Research
I do not have a lot of publications yet, but I hope that the ones I have right now are of interest for you. If you have any questions or are interested in commenting my work I will be happy to receive any comments.
1.
Font-Julian, Cristina I.; Orduña-Malea, Enrique; Codina, Lluís
ChatGPT Search as a tool for scholarly tasks: evolution or devolution? Journal Article
In: Infonomy, vol. 2, no. 5, pp. 15, 2024, ISSN: 2990-2290.
Abstract | Links | BibTeX | Tags: A-GEO, Academic Generative Engine Optimization, Academic Tasks, AI, ChatGPT, ChatGPT Search, Generative Artificial Intelligente, Information Retrieval, Link Analysis, Narrative Synthesis, Quantitative vs Qualitative, Scholarly Tasks, Search Engines, Web Search
@article{chatgpt-search,
title = {ChatGPT Search as a tool for scholarly tasks: evolution or devolution?},
author = {Cristina I. Font-Julian and Enrique Orduña-Malea and Lluís Codina},
doi = { https://doi.org/10.3145/infonomy.24.059},
issn = {2990-2290},
year = {2024},
date = {2024-11-26},
journal = {Infonomy},
volume = {2},
number = {5},
pages = {15},
abstract = {ChatGPT Search was launched on October 31 by OpenAI as a new AI-powered search engine. Among its features, it stands out for its ability to retrieve information from various online sources, including scholarly databases, which potentially allows the use of this tool for academic tasks, both quantitative and qualitative. To test its features, five academic tasks are designed: two quantitative (collecting hit count estimates from Google Search and scraping bibliometric indicators from ResearchGate); two qualitative tasks (performing a narrative synthesis of an academic topic and generating a brief academic author profile), and a mixed task (identifying, collecting and describing a list of publications from Google Scholar Profiles). The results show the inability of ChatGPT Search to conduct quantitative tasks correctly, fabricating the results (hallucination). Qualitative tasks are performed with better results; however, errors are detected, which prevent recommending the tool without manual analysis and refinement. Finally, the ability to generate links to scientific publications can open up competition among academic sites to be mentioned in the ChatGPT Search responses, giving rise to Academic Generative Engine Optimization (A-GEO).},
keywords = {A-GEO, Academic Generative Engine Optimization, Academic Tasks, AI, ChatGPT, ChatGPT Search, Generative Artificial Intelligente, Information Retrieval, Link Analysis, Narrative Synthesis, Quantitative vs Qualitative, Scholarly Tasks, Search Engines, Web Search},
pubstate = {published},
tppubtype = {article}
}
ChatGPT Search was launched on October 31 by OpenAI as a new AI-powered search engine. Among its features, it stands out for its ability to retrieve information from various online sources, including scholarly databases, which potentially allows the use of this tool for academic tasks, both quantitative and qualitative. To test its features, five academic tasks are designed: two quantitative (collecting hit count estimates from Google Search and scraping bibliometric indicators from ResearchGate); two qualitative tasks (performing a narrative synthesis of an academic topic and generating a brief academic author profile), and a mixed task (identifying, collecting and describing a list of publications from Google Scholar Profiles). The results show the inability of ChatGPT Search to conduct quantitative tasks correctly, fabricating the results (hallucination). Qualitative tasks are performed with better results; however, errors are detected, which prevent recommending the tool without manual analysis and refinement. Finally, the ability to generate links to scientific publications can open up competition among academic sites to be mentioned in the ChatGPT Search responses, giving rise to Academic Generative Engine Optimization (A-GEO).
2.
Orduña-Malea, Enrique; Font-Julian, Cristina I.
Are patents linked on Twitter? A case study of Google patents Journal Article
In: Scientometrics, vol. 127, pp. 6339–6362, 2022.
