1,074 research outputs found
HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting
No full textJiaying Lu, Jiaming Shen, Bo Xiong, Wenjing Ma, Steffen Staab, and Carl Yan
Time-invariant degree growth in preferential attachment network models
No full textPreferential attachment drives the evolution of many complex networks. Its analytical studies mostly consider the simplest case of a network that grows uniformly in time despite the accelerating growth of many real networks. Motivated by the observation that the average degree growth of nodes is time invariant in empirical network data, we study the degree dynamics in the relevant class of network models where preferential attachment is combined with heterogeneous node fitness and aging. We propose an analytical framework based on the time invariance of the studied systems and show that it is self-consistent only for two special network growth forms: the uniform and the exponential network growth. Conversely, the breaking of such time invariance explains the winner-takes-all effect in some model settings, revealing the connection between the Bose-Einstein condensation in the Bianconi-Barabási model and similar gelation in superlinear preferential attachment. Aging is necessary to reproduce realistic node degree growth curves and can prevent the winner-takes-all effect under weak conditions. Our results are verified by extensive numerical simulations
Linked data querying through FCA-based schema indexing
No full textThe efficiency of SPARQL query evaluation against Linked Open Data may benefit from schema-based indexing. However, many data items come with incomplete schema information or lack schema descriptions entirely. In this position paper, we outline an approach to an indexing of linked data graphs based on schemata induced through Formal Concept Analysis. We show how to map queries onto RDF graphs based on such derived schema information. We sketch next steps for realizing and optimizing the suggested approach
Compressing and maintaining statistics information about resource occurrences in a distributed RDF store
No full textIn distributed RDF stores triples are assigned to one or several storage and compute nodes. In order to perform query planning and optimization, statistical information about the occurrences of IRIs and literals on the individual storage and compute nodes is needed. In this paper, we present our novel compressed storage format for statistical information that can be updated with a single read and write operation if resources occur on few storage and compute nodes only. In our experiments this novel storage format reduced the time to collect statistical information by up to 97% and the required space by up to 99%.</p
From tokens to lattices: emergent lattice structures in language models
No full textPretrained masked language models (MLMs) have demonstrated an impressive capability to comprehend and encode conceptual knowledge, revealing a lattice structure among concepts. This raises a critical question: how does this conceptualization emerge from MLM pretraining? In this paper, we explore this problem from the perspective of Formal Concept Analysis (FCA), a mathematical framework that derives concept lattices from the observations of object-attribute relationships. We show that the MLM's objective implicitly learns a formal context that describes objects, attributes, and their dependencies, which enables the reconstruction of a concept lattice through FCA. We propose a novel framework for concept lattice construction from pretrained MLMs and investigate the origin of the inductive biases of MLMs in lattice structure learning. Our framework differs from previous work because it does not rely on human-defined concepts and allows for discovering "latent" concepts that extend beyond human definitions. We create three datasets for evaluation, and the empirical results verify our hypothesis
Learning by Googling
No full textCimiano P, Staab S. Learning by Googling. SIGKDD Explorations. 2004;6(2):24-33
Learning Concept Hierarchies from Text Corpora using Formal Concept Anaylsis
No full textCimiano P, Hotho A, Staab S. Learning Concept Hierarchies from Text Corpora using Formal Concept Anaylsis. Journal of Artificial Intelligence Research (JAIR). 2005;24:305-339
Concepts in application context
No full textFormal concept analysis (FCA) derives a hierarchy of concepts in a formal context that relates objects with attributes. This approach is very well aligned with the traditions of Frege, Saussure and Peirce, which relate a signifier (e.g. a word/an attribute) to a mental concept evoked by this word and meant to refer to a specific object in the real world. However, in the practice of natural languages as well as artificial languages (e.g. programming languages), the application context often constitutes a latent variable that influences the interpretation of a signifier. We present some of our current work that analyzes the usage of words in natural language in varying application contexts as well as the usage of variables in programming languages in varying application contexts in order to provide conceptual constraints on these signifiers
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