{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:54:56Z","timestamp":1773802496358,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Lifelogging involves the continuous and comprehensive recording of a user\u2019s daily activities, behaviors, and interactions, offering valuable insights for personalized healthcare, event retrieval, and lifestyle analysis. However, extracting meaningful patterns from lifelog data requires models to capture deeper temporal contexts beyond simple retrieval. To address this, we introduce ContextGraph, a lifelog intelligence framework that models lifelogs as a Temporal Knowledge Graph (TKG) to reason about the user\u2019s evolving life patterns over time. ContextGraph computes Day Context Embeddings (DCE) to encode the temporal spread and social scene context of user's daily behavior. Then a novel Lens module extracts semantically meaningful subgraph snapshots around an anchor node in the TKG, representing specific personal contexts in the user\u2019s life. The Lens module also computes an evolution signature for each subgraph, indicating whether it is growing, decaying, or remaining static. By analyzing these evolution signatures, ContextGraph provides actionable insights into the user\u2019s lifelogs such as stable routines, behavioral drifts, or lifestyle changes. Our experiments showcase DCE's versatility, outperforming baselines in graph\/node classification and reasoning on the Enzyme and DBLP datasets.<\/jats:p>","DOI":"10.1609\/aaai.v40i18.38600","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:36:49Z","timestamp":1773794209000},"page":"15698-15706","source":"Crossref","is-referenced-by-count":0,"title":["ContextGraph: Lifelog Intelligence Framework for Contextual Subgraph Evolution"],"prefix":"10.1609","volume":"40","author":[{"given":"Anil","family":"Sharma","sequence":"first","affiliation":[]},{"given":"Gunturi Venkata Sai Phani","family":"Kiran","sequence":"additional","affiliation":[]},{"given":"Jayesh Rajkumar","family":"Vachhani","sequence":"additional","affiliation":[]},{"given":"Sourabh Vasant","family":"Gothe","sequence":"additional","affiliation":[]},{"given":"Ayon","family":"Chattopadhyay","sequence":"additional","affiliation":[]},{"given":"Yashwant","family":"Saini","sequence":"additional","affiliation":[]},{"given":"Parameswaranath Vadackupurath","family":"Mani","sequence":"additional","affiliation":[]},{"given":"Barath Raj","family":"Kandur Raja","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38600\/42562","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/38600\/42562","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:36:49Z","timestamp":1773794209000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/38600"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i18.38600","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}