Abstract | Links | BibTeX | Tags: Altmetrics, Link Analysis, Patent, Social Media Metrics, Social Patentometrics, Twitter
@article{nokey,
title = {Are patents linked on Twitter? A case study of Google patents},
author = {Enrique Orduña-Malea and Cristina I. Font-Julian},
doi = {https://doi.org/10.1007/s11192-022-04519-y},
year = {2022},
date = {2022-10-10},
urldate = {2022-10-10},
journal = {Scientometrics},
volume = {127},
pages = {6339–6362},
abstract = {This study attempts to analyze patents as cited/mentioned documents to better understand the interest, dissemination and engagement of these documents in social environments, laying the foundations for social media studies of patents (social Patentometrics).Particularly, this study aims to determine how patents are disseminated on Twitter by analyzing three elements: tweets linking to patents, users linking to patents, and patents linked from Twitter. To do this, all the tweets containing at least one link to a full-text patent available on Google Patents were collected and analyzed, yielding a total of 126,815 tweets (and 129,001 links) to 86,417 patents. The results evidence an increase of the number of linking tweets over the years, presumably due to the creation of a standardized patent URL ID and the integration of Google Patents and Google Scholar, which took place in 2015. The engagement achieved by these tweets is limited (80.2% of tweets did not attract likes) but increasing notably since 2018. Two super-publisher twitter bot accounts (dailypatent and uspatentbot) are responsible of 53.3% of all the linking tweets, while most accounts are sporadic users linking to patent as part of a conversation. The patents most tweeted are, by far, from United States (87.5% of all links to Google Patents), mainly due to the effect of the two super-publishers. The impact of patents in terms of the number of tweets linking to them is unrelated to their year of publication, status or number of patent citations received, while controversial and media topics might be more determinant factors. However, further research is needed to better understand the topics discussed around patents on Twitter, the users involved, and the metrics attained. Given the increasing number of linking users and linked patents, this study finds Twitter as a relevant source to measure patent-level metrics, shedding light on the impact and interest of patents by the broad public.},
keywords = {Altmetrics, Link Analysis, Patent, Social Media Metrics, Social Patentometrics, Twitter},
pubstate = {published},
tppubtype = {article}
}
This study attempts to analyze patents as cited/mentioned documents to better understand the interest, dissemination and engagement of these documents in social environments, laying the foundations for social media studies of patents (social Patentometrics).Particularly, this study aims to determine how patents are disseminated on Twitter by analyzing three elements: tweets linking to patents, users linking to patents, and patents linked from Twitter. To do this, all the tweets containing at least one link to a full-text patent available on Google Patents were collected and analyzed, yielding a total of 126,815 tweets (and 129,001 links) to 86,417 patents. The results evidence an increase of the number of linking tweets over the years, presumably due to the creation of a standardized patent URL ID and the integration of Google Patents and Google Scholar, which took place in 2015. The engagement achieved by these tweets is limited (80.2% of tweets did not attract likes) but increasing notably since 2018. Two super-publisher twitter bot accounts (dailypatent and uspatentbot) are responsible of 53.3% of all the linking tweets, while most accounts are sporadic users linking to patent as part of a conversation. The patents most tweeted are, by far, from United States (87.5% of all links to Google Patents), mainly due to the effect of the two super-publishers. The impact of patents in terms of the number of tweets linking to them is unrelated to their year of publication, status or number of patent citations received, while controversial and media topics might be more determinant factors. However, further research is needed to better understand the topics discussed around patents on Twitter, the users involved, and the metrics attained. Given the increasing number of linking users and linked patents, this study finds Twitter as a relevant source to measure patent-level metrics, shedding light on the impact and interest of patents by the broad public.
3.
Font-Julian, Cristina I.; Ontalba-Ruipérez, José-Antonio; Orduña-Malea, Enrique; Thelwall, Mike
Which types of online resource support US patent claims? Journal Article
In: Journal of Informetrics, vol. 16, iss. 2022, no. 101247, pp. 14, 2022, ISSN: 1751-1577.
