An abundance of biomedical data is generated in the form of clinical notes, reports, and research... more An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. ...
Abstract—In this paper we explore the task of mood classification for blog postings. We propose a... more Abstract—In this paper we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a dataset to train and evaluate our method. Keywords:
Efficient practices to provide access to biomedical publications facilitate the timely transfer o... more Efficient practices to provide access to biomedical publications facilitate the timely transfer of information from the scientific research community to peer investigators and other healthcare practitioners. At present, the portable document format (PDF) is one of the dominating formats to share scientific knowledge offline. Additionally, some HTML-based formats have been introduced to share scientific content online. Online Search engines, e.g., GoogleScholar, require machine-interpretable metadata to correctly index items in a context-aware manner for accurate biomedical literature searches. We have developed a lightweight technical infrastructure (goSemantically) and miniaturized that as Google Docs add-ons that helps authors to add machine-interpretable metadata at the content and structural levels while authoring biomedical content. The infrastructure uses the NCBO Bioportal resources to annotate the biomedical content with appropriate semantic vocabularies. It further utilizes...
2022 IEEE 16th International Conference on Semantic Computing (ICSC), 2022
Significant barriers exist in achieving fast and accurate access to online biomedical content bec... more Significant barriers exist in achieving fast and accurate access to online biomedical content because of the proliferation of unstructured biomedical information. Accompanying semantic annotations with growing biomedical content is critical to enhancing search engines' context-aware indexing, improving search speeds and retrieval accuracy. We have developed “Semantically”: a biomedical structured content authoring and publishing framework to enhance biomedical content FAIR-ness (Findability, Accessibility, Interoperability, and Reusability). Finding the appropriate semantic vocabulary to annotate biomed-ical content is time-consuming and technically challenging. “Semantically” automates and streamlines this process for users by recommending highly accurate annotations from an array of biomedical ontologies. Similarly, preserving content-level se-mantics at the content publishing stage to foster semantic search remains a critical research challenge. “Semantically” addresses this obstacle by extending schema.org, a community-agreed and research engine endorsed guideline for publishing structured content on the web. In future works, we aim to improve the biomedical content annotation process through a socio-technical approach by enabling a collaborative annotation scheme. The demo of the system is accessible at: https://gosemantically.com/
Due to the ubiquity of unstructured biomedical data, significant obstacles still remain in achiev... more Due to the ubiquity of unstructured biomedical data, significant obstacles still remain in achieving accurate and fast access to online biomedical content. In lieu of the growing volume of biomedical content on the web, embedding semantic annotations has become key to enhancing search engine context-aware indexing, thereby improving search speeds and retrieval accuracy. We introduce Semantically: a socio-technical framework for semantic biomedical content authoring and publishing. Identifying the appropriate semantic vocabulary for biomedical content annotation is a time-consuming and technically challenging process. Semantically automates this search by recommending highly accurate annotations from a wide range of biomedical ontologies. Furthermore, the framework is integrated with a knowledge-sharing system which allows biomedical authors to collaborate on identifying precise annotations during the content authoring process. Similarly, preserving content-level semantics during and...
@Book{UCNLG+Sum:2009, editor = {Anja Belz, University of Brighton, UK and Roger Evans, University... more @Book{UCNLG+Sum:2009, editor = {Anja Belz, University of Brighton, UK and Roger Evans, University of Brighton, UK and Sebastian Varges, University of Trento, Italy}, title = {Proceedings of the 2009 Workshop on Language Generation and Summarisation (UCNLG+Sum 2009)}, month = {August}, year = {2009}, address = {Suntec, Singapore}, publisher = {Association for Computational Linguistics}, url = {http://www.aclweb.org/anthology/W/W09/W09-28} } @InProceedings{mckeown: 2009:UCNLG+Sum, author = {McKeown, Kathy}, title = {Query ...
One of the goals of Educational Data Mining is to develop the methods for student modeling based ... more One of the goals of Educational Data Mining is to develop the methods for student modeling based on educational data, such as; chat conversation, class discussion, etc. On the other hand, individual behavior and personality play a major role in Intelligent Tutoring Systems (ITS) and Educational Data Mining (EDM). Thus, to develop a user adaptable system, the student’s behaviors that occurring during interaction has huge impact EDM and ITS. In this chapter, we introduce a novel data mining techniques and natural language processing approaches for automated detection student’s personality and behaviors in an educational game (Land Science) where students act as interns in an urban planning firm and discuss in groups their ideas. In order to apply this framework, input excerpts must be classified into one of six possible personality classes. We applied this personality classification method using machine learning algorithms, such as: Naive Bayes, Support Vector Machine (SVM) and Decision Tree.
An abundance of biomedical data is generated in the form of clinical notes, reports, and research... more An abundance of biomedical data is generated in the form of clinical notes, reports, and research articles available online. This data holds valuable information that requires extraction, retrieval, and transformation into actionable knowledge. However, this information has various access challenges due to the need for precise machine-interpretable semantic metadata required by search engines. Despite search engines' efforts to interpret the semantics information, they still struggle to index, search, and retrieve relevant information accurately. To address these challenges, we propose a novel graph-based semantic knowledge-sharing approach to enhance the quality of biomedical semantic annotation by engaging biomedical domain experts. In this approach, entities in the knowledge-sharing environment are interlinked and play critical roles. Authorial queries can be posted on the "Knowledge Cafe," and community experts can provide recommendations for semantic annotations. ...
