-
Deng, Z.; Deng, Z.; Fan, G.; Wang, B.; Fan, W.(P.); Liu, S.: More is better? : understanding the effects of online interactions on patients health anxiety (2023)
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- Abstract
- Online health platforms play an important role in chronic disease management. Patients participate in online health platforms to receive and provide health-related support from each other. However, there remains a debate about whether the influence of social interaction on patient health anxiety is linearly positive. Based on uncertainty, information overload, and the theory of motivational information management, we develop and test a model considering a potential curvilinear relationship between social interaction and health anxiety, as well as a moderating effect of health literacy. We collect patient interaction data from an online health platform based on chronic disease management in China and use text mining and econometrics to test our hypotheses. Specifically, we find an inverted U-shaped relationship between informational provision and health anxiety. Our results also show that information receipt and emotion provision have U-shaped relationships with health anxiety. Interestingly, health literacy can effectively alleviate the U-shaped relationship between information receipt and health anxiety. These findings not only provide new insights into the literature on online patient interactions but also provide decision support for patients and platform managers.
- Type
- a
-
Brembs, B.; Förstner, K.; Kraker, P.; Lauer, G.; Müller-Birn, C.; Schönbrodt, F.; Siems, R.: Auf einmal Laborratte (2021)
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- Type
- a
-
Zilm, G.: "Kl ist ein glorifizierter Taschenrechner" (2023)
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- Date
- 27. 1.2023 16:22:55
- Type
- a
-
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)
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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
-
Butlin, P.; Long, R.; Elmoznino, E.; Bengio, Y.; Birch, J.; Constant, A.; Deane, G.; Fleming, S.M.; Frith, C.; Ji, X.; Kanai, R.; Klein, C.; Lindsay, G.; Michel, M.; Mudrik, L.; Peters, M.A.K.; Schwitzgebel, E.; Simon, J.; VanRullen, R.: Consciousness in artificial intelligence : insights from the science of consciousness (2023)
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- Abstract
- Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing AI systems in detail, in light of our best-supported neuroscientific theories of consciousness. We survey several prominent scientific theories of consciousness, including recurrent processing theory, global workspace theory, higher-order theories, predictive processing, and attention schema theory. From these theories we derive "indicator properties" of consciousness, elucidated in computational terms that allow us to assess AI systems for these properties. We use these indicator properties to assess several recent AI systems, and we discuss how future systems might implement them. Our analysis suggests that no current AI systems are conscious, but also suggests that there are no obvious technical barriers to building AI systems which satisfy these indicators.
- Type
- a
-
Zhu, L.; Xu, A.; Deng, S.; Heng, G.; Li, X.: Entity management using Wikidata for cultural heritage information (2024)
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- Abstract
- Entity management in a Linked Open Data (LOD) environment is a process of associating a unique, persistent, and dereferenceable Uniform Resource Identifier (URI) with a single entity. It allows data from various sources to be reused and connected to the Web. It can help improve data quality and enable more efficient workflows. This article describes a semi-automated entity management project conducted by the "Wikidata: WikiProject Chinese Culture and Heritage Group," explores the challenges and opportunities in describing Chinese women poets and historical places in Wikidata, the largest crowdsourcing LOD platform in the world, and discusses lessons learned and future opportunities.
- Source
- Cataloging and classification quarterly. 61(2023) no.1, p.20-46
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- a
-
Hindrichs, G.: Kriegszivilgesellschaft (2022)
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-
Noever, D.; Ciolino, M.: ¬The Turing deception (2022)
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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
- Type
- p
a
-
Liang, Z.; Mao, J.; Li, G.: Bias against scientific novelty : a prepublication perspective (2023)
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- Abstract
- Novel ideas often experience resistance from incumbent forces. While evidence of the bias against novelty has been widely identified in science, there is still a lack of large-scale quantitative work to study this problem occurring in the prepublication process of manuscripts. This paper examines the association between manuscript novelty and handling time of publication based on 778,345 articles in 1,159 journals indexed by PubMed. Measuring the novelty as the extent to which manuscripts disrupt existing knowledge, we found systematic evidence that higher novelty is associated with longer handling time. Matching and fixed-effect models were adopted to confirm the statistical significance of this pattern. Moreover, submissions from prestigious authors and institutions have the advantage of shorter handling time, but this advantage is diminishing as manuscript novelty increases. In addition, we found longer handling time is negatively related to the impact of manuscripts, while the relationships between novelty and 3- and 5-year citations are U-shape. This study expands the existing knowledge of the novelty bias by examining its existence in the prepublication process of manuscripts.
