Query Languages for Machine-Learning Models
In this paper, I discuss two logics for weighted finite structures: first-order logic with summation (FO(SUM)) and its recursive extension IFP(SUM). These logics originate from foundational work by Grädel, Gurevich, and Meer in the 1990s. In recent joint work with Standke, Steegmans, and Van den Bussche, we have investigated these logics as query languages for machine learning models, specifically neural networks, which are naturally represented as weighted graphs. I present illustrative examples of queries to neural networks that can be expressed in these logics and discuss fundamental results on their expressiveness and computational complexity.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain
We present a thorough comparison of two principal approaches to Cross-Lingual Information Retrieval: document translation (DT) and query translation (QT). Our experiments are conducted using the cross-lingual test collec…
Cross-Lingual Information RetrievalDocument TranslationInformation RetrievalMachine Translation+3Towards Universal Languages for Tractable Ontology Mediated Query Answering
An ontology language for ontology mediated query answering (OMQA-language) is universal for a family of OMQA-languages if it is the most expressive one among this family. In this paper, we focus on three families of trac…
Evaluating Query Languages for a Corpus Processing System
This paper documents a pilot study conducted as part of the development of a new corpus processing system at the Institut f{\"u}r Deutsche Sprache in Mannheim and in the context of the ISO TC37 SC4/WG6 activity on the su…
Labeling of Query Words using Conditional Random Field
This paper describes our approach on Query Word Labeling as an attempt in the shared task on Mixed Script Information Retrieval at Forum for Information Retrieval Evaluation (FIRE) 2015. The query is written in Roman scr…
Information RetrievalLanguage IdentificationRetrievalSM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmark
Electronic health records (EHRs) are stored in various database systems with different database models on heterogeneous storage architectures, such as relational databases, document stores, or graph databases. These diff…
In-Context Learning