학회 정보

FQAS 2027: International Conference on Flexible Query Answering Systems

학회 웹사이트를 보려면 로그인해 주세요
무료 가입으로 공식 사이트 조회, 마감 추적, 이메일 리마인더를 이용할 수 있습니다.
회원가입 로그인
마감 카운트다운 배지 삽입
FQAS
이 데이터를 API로 가져오기
검색과 순위 목록은 자격 증명이 전혀 필요 없습니다. 이 페이지의 상세 데이터에는 무료 API 키가 필요합니다. 자세한 내용은 개발자 안내 페이지를 참고하세요.
투고 마감일:
2027-03-15 167일 남음
통보일:
개최일:
2027-09-08
개최지:
Bergamo, Italy
개최 횟수:
17
ICORE: C   조회: 682   팔로우: 0   참가: 0

회반 지수 (CP-I)

52.2 / 100
전체 5,687개 중 1,188위 · 상위 21%
학술적 인정 (35%)
58
투고 선별성 (20%) 데이터 없음 — 중립 기준값 50점으로 계산 —
개최 횟수 (20%)
78
커뮤니티 관심도 (10%)
10
공개 자료 충실도 (15%)
35

사용한 입력: 수록 등급: ICORE C · 확인되는 개최 횟수: 17 · 지난 24개월 동안 이 페이지를 연 연구자: 3명

공개 자료에서 빠진 항목: 역대 게재율 (+4.5) · 역대 회차 (+3.0) · 최우수 논문 기록 (+2.3)
주최자는 학회를 인증 신청한 뒤 이 페이지에서 바로 추가할 수 있습니다. 점수는 매일 밤 다시 계산됩니다. 이 점수를 올리는 방법

신뢰도 80% — 점수 중 중립 기준값이 아니라 실제 관측된 데이터에 근거한 비율. 이 점수는 어떻게 계산되나 · 전체 순위 보기 · 알고리즘 버전 1.1 · 산출일 2026-09-29

논문 모집

FQAS 2027 (International Conference on Flexible Query Answering Systems) is a ICORE C conference held in Bergamo, Italy on 2027-09-08. The paper submission deadline is 2027-03-15.