- Type
- a
-
Jansen, B.; Browne, G.M.: Navigating information spaces : index / mind map / topic map? (2021)
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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?
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
- Type
- p
-
P-L-U-R-V : das sind die häufigsten Methoden der Desinformation. Neue Infografik im Posterformat (2020)
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- Content
- Vgl. auch: https://www.klimafakten.de/meldung/p-l-u-r-v-dies-sind-die-haeufigsten-desinformations-tricks-von-wissenschafts-leugnern.
- Source
- https://www.klimafakten.de/meldung/p-l-u-r-v-das-sind-die-haeufigsten-methoden-der-desinformation-neue-infografik-im
-
Daquino, M.; Peroni, S.; Shotton, D.; Colavizza, G.; Ghavimi, B.; Lauscher, A.; Mayr, P.; Romanello, M.; Zumstein, P.: ¬The OpenCitations Data Model (2020)
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- Abstract
- A variety of schemas and ontologies are currently used for the machine-readable description of bibliographic entities and citations. This diversity, and the reuse of the same ontology terms with different nuances, generates inconsistencies in data. Adoption of a single data model would facilitate data integration tasks regardless of the data supplier or context application. In this paper we present the OpenCitations Data Model (OCDM), a generic data model for describing bibliographic entities and citations, developed using Semantic Web technologies. We also evaluate the effective reusability of OCDM according to ontology evaluation practices, mention existing users of OCDM, and discuss the use and impact of OCDM in the wider open science community.
- Type
- a
-
Dogtas, G.; Ibitz, M.-P.; Jonitz, F.; Kocher, V.; Poyer, A.,; Stapf, L.: Kritik an rassifizierenden und diskriminierenden Titeln und Metadaten : Praxisorientierte Lösungsansätze (2022)
0.03
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- a
-
Heng, G.; Cole, T.W.; Tian, T.(C.); Han, M.-J.: Rethinking authority reconciliation process (2022)
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- Abstract
- Entity identity management and name reconciliation are intrinsic to both Linked Open Data (LOD) and traditional library authority control. Does this mean that LOD sources can facilitate authority control? This Emblematica Online case study examines the utility of five LOD sources for name reconciliation, comparing design differences regarding ontologies, linking models, and entity properties. It explores the challenges of name reconciliation in the LOD environment and provides lessons learned during a semi-automated name reconciliation process. It also briefly discusses the potential values and benefits of LOD authorities to the authority reconciliation process itself and library services in general.
- Source
- Cataloging and classification quarterly. 60(2022) no.1, p.45-68
- Type
- a
-
Candela, G.: ¬An automatic data quality approach to assess semantic data from cultural heritage institutions (2023)
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- Abstract
- In recent years, cultural heritage institutions have been exploring the benefits of applying Linked Open Data to their catalogs and digital materials. Innovative and creative methods have emerged to publish and reuse digital contents to promote computational access, such as the concepts of Labs and Collections as Data. Data quality has become a requirement for researchers and training methods based on artificial intelligence and machine learning. This article explores how the quality of Linked Open Data made available by cultural heritage institutions can be automatically assessed. The results obtained can be useful for other institutions who wish to publish and assess their collections.
- Date
- 22. 6.2023 18:23:31
- Type
- a
-
Kudlow, P.; Dziadyk, D.B.; Rutledge, A.; Shachak, A.; Eysenbach, G.: ¬The citation advantage of promoted articles in a cross-publisher distribution platform : a 12-month randomized controlled trial (2020)
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- Abstract
- There is currently a paucity of evidence-based strategies that have been shown to increase citations of peer-reviewed articles following their publication. We conducted a 12-month randomized controlled trial to examine whether the promotion of article links in an online cross-publisher distribution platform (TrendMD) affects citations. In all, 3,200 articles published in 64 peer-reviewed journals across eight subject areas were block randomized at the subject level to either the TrendMD group (n = 1,600) or the control group (n = 1,600) of the study. Our primary outcome compares the mean citations of articles randomized to TrendMD versus control after 12 months. Articles randomized to TrendMD showed a 50% increase in mean citations relative to control at 12 months. The difference in mean citations at 12 months for articles randomized to TrendMD versus control was 5.06, 95% confidence interval [2.87, 7.25], was statistically significant (p?<?.001) and found in three of eight subject areas. At 6 months following publication, articles randomized to TrendMD showed a smaller, yet statistically significant (p = .005), 21% increase in mean citations, relative to control. To our knowledge, this is the first randomized controlled trial to demonstrate how an intervention can be used to increase citations of peer-reviewed articles after they have been published.
