Tab2Know: Building a Knowledge Base from Tables in Scientific Papers

Kruit, Benno; He, Hongyu; Urbani, Jacopo

doi:10.1007/978-3-030-62419-4_20

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…Then, EnergAt attributes energy to the user application and reports corresponding energy credits to the Power Manager of the cluster, informing the application owner of the energy consumption ( 3 ). The precise energy credits can be used to construct provenance graphs [33,43,44] ( 4 ) for tracing the power relationships among deployed applications. Such graph representations can be leveraged to train high-quality ML models that facilitate power management.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy

Friedman

Ρεκατσίνας³

2023

Proceedings of the 2nd Workshop on Sustainable Computer Systems

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In the post-Moore's Law era, relying solely on hardware advancements for automatic performance gains is no longer feasible without increased energy consumption, due to the end of Dennard scaling. Consequently, computing accounts for an increasing amount of global energy usage, contradicting the objective of sustainable computing. The lack of hardware support and the absence of a standardized, software-centric method for the precise tracing of energy provenance exacerbates the issue. Aiming to overcome this challenge, we argue that fine-grained software energy attribution is attainable, even with limited hardware support. To support our position, we present a thread-level, NUMA-aware energy attribution method for CPU and DRAM in multi-tenant environments. The evaluation of our prototype implementation, EnergAt, demonstrates the validity, effectiveness, and robustness of our theoretical model, even in the presence of the noisy-neighbor effect. We envisage a sustainable cloud environment and emphasize the importance of collective efforts to improve software energy efficiency.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%

EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy

Friedman

Ρεκατσίνας³

2023

Proceedings of the 2nd Workshop on Sustainable Computer Systems

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“…A second use is that of goal-driven query answering, i.e., deriving the knowledge specific to a given query only, using database techniques such as magic sets and subsumptive tabling [8,9,13,55]. Beyond knowledge exploration, other applications of materialization are data wrangling [35], entity resolution [37], data exchange [26] and query answering over OWL [44] and RDFS [16] ontologies. Finally, materialization has been also used in probabilistic KBs [56].…”

Section: Introductionmentioning

confidence: 99%

Materializing Knowledge Bases via Trigger Graphs

Tsamoura¹,

Carral²,

Malizia³

et al. 2021

Preprint

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The chase is a well-established family of algorithms used to materialize Knowledge Bases (KBs), like Knowledge Graphs (KGs), to tackle important tasks like query answering under dependencies or data cleaning. A general problem of chase algorithms is that they might perform redundant computations. To counter this problem, we introduce the notion of Trigger Graphs (TGs), which guide the execution of the rules avoiding redundant computations. We present the results of an extensive theoretical and empirical study that seeks to answer when and how TGs can be computed and what are the benefits of TGs when applied over real-world KBs. Our results include introducing algorithms that compute (minimal) TGs. We implemented our approach in a new engine, and our experiments show that it can be significantly more efficient than the chase enabling us to materialize KBs with 17B facts in less than 40 min on commodity machines.

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“…Fang et al [93] x x x Kruit et al [159] x x Gao et al [110] x x x Gao et al [108] x x x x x Ramanathan et al [226] x x x Wu et al [293] x x x Tkaczyk et al [265] x x x x x Wu et al [290] x x x x x Bast & Korzen [26] x x x x Tuarob et al [269,270] x x x x Larrañaga et al [165] x x Wali et al [280] x Lopes et al [171,172] x Dwarakanath et al [82] x Wang et al [284,285] x x Mihalcea & Csomai [195] x x Medelyan et al [189] x x Milne & Witten [197] x x Mendes et al [192] / Daiber et al [63] x x Moro et al [204] x x Zhu & Iglesias [302] x x Aghaebrahimian & Cielieback [5] x x…”

Section: Approachesmentioning

confidence: 99%

“…There are nine approaches in this cluster, which is identified with the color. The approach by Kruit et al [159] is a particular case. Even though they perform entity linking, the recognized entities are local from the extracted table data.…”

Section: Approachesmentioning

confidence: 99%

“…For example, Zhou et al [301] implemented a system to recognize semi-structured patient records and extract three types of information (numeric values, medical terms, and categorical values). In a more recent approach, Kruit et al [159] described a 3-step pipeline to build a Knowledge Base (KB) from tables in scientific publications. Due to the diversity of knowledge extraction approaches and their scopes, they can be compared along several axes: the types of textual resources used as input, the different applied methods or techniques, the specific objects that are recognized and extracted, the enrichment of the gathered knowledge, the used knowledge representation mechanisms, among others.…”

Section: Introductionmentioning

confidence: 99%

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Extraction of Knowledge Models from Textbooks

Chacón¹

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Many adaptive educational systems and other artificial intelligence applications rely on high-quality knowledge representations. Still, knowledge acquisition remains the primary bottleneck hindering large-scale deployment and adoption of knowledge-based systems. One path to scalable knowledge extraction is using digital textbooks, given their domain-oriented content, structure, and availability. This dissertation presents a unified approach for automatically extracting high-quality and domain-specific knowledge models from digital textbooks. The proposed approach leverages the authors’ knowledge encoded in the textbooks’ elements that facilitate navigation and understanding of the material (table of contents, index, formatting styles) to create knowledge models. The proposed workflow first extracts initial information elements from the textbooks: the structure of chapters and subchapters using the Table of Content, the content of each section, and domain terminology from the back-of-the-book index. Then, new information is added: domain terms are linked to external entities in a knowledge graph (DBpedia) and are enriched with semantic content (e.g., abstracts and categories). Finally, the knowledge about the domain is refined by identifying the relevance of concepts to the target domain. The extracted knowledge is represented in a model using the Text Encoding Initiative. Multiple evaluations show that the extracted knowledge models have high levels of quality across several properties: accuracy, semantics, coverage, specificity, cognitive validity, and granularity. Additionally, the approach is effective in multiple domains—for example, statistics, ancient philosophy, and Python programming. Finally, there are many potential applications for the extracted knowledge models. This dissertation presents three different educational systems supported by the knowledge models.

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Tab2Know: Building a Knowledge Base from Tables in Scientific Papers

Cited by 6 publications

References 40 publications

EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy

EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy

Materializing Knowledge Bases via Trigger Graphs

Extraction of Knowledge Models from Textbooks

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