This article studies opinion mining from social media with probabilistic logic reasoning. As it is known, Twitter is one of the most active social networks, with millions of tweets sent daily, where ...multiple users express their opinion about traveling, economic issues, political decisions etc. As such, it offers a valuable source of information for opinion mining. In this paper we present OpinionMine, a Bayesian-based framework for opinion mining, exploiting Twitter Data. Initially, our framework imports Tweets massively by using Twitter’s API. Next, the imported Tweets are further processed automatically for constructing a set of untrained rules and random variables. Then, a Bayesian Network is derived by using the set of untrained rules, the random variables and an evidence set. After that, the trained model can be used for the evaluation of new Tweets. Finally, the constructed model can be retrained incrementally, thus becoming more robust. As application domain for the development of our methodology we have selected tourism because it is one of the most popular topics in social media. Our framework can predict users’ intention to visit a place. Among the advantages of our framework is that it follows an incremental learning strategy. That is, the derived model can be retrained incrementally with new training sets thus becoming more robust. Further, our framework can be easily adapted to opinion mining from social media on other topics, whereas the rules of the derived model are constructed in an efficient way and automatically.
The early and accurate identification of a disease is important for its effective treatment. However, medical errors represent a serious problem and pose a threat to patient safety. To this ...direction, appropriate and continuous education of the medical personnel has been widely recognized as an important mean to reduce medical errors and increase the quality of the health system. In this paper, we present MediExpert, an expert system targeting on continuous education of health personnel, providing also guidelines to persons that either cannot easily move due to age related comorbidities, or because they are away from healthcare units, further recommending users to talk with their doctors. It is based on differential diagnosis, employs ontologies for effective classification of health related problems and intelligent algorithms to enhance continuous education. We present the various components of the system and we elaborate on the benefits gained when using it for education.
•A novel intelligent, collaborative health knowledge system supporting diagnosis.•Temporal rule and knowledge management.•An inference engine handling the embedded time and uncertainty.•A conflict ...detection policy for supporting rule generation.•A well-designed and user-friendly web interface with strong collaboration features.
The huge diversity and quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and reasoning This paper elaborates on the design and development of COSMOS, an intelligent system which supports a) collaboration of experts for developing common knowledge bases and b) diagnosis derivation which takes into account time and uncertainty. The health domain is used for illustration and discussion of the features of our approach. Initially, we present the syntax and the semantics of the rules which incorporate time (temporal rules) and the data items upon which reasoning is performed. Then we introduce its inference engine, able to perform reasoning on top of rules and data, handling also the embedded time and uncertainty. We proceed further to define a conflict detection policy for supporting the difficult and error prone task of rule generation. The complexities of the aforementioned tasks are hidden from users via a well-designed and user friendly web interface that possesses strong collaboration features enabling multiple experts to work on defining a rule and on developing a common knowledge base. Evaluation of COSMOS has been performed using a) students studying expert systems and b) health experts in order to demonstrate the usability of the approach and the considerable advantages gained. To the best of our knowledge, COSMOS is one of the very few systems combining temporal rules, a powerful inference engine handling uncertainty and conflict detection and progresses beyond state of the art by adopting strong collaboration features and paradigms.
As the number of clinical guidelines and rules for effective management of cancer therapy is rapidly increasing decision support systems are more and more required. To this direction, in this paper, ...we present a collaborative knowledge management system for cancer diseases leading to decision support and intelligent diagnosis. Clinicians can specify a variety of knowledge rules in a collaborative fashion. Then, those rules are applied on top of patient data collected within a personal health record. The generated knowledge is formulated as a free text and returned back to the clinicians to support them and enhance the communication with their patients.
Querying Knowledge Graphs in Greek Language Tsampos, Ioannis; Marakakis, Emmanouil
Proceedings of the 17th International Conference on PErvasive Technologies Related to Assistive Environments,
06/2024
Conference Proceeding
Odprti dostop
We present a method for querying knowledge graphs through natural language, emphasizing its application to the Greek language. It integrates NLP techniques with the capabilities of graph databases to ...enable seamless interaction with knowledge graphs through natural language queries. Upon receiving a user's question in