- Type
- a
-
Geras, A.; Siudem, G.; Gagolewski, M.: Should we introduce a dislike button for academic articles? (2020)
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- Abstract
- There is a mutual resemblance between the behavior of users of the Stack Exchange and the dynamics of the citations accumulation process in the scientific community, which enabled us to tackle the outwardly intractable problem of assessing the impact of introducing "negative" citations. Although the most frequent reason to cite an article is to highlight the connection between the 2 publications, researchers sometimes mention an earlier work to cast a negative light. While computing citation-based scores, for instance, the h-index, information about the reason why an article was mentioned is neglected. Therefore, it can be questioned whether these indices describe scientific achievements accurately. In this article we shed insight into the problem of "negative" citations, analyzing data from Stack Exchange and, to draw more universal conclusions, we derive an approximation of citations scores. Here we show that the quantified influence of introducing negative citations is of lesser importance and that they could be used as an indicator of where the attention of the scientific community is allocated.
- Date
- 6. 1.2020 18:10:22
- Type
- a
-
Safder, I.; Ali, M.; Aljohani, N.R.; Nawaz, R.; Hassan, S.-U.: Neural machine translation for in-text citation classification (2023)
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- Abstract
- The quality of scientific publications can be measured by quantitative indices such as the h-index, Source Normalized Impact per Paper, or g-index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full-text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning-based architecture for citation context classification. Unlike feature-based state-of-the-art models, our proposed focal-loss and class-weight-aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state-of-the-art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings.
- Type
- a
-
Ruotsalo, T.; Jacucci, G.; Kaski, S.: Interactive faceted query suggestion for exploratory search : whole-session effectiveness and interaction engagement (2020)
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- Abstract
- The outcome of exploratory information retrieval is not only dependent on the effectiveness of individual responses to a set of queries, but also on relevant information retrieved during the entire exploratory search session. We study the effect of search assistance, operationalized as an interactive faceted query suggestion, for both whole-session effectiveness and engagement through interactive faceted query suggestion. A user experiment is reported, where users performed exploratory search tasks, comparing interactive faceted query suggestion and a control condition with only conventional typed-query interaction. Data comprised of interaction and search logs show that the availability of interactive faceted query suggestion substantially improves whole-session effectiveness by increasing recall without sacrificing precision. The increased engagement with interactive faceted query suggestion is targeted to direct situated navigation around the initial query scope, but is not found to improve individual queries on average. The results imply that research in exploratory search should focus on measuring and designing tools that engage users with directed situated navigation support for improving whole-session performance.
- Theme
- Semantisches Umfeld in Indexierung u. Retrieval
- Type
- a
-
Li, G.; Siddharth, L.; Luo, J.: Embedding knowledge graph of patent metadata to measure knowledge proximity (2023)
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- Abstract
- Knowledge proximity refers to the strength of association between any two entities in a structural form that embodies certain aspects of a knowledge base. In this work, we operationalize knowledge proximity within the context of the US Patent Database (knowledge base) using a knowledge graph (structural form) named "PatNet" built using patent metadata, including citations, inventors, assignees, and domain classifications. We train various graph embedding models using PatNet to obtain the embeddings of entities and relations. The cosine similarity between the corresponding (or transformed) embeddings of entities denotes the knowledge proximity between these. We compare the embedding models in terms of their performances in predicting target entities and explaining domain expansion profiles of inventors and assignees. We then apply the embeddings of the best-preferred model to associate homogeneous (e.g., patent-patent) and heterogeneous (e.g., inventor-assignee) pairs of entities.
- Date
- 22. 3.2023 12:06:55
- Type
- a
