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  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.04 
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    Abstract
    This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.
    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
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    p
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  2. Großjohann, K.: Gathering-, Harvesting-, Suchmaschinen (1996) 0.04 
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    Date
    7. 2.1996 22:38:41
    Pages
    22 S
    Type
    p
  3. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.03 
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    Abstract
    Logical rules are essential for uncovering the logical connections between relations, which could improve the reasoning performance and provide interpretable results on knowledge graphs (KGs). Although there have been many efforts to mine meaningful logical rules over KGs, existing methods suffer from the computationally intensive searches over the rule space and a lack of scalability for large-scale KGs. Besides, they often ignore the semantics of relations which is crucial for uncovering logical connections. Recently, large language models (LLMs) have shown impressive performance in the field of natural language processing and various applications, owing to their emergent ability and generalizability. In this paper, we propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs to prompt LLMs to generate logical rules. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs. Last, a rule validator harnesses the reasoning ability of LLMs to validate the logical correctness of ranked rules through chain-of-thought reasoning. ChatRule is evaluated on four large-scale KGs, w.r.t. different rule quality metrics and downstream tasks, showing the effectiveness and scalability of our method.
    Date
    23.11.2023 19:07:22
    Type
    p
  4. Wätjen, H.-J.: Mensch oder Maschine? : Auswahl und Erschließung vonm Informationsressourcen im Internet (1996) 0.03 
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    Date
    2. 2.1996 15:40:22
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  5. Bhattacharyya, G.: Classifying by UDC and CC: a comparative study (1972) 0.01 
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  6. Satija, M.P.: Classification and indexing in India : a state-of-the-art (1992) 0.01 
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  7. Stephan, W.: Guidelines for subject authority and reference entries (GSARE) : a first step to a worldwide accepted standard (1992) 0.01 
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  8. Seymour, C.: ¬A time to build : Israeli cataloging in transition (2000) 0.01 
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  9. Oberhauser, O.; Labner, J.: Einführung der automatischen Indexierung im Österreichischen Verbundkatalog? : Bericht über eine empirische Studie (2003) 0.01 
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  10. Yitzhaki, M.: ¬A draft version of a consolidated thesaurus for the rapidly growing field of alternative medicine (2000) 0.01 
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  11. Jaenecke, P.: Knowledge organization due to theory formation (1995) 0.01 
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    Abstract
    Theory formation is regarded as a process of domain-internal knowledge organization. Misunderstandings about the concept 'theory' are explained. A theory is considered as a systematical representation of a domain realized by three closely related theory-forming actions: establishment of a suitable system of basic concepts, ordering of the experience or given experimental results, synthesizing of conflicting hypotheses. In this view, theory formation means an ambitious kind of knowledge representation. Its consequences are summarized and its importance for the human sciences and for society is emphasized
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  12. Couture-Lafleur, R.: ¬The French translation of the Dewey Decimal Classification : The making of a DDC translation (1998) 0.01 
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  13. Balikova, M.: ¬The national bibliography of a small country in international context (2000) 0.01 
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  14. Broughton, V.: ¬A new classification for the literature for religion (2000) 0.01 
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  15. Elazar, D.H.: ¬The making of a classification scheme for libraries of Judaica (2000) 0.01 
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  16. Stoklasova, B.: ¬The national bibliography of a small country in international context (2000) 0.01 
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  17. Schleifenbaum, P.: DK im Südwestverbund (SWB) : Vortragsmanuskript (1991) 0.01 
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  18. Lund, B.D.: ¬A chat with ChatGPT : how will AI impact scholarly publishing? (2022) 0.01 
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    Abstract
    This is a short project that serves as an inspiration for a forthcoming paper, which will explore the technical side of ChatGPT and the ethical issues it presents for academic researchers, which will result in a peer-reviewed publication. This demonstrates that capacities of ChatGPT as a "chatbot" that is far more advanced than many alternatives available today and may even be able to be used to draft entire academic manuscripts for researchers. ChatGPT is available via https://chat.openai.com/chat.
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  19. Green, R.: Relationships in the Dewey Decimal Classification (DDC) : plan of study (2008) 0.01 
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    Abstract
    EPC Exhibit 129-36.1 presented intermediate results of a project to connect Relative Index terms to topics associated with classes and to determine if those Relative Index terms approximated the whole of the corresponding class or were in standing room in the class. The Relative Index project constitutes the first stage of a long(er)-term project to instill a more systematic treatment of relationships within the DDC. The present exhibit sets out a plan of study for that long-term project.
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  20. Jansen, B.; Browne, G.M.: Navigating information spaces : index / mind map / topic map? (2021) 0.01 
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    Abstract
    This paper discusses the use of wiki technology to provide a navigation structure for a collection of newspaper clippings. We overview the architecture of the wiki, discuss the navigation structure and pose the question: is the navigation structure an index, and if so, what type, or is it just a linkage structure or topic map. Does such a distinction really matter? Are these definitions in reality function based?
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Authors

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  • e 65
  • d 29

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