322,924 research outputs found
LM-KBC 2023: 2<sup>nd</sup> Challenge on Knowledge Base Construction from Pre-trained Language Models
No full textLarge language models (LLMs) like chatGPT [1] have advanced a range of semantic tasks and are being ubiquitously used for knowledge extraction. Although several works have explored this ability by crafting prompts with in-context or instruction learning, the viability of complete and precise knowledge base construction from LMs is still in its nascent form. In the 2nd edition of this challenge, we invited participants to extract disambiguated knowledge triples from LMs for a given set of subjects and relations. In crucial difference to existing probing benchmarks like LAMA [2], we made no simplifying assumptions on relation cardinalities, i.e., a subject-entity can stand in relation with zero, one, or many object-entities. Furthermore, submissions needed to go beyond just ranking predicted surface strings, and materialize disambiguated entities in the output, which were evaluated using established KB metrics of precision, recall, and F1-score. The challenge had two tracks: (1) a small model track, where models with < 1 billion parameters could be probed, and (2) an open track, where participants could use any LM of their choice. We received seven submissions, two for track 1 and five for track 2. We present the contributions and insights of the submitted peer-reviewed submissions and lay out the possible paths for future work. All the details related to the challenge can be found on our website at https://lm-kbc.github.io/challenge2023/.</p
LM-KBC 2023: 2<sup>nd</sup> Challenge on Knowledge Base Construction from Pre-trained Language Models
No full textLarge language models (LLMs) like chatGPT [1] have advanced a range of semantic tasks and are being ubiquitously used for knowledge extraction. Although several works have explored this ability by crafting prompts with in-context or instruction learning, the viability of complete and precise knowledge base construction from LMs is still in its nascent form. In the 2nd edition of this challenge, we invited participants to extract disambiguated knowledge triples from LMs for a given set of subjects and relations. In crucial difference to existing probing benchmarks like LAMA [2], we made no simplifying assumptions on relation cardinalities, i.e., a subject-entity can stand in relation with zero, one, or many object-entities. Furthermore, submissions needed to go beyond just ranking predicted surface strings, and materialize disambiguated entities in the output, which were evaluated using established KB metrics of precision, recall, and F1-score. The challenge had two tracks: (1) a small model track, where models with < 1 billion parameters could be probed, and (2) an open track, where participants could use any LM of their choice. We received seven submissions, two for track 1 and five for track 2. We present the contributions and insights of the submitted peer-reviewed submissions and lay out the possible paths for future work. All the details related to the challenge can be found on our website at https://lm-kbc.github.io/challenge2023/.</p
LM-KBC 2023: 2<sup>nd</sup> Challenge on Knowledge Base Construction from Pre-trained Language Models
No full textLarge language models (LLMs) like chatGPT [1] have advanced a range of semantic tasks and are being ubiquitously used for knowledge extraction. Although several works have explored this ability by crafting prompts with in-context or instruction learning, the viability of complete and precise knowledge base construction from LMs is still in its nascent form. In the 2nd edition of this challenge, we invited participants to extract disambiguated knowledge triples from LMs for a given set of subjects and relations. In crucial difference to existing probing benchmarks like LAMA [2], we made no simplifying assumptions on relation cardinalities, i.e., a subject-entity can stand in relation with zero, one, or many object-entities. Furthermore, submissions needed to go beyond just ranking predicted surface strings, and materialize disambiguated entities in the output, which were evaluated using established KB metrics of precision, recall, and F1-score. The challenge had two tracks: (1) a small model track, where models with < 1 billion parameters could be probed, and (2) an open track, where participants could use any LM of their choice. We received seven submissions, two for track 1 and five for track 2. We present the contributions and insights of the submitted peer-reviewed submissions and lay out the possible paths for future work. All the details related to the challenge can be found on our website at https://lm-kbc.github.io/challenge2023/.</p
LM-KBC 2023: 2<sup>nd</sup> Challenge on Knowledge Base Construction from Pre-trained Language Models
No full textLarge language models (LLMs) like chatGPT [1] have advanced a range of semantic tasks and are being ubiquitously used for knowledge extraction. Although several works have explored this ability by crafting prompts with in-context or instruction learning, the viability of complete and precise knowledge base construction from LMs is still in its nascent form. In the 2nd edition of this challenge, we invited participants to extract disambiguated knowledge triples from LMs for a given set of subjects and relations. In crucial difference to existing probing benchmarks like LAMA [2], we made no simplifying assumptions on relation cardinalities, i.e., a subject-entity can stand in relation with zero, one, or many object-entities. Furthermore, submissions needed to go beyond just ranking predicted surface strings, and materialize disambiguated entities in the output, which were evaluated using established KB metrics of precision, recall, and F1-score. The challenge had two tracks: (1) a small model track, where models with < 1 billion parameters could be probed, and (2) an open track, where participants could use any LM of their choice. We received seven submissions, two for track 1 and five for track 2. We present the contributions and insights of the submitted peer-reviewed submissions and lay out the possible paths for future work. All the details related to the challenge can be found on our website at https://lm-kbc.github.io/challenge2023/.</p
Completeness statements about RDF data sources and their use for query answering
No full textWith thousands of RDF data sources available on the Web covering disparate and possibly overlapping knowledge domains, the problem of providing high-level descriptions (in the form of metadata) of their content becomes crucial. In this paper we introduce a theoretical framework for describing data sources in terms of their completeness. We show how existing data sources can be described with completeness statements expressed in RDF. We then focus on the problem of the completeness of query answering over plain and RDFS data sources augmented with completeness statements. Finally, we present an extension of the completeness framework for federated data sources. © 2013 Springer-Verlag
Completeness Management for RDF Data Sources
No full textThe Semantic Web is commonly interpreted under the open-world assumption meaning that information available (e.g., in a data source) only captures a subset of the reality. Therefore, there is no certainty about whether the available information provides a complete representation of the reality. The broad aim of this paper is to contribute a formal study of how to describe the completeness of parts of the Semantic Web stored in RDF data sources. We introduce a theoretical framework allowing to augment RDF data sources with statements, also expressed in RDF, about their completeness. One immediate benefit of this framework is that now query answers can be complemented with information about their completeness. We study the impact of completeness statements on the complexity of query answering by considering different fragments of the SPARQL language, including the RDFS entailment regime, and the federated scenario. We implement an efficient method for reasoning about query completeness and provide an experimental evaluation in the presence of large sets of completeness statements
Diffusive author(s), cohesive author: Analysis of S/N (1994)
No full textThis study indicates the ways in which various aspects of the author(s) are brought forth in Dumb type’s performance art, the S/N production. Previous research has suggested a non-hierarchical organization of Dumb type and the absence of a “privileged author” in Dumb type’s collaborative work, S/N. However, the results that I have investigated from member’s interviews on the creative process of S/N along with my analysis of the recorded images of S/N, indicate a different aspect of the author(s). First, S/N was created through, so to speak, the collective ideas of the members of Dumb type. Further, S/N has at least nine quotations from previous performances, installations, and printed writings, besides the work-in-progress technique. Explicating one of the “author functions” as given by Michel Foucault, each text has plural subjects of the author. However, it has been revealed from members’ interviews that Teiji Furuhashi had a decision-making role in selecting the members’ ideas within the performance. Since then, S/N has had plural subjects of creation; however, Furuhashi is one of the subjects of creation along with the “privileged author.” S/N has plural authors (diffusive authors) yet at the same time, it has a “privileged author,” Teiji Furuhashi (cohesive author)
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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