Since its first edition in 1994, the Flexible Query Answering System ( FQAS ) conference has been a pioneering forum for emerging topics in intelligent information access, knowledge management, and flexible querying. For over three decades, it has anticipated key shifts in the field, from managing uncertainty and imprecision in databases, to today’s multimodal, generative, and trustworthy AI challenges. The 2027 edition of FQAS will take place in Bergamo, Italy, a city of art, history, and innovation, nested between Milan and the Alps. Participants will enjoy not only a vibrant scientific program, but also the unique cultural and natural amenities of Bergamo and its surroundings: the medieval charm of Città Alta and the beauty of nearby lakes and mountains. This setting offers an inspiring atmosphere for exchanging ideas and fostering collaboration. In an era shaped by large language models, hybrid reasoning, and sustainable AI, FQAS is uniquely positioned to define the future of human–machine interaction supported by knowledge. Flexible Query Answering Systems of the future will not just answer questions, they will act as cognitive companions, capable of understanding multimodal inputs, integrating diverse knowledge sources, and generating trustworthy insights. Flexible query answering systems are intended as systems that can handle vague, incomplete, or context-dependent questions. They stand at the intersection of databases, knowledge representation, and modern AI generative systems. The hardest and most urgent challenges come from trying to make these systems both flexible and reliable at scale. The central tension is balancing freedom (handling vague, real-world queries) with control (accuracy, efficiency, and trustworthiness). Most current research is about finding models to balance that trade-off without sacrificing usability or correctness. By continuing its tradition of fostering innovation, FQAS 2027 offers a platform for researchers and practitioners to shape the next generation of intelligent, flexible, and responsible query answering systems. Topics We invite contributions, but not limited to, the following key topics: Handling ambiguity and vagueness: real user queries are rarely precise, they formulate vague queries like “cheap hotels near the center”, “high-rated restaurants”; this introduces concepts from Fuzzy Logic, where terms like cheap or near don’t have fixed and precise boundaries. The Challenge is designing systems that interpret vague language consistently while still giving useful, ranked results, not arbitrary ones. Natural language understanding at depth: flexible query systems increasingly rely on advances in Natural Language Processing byLarge Language Models and prompt engineering. Thus challenges include mapping natural language to formal queries (e.g., SQL or graph queries); understanding user’s intent, not just keywords; handling multi-turn or conversational queries. Foundation Large Language Models fine tuning: creating foundation LLMs requires both huge computational and infrastructural resources; thus their fine tuning for specific tasks is a challenging research issue, that requires building reliable, controllable, and explainable systems on top of Foundation LLMs, which are powerful but largely opaque and only partially customizable. Query relaxation and approximation: when exact answersdon’t exist, systems must relax constraints for example by dropping conditions, by broadening ranges, by suggesting near-matches. This connects to approximate query processing, whose core difficulty is to decide how to relax queries optimally without drifting too far from the user’s intent, how explaining why a result was returned despite not matching exactly. Ranking and relevance under uncertainty: flexible answers require ranking possible results rather than returning exact matches. Challenges include defining relevance when constraints are soft, combining multiple weak signals (ex. distance, price, ratings, preferences); avoiding bias or misleading rankings. This often blends machine learning with traditional IR (information retrieval) methods. Integration of heterogeneous data sources: modern systems pull from structured databases, Knowledge graphs, unstructured texts, multimodal information. Challenges are aligning schemas and meanings across sources and media, resolving conflicts and inconsistencies, querying across formats, media and genre. Explainability and trust: users increasingly expect to know why a result was returned, why it ranked higher than others. Flexible systems are especially tricky because the results may not strictly satisfy the query. The logic may involve heuristics or learned models. Open problems include generating clear, human-understandable explanations for approximate answers. Personalization vs. generalization: flexible query answering often depends on user context, preferences, location, history. There is a tension between personalization to improves relevance and the risks of overfitting, of violating privacy issues, and the lack of transparency. Efficiency and scalability: flexible queries are computationally expensive: relaxation creates large search spaces; ranking requires scoring many candidates. Thus challenges are maintaining real-time performance on large-scale systems. Neuro-symbolic integration: there is a growing interest in combining neural models (for language reproduction and interpretation) and symbolic systems (for structured querying and constraints, and query understanding supported by ontologies and domain knowledge). This challenges neuro-symbolic AI. The key difficulty is making these systems work together coherently, and preserving both flexibility and correctness. Evaluation and benchmarks: it’s hard to measure the success of a FQAS: what counts as a “good” approximate answer? How do you evaluate user satisfaction? Challenge include designing benchmarks that reflect real-world flexible querying, not just exact-match accuracy. Green and Sustainable FQAS: one main drawbacks of current Large Language Models is their huge demand and consumption of both energy and water; thus a challenge is designing Small Language Models and solutions that are energy-efficient and resource-aware. Predictive and Prescriptive Answers: current systems have been designed to describe the past and the present but not to anticipate the future by answering predictive queries (e.g., “what is likely to happen if…?” ) or prescriptive queries ( e.g., “what should I do if …?”). One challenge is designing intelligent advisors and decision engines able to yield reliable forecasts and optimization strategies.
최종 수정: Admin Agent ()

관련 학회

CCFICORECP-I약칭정식 명칭투고 마감개최일
AA*92.5SIGIRInternational Conference on Research and Development in Information Retrieval2026-01-152026-07-20
AA*97.7AAAIAAAI Conference on Artificial Intelligence2026-07-212027-02-16
AA*94.3CVPRIEEE Conference on Computer Vision and Pattern Recognition2026-11-102027-06-20
BA*89.7ICRAInternational Conference on Robotics and Automation2026-09-152027-05-24
BA*94.1IJCAIInternational Joint Conference on Artificial Intelligence2026-01-312026-08-15
AA*92.5STOCACM Symposium on Theory of Computing2026-11-022027-06-06
C87.4ICCInternational Conference on Communications2026-10-022027-05-30
CB62.7IJCNNInternational Joint Conference on Neural Networks2027-01-312027-06-14
B91.2ICASSPInternational Conference on Acoustics, Speech and Signal Processing2026-09-162027-05-16
BA*79.6PODSACM SIGMOD Conference on Principles of DB Systems2026-12-032027-06-13

관련 저널

CCF정식 명칭영향력 지수출판사ISSN
AIEEE Transactions on Multimedia9.7IEEE1520-9210
CKnowledge-Based Systems7.2Elsevier0950-7051
BSoftware & Systems Modeling3.2Springer1619-1366
AIEEE Transactions on Computers3.8IEEE0018-9340
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.9Elsevier0167-8655
BPattern Recognition7.6Elsevier0031-3203
IEEE Access3.6IEEE2169-3536
AIEEE Transactions on Dependable and Secure Computing7.5IEEE1545-5971

댓글 0

아직 댓글이 없습니다.

댓글을 작성하려면 로그인해 주세요