including language-steerable interfaces, unified retrieval-and-reasoning models, preference-aligned generation, and evaluation frameworks designed to measure user-specific relevance at scale. As search, recommendation, and conversational AI continue to converge, these directions point toward a new generation of discovery systems that can understand intent, reason over large catalogs, and help users navigate increasingly complex information spaces.
Paul N. Bennett· Proceedings of the 32nd ACM...· 0 citations
Reasoning-intensive temporal retrieval requires matching a query to documents whose relevance depends on shared temporal reasoning rather than lexical overlap. Expanding a query into several reformulations that make its temporal intent explicit, and retrieving with each, supplies this reasoning, but fusing the resulting rankings with equal weights wastes accuracy: for any single query, only some reformulations are reliable. We propose query-difficulty-gated fusion of reasoning views. From each view we read an eight-dimensional signature of its score distribution, built from query-performance-prediction quantities such as softmax entropy, score gaps, and dispersion, and a gate of roughly one thousand parameters maps these signatures to per-query view weights. The fused ranking uses no relevance labels at inference, no re-ranking, and no fine-tuning of the retriever; the gate is trained leave-one-task-out. On the \textsc{Tempo} benchmark, the method improves all six retrievers we evaluate, from BERT encoders to 7B decoder retrievers, with the largest gains on the weaker backbones. The strongest retrievers reach $0.297$ and $0.303$ nDCG@10, and the per-query gain over the original query is significant under a paired bootstrap ($p<0.001$). A per-query oracle reaches $0.364$ against our realized $0.297$, exposing headroom that identifies per-query view selection as a concrete next step.
J. Holdcroft, Abdelrahman Abdallah, Adam Jatowt· 0 citations
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