Abstract—In this paper we explore the task of mood classification for blog postings. We propose a... more Abstract—In this paper we explore the task of mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard machine learning approach. We also show that using sentiment orientation features improves the performance of classification. We used the Livejournal blog corpus as a dataset to train and evaluate our method. Keywords:
Efficient practices to provide access to biomedical publications facilitate the timely transfer o... more Efficient practices to provide access to biomedical publications facilitate the timely transfer of information from the scientific research community to peer investigators and other healthcare practitioners. At present, the portable document format (PDF) is one of the dominating formats to share scientific knowledge offline. Additionally, some HTML-based formats have been introduced to share scientific content online. Online Search engines, e.g., GoogleScholar, require machine-interpretable metadata to correctly index items in a context-aware manner for accurate biomedical literature searches. We have developed a lightweight technical infrastructure (goSemantically) and miniaturized that as Google Docs add-ons that helps authors to add machine-interpretable metadata at the content and structural levels while authoring biomedical content. The infrastructure uses the NCBO Bioportal resources to annotate the biomedical content with appropriate semantic vocabularies. It further utilizes...
2022 IEEE 16th International Conference on Semantic Computing (ICSC), 2022
Significant barriers exist in achieving fast and accurate access to online biomedical content bec... more Significant barriers exist in achieving fast and accurate access to online biomedical content because of the proliferation of unstructured biomedical information. Accompanying semantic annotations with growing biomedical content is critical to enhancing search engines' context-aware indexing, improving search speeds and retrieval accuracy. We have developed “Semantically”: a biomedical structured content authoring and publishing framework to enhance biomedical content FAIR-ness (Findability, Accessibility, Interoperability, and Reusability). Finding the appropriate semantic vocabulary to annotate biomed-ical content is time-consuming and technically challenging. “Semantically” automates and streamlines this process for users by recommending highly accurate annotations from an array of biomedical ontologies. Similarly, preserving content-level se-mantics at the content publishing stage to foster semantic search remains a critical research challenge. “Semantically” addresses this obstacle by extending schema.org, a community-agreed and research engine endorsed guideline for publishing structured content on the web. In future works, we aim to improve the biomedical content annotation process through a socio-technical approach by enabling a collaborative annotation scheme. The demo of the system is accessible at: https://gosemantically.com/
Due to the ubiquity of unstructured biomedical data, significant obstacles still remain in achiev... more Due to the ubiquity of unstructured biomedical data, significant obstacles still remain in achieving accurate and fast access to online biomedical content. In lieu of the growing volume of biomedical content on the web, embedding semantic annotations has become key to enhancing search engine context-aware indexing, thereby improving search speeds and retrieval accuracy. We introduce Semantically: a socio-technical framework for semantic biomedical content authoring and publishing. Identifying the appropriate semantic vocabulary for biomedical content annotation is a time-consuming and technically challenging process. Semantically automates this search by recommending highly accurate annotations from a wide range of biomedical ontologies. Furthermore, the framework is integrated with a knowledge-sharing system which allows biomedical authors to collaborate on identifying precise annotations during the content authoring process. Similarly, preserving content-level semantics during and...
@Book{UCNLG+Sum:2009, editor = {Anja Belz, University of Brighton, UK and Roger Evans, University... more @Book{UCNLG+Sum:2009, editor = {Anja Belz, University of Brighton, UK and Roger Evans, University of Brighton, UK and Sebastian Varges, University of Trento, Italy}, title = {Proceedings of the 2009 Workshop on Language Generation and Summarisation (UCNLG+Sum 2009)}, month = {August}, year = {2009}, address = {Suntec, Singapore}, publisher = {Association for Computational Linguistics}, url = {http://www.aclweb.org/anthology/W/W09/W09-28} } @InProceedings{mckeown: 2009:UCNLG+Sum, author = {McKeown, Kathy}, title = {Query ...
One of the goals of Educational Data Mining is to develop the methods for student modeling based ... more One of the goals of Educational Data Mining is to develop the methods for student modeling based on educational data, such as; chat conversation, class discussion, etc. On the other hand, individual behavior and personality play a major role in Intelligent Tutoring Systems (ITS) and Educational Data Mining (EDM). Thus, to develop a user adaptable system, the student’s behaviors that occurring during interaction has huge impact EDM and ITS. In this chapter, we introduce a novel data mining techniques and natural language processing approaches for automated detection student’s personality and behaviors in an educational game (Land Science) where students act as interns in an urban planning firm and discuss in groups their ideas. In order to apply this framework, input excerpts must be classified into one of six possible personality classes. We applied this personality classification method using machine learning algorithms, such as: Naive Bayes, Support Vector Machine (SVM) and Decision Tree.
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Papers by Fazel Keshtkar