Abstract | Links | BibTeX | Tags: Informetrics, Link Analysis, Patent, Patent Analysis, United States Patent and Trademark Office online resources
@article{nokey,
title = {Which types of online resource support US patent claims?},
author = {Cristina I. Font-Julian and José-Antonio Ontalba-Ruipérez and Enrique Orduña-Malea and Mike Thelwall},
url = {https://crifonju.upv.edu.es/wp-content/uploads/2022/02/Graphical-abstract.pdf},
doi = {10.1016/j.joi.2021.101247},
issn = {1751-1577},
year = {2022},
date = {2022-01-05},
urldate = {2022-01-05},
journal = {Journal of Informetrics},
volume = {16},
number = {101247},
issue = {2022},
pages = {14},
abstract = {Patents are key documents to support the commercial exploitation of inventions. Patent docu- ments must claim inventiveness, industrial application, and novelty to be granted and may use citations and URLs to support these claims as well as to explain their ideas. Although there is much research into the citations used to support inventions, almost nothing is known about the cited URLs. This may hinder inventors and evaluators from deciding which URLs are appropriate. To investigate this issue, all 3,133,247 patents granted by the United States Patent and Trademark Office (USPTO) from 2008 to 2018 were investigated, and 2,719,705 URLs (patent outlinks) were automatically extracted using heuristics, and analyzed using link analyses techniques. A minority of patents included URLs (17.1%), with the percentage increasing over time. The inclusion of URLs differs between disciplines, with Physics (especially the subcategory Computation) having the most URLs per patent. Patents are generally embedded in the “other citations” patent section (referring to academic publications) and the “description” section (e.g., supplementary infor- mation and definitions). Online content-oriented resources (e.g., Wayback Machine, Wikipedia, YouTube), academic bibliographic databases (e.g., IEEE Xplore, Microsoft Academic, PubMed, CiteSeerX) and technological companies (e.g., IBM, Amazon, Microsoft) are often linked from USPTO patents. These findings show the broad roles that URLs can play when supporting a patent claim. Finally, in order to avoid bad practices found in the inclusion of URLs in patents, a list of recommendations to cite online resources from patents is provided.},
keywords = {Informetrics, Link Analysis, Patent, Patent Analysis, United States Patent and Trademark Office online resources},
pubstate = {published},
tppubtype = {article}
}
Patents are key documents to support the commercial exploitation of inventions. Patent docu- ments must claim inventiveness, industrial application, and novelty to be granted and may use citations and URLs to support these claims as well as to explain their ideas. Although there is much research into the citations used to support inventions, almost nothing is known about the cited URLs. This may hinder inventors and evaluators from deciding which URLs are appropriate. To investigate this issue, all 3,133,247 patents granted by the United States Patent and Trademark Office (USPTO) from 2008 to 2018 were investigated, and 2,719,705 URLs (patent outlinks) were automatically extracted using heuristics, and analyzed using link analyses techniques. A minority of patents included URLs (17.1%), with the percentage increasing over time. The inclusion of URLs differs between disciplines, with Physics (especially the subcategory Computation) having the most URLs per patent. Patents are generally embedded in the “other citations” patent section (referring to academic publications) and the “description” section (e.g., supplementary infor- mation and definitions). Online content-oriented resources (e.g., Wayback Machine, Wikipedia, YouTube), academic bibliographic databases (e.g., IEEE Xplore, Microsoft Academic, PubMed, CiteSeerX) and technological companies (e.g., IBM, Amazon, Microsoft) are often linked from USPTO patents. These findings show the broad roles that URLs can play when supporting a patent claim. Finally, in order to avoid bad practices found in the inclusion of URLs in patents, a list of recommendations to cite online resources from patents is provided.
4.
Font-Julian, Cristina I.
Descubrimiento y evaluación de recursos web de calidad mediante Patent Link Analysis PhD Thesis
2021.
Abstract | Links | BibTeX | Tags: Cybermetrics, Informetrics, Link Analysis, Patent, United States Patent and Trademark Office online resources, Web Quality, Webmetrics
@phdthesis{Font-Julian2021,
title = {Descubrimiento y evaluación de recursos web de calidad mediante Patent Link Analysis},
author = {Cristina I. Font-Julian},
editor = {Universitat Politècnica de València},
url = {https://crifonju.upv.edu.es/phd-cfont-vdeposito/},
doi = {https://doi.org/10.4995/Thesis/10251/170640},
year = {2021},
date = {2021-06-22},
urldate = {2021-06-22},
abstract = {Patents are legal documents that describe the exact operation of an invention, granting the right of economic exploitation to its owners in exchange for describing the details of the operation of said invention. For a patent to be granted, it must meet three requirements: be novel (not have been previously exhibited or published), comply with the inventive step, and have industrial application. That is why patents are valuable documents, since they contain a large amount of technical information not previously included in another type of document (published or available). Due to the particular characteristics of patents, the resources that they mention, as well as the resources that mention patents, contain links that can be useful and give support to various applications (technological surveillance, development and innovation, Triple-Helix, etc.) by having complementary information, as well as the creation of tools and techniques that allow them to be extracted and analyzed. The proposed method to achieve the objectives that define the thesis is divided into two complementary blocks: Patent Outlink and Patent Inlink, which together make up the Patent Link Analysis technique. To carry out the study, the United States Patent and Trademark Office (USPTO) is selected, collecting all those patents granted between 2008 and 2018 (both included). Once the information to be analyzed has been extracted in each block, there are: 3,133,247 patents, 2,745,973 million links contained in patents, 2,297,366 million linked patent web pages, 17,001 unique web pages linking patents and 990,663 Unique patents linked from web documents. The results of the Patent Outlink analysis show that both the number of patents that contain links (20%) and the number of links contained in patents (median 4-5) is still low, but has grown significantly in recent years and you can expect more use in the future. There is a clear difference in the use of links between areas of knowledge (42% belong to Physics, especially Computing and Calculus), as well as by sections within the documents, explaining the results obtained and the projection of future analyzes. The results of the Patent Inlink analysis identify considerably fewer web domains that link to patents (17,001 vs. 256,724), but there are more links per linking document (the total number of links is similar for both analysis blocks). Likewise, the data shows a high dispersion, with a few domains generating a large number of links. Both blocks show the existence of a high relationship with companies and technological services, with differences related to links to Universities and Governments (more links in Outlink). Finally, it is verified that the proposed model allows in an efficient, effective and replicable way the extraction and analysis of the links contained and directed to patent documents, as well as facilitating the discovery and evaluation of quality web resources. In addition, it is concluded that cybermetrics, through the link analysis technique, provides information of interest for the analysis of quality web resources through the links contained and directed to patent documents. The proposed and validated method allows defining, modeling and characterizing Patent Link Analysis as a subgenre of Link Analysis that can be used for the construction of link intelligence monitoring, evaluation and / or quality systems, among others, through the use of the inbound and outbound links of applicable patent documents universities, research centers, as well as public and private companies.},
howpublished = {Universitat Politècnica de València},
keywords = {Cybermetrics, Informetrics, Link Analysis, Patent, United States Patent and Trademark Office online resources, Web Quality, Webmetrics},
pubstate = {published},
tppubtype = {phdthesis}
}
Patents are legal documents that describe the exact operation of an invention, granting the right of economic exploitation to its owners in exchange for describing the details of the operation of said invention. For a patent to be granted, it must meet three requirements: be novel (not have been previously exhibited or published), comply with the inventive step, and have industrial application. That is why patents are valuable documents, since they contain a large amount of technical information not previously included in another type of document (published or available). Due to the particular characteristics of patents, the resources that they mention, as well as the resources that mention patents, contain links that can be useful and give support to various applications (technological surveillance, development and innovation, Triple-Helix, etc.) by having complementary information, as well as the creation of tools and techniques that allow them to be extracted and analyzed. The proposed method to achieve the objectives that define the thesis is divided into two complementary blocks: Patent Outlink and Patent Inlink, which together make up the Patent Link Analysis technique. To carry out the study, the United States Patent and Trademark Office (USPTO) is selected, collecting all those patents granted between 2008 and 2018 (both included). Once the information to be analyzed has been extracted in each block, there are: 3,133,247 patents, 2,745,973 million links contained in patents, 2,297,366 million linked patent web pages, 17,001 unique web pages linking patents and 990,663 Unique patents linked from web documents. The results of the Patent Outlink analysis show that both the number of patents that contain links (20%) and the number of links contained in patents (median 4-5) is still low, but has grown significantly in recent years and you can expect more use in the future. There is a clear difference in the use of links between areas of knowledge (42% belong to Physics, especially Computing and Calculus), as well as by sections within the documents, explaining the results obtained and the projection of future analyzes. The results of the Patent Inlink analysis identify considerably fewer web domains that link to patents (17,001 vs. 256,724), but there are more links per linking document (the total number of links is similar for both analysis blocks). Likewise, the data shows a high dispersion, with a few domains generating a large number of links. Both blocks show the existence of a high relationship with companies and technological services, with differences related to links to Universities and Governments (more links in Outlink). Finally, it is verified that the proposed model allows in an efficient, effective and replicable way the extraction and analysis of the links contained and directed to patent documents, as well as facilitating the discovery and evaluation of quality web resources. In addition, it is concluded that cybermetrics, through the link analysis technique, provides information of interest for the analysis of quality web resources through the links contained and directed to patent documents. The proposed and validated method allows defining, modeling and characterizing Patent Link Analysis as a subgenre of Link Analysis that can be used for the construction of link intelligence monitoring, evaluation and / or quality systems, among others, through the use of the inbound and outbound links of applicable patent documents universities, research centers, as well as public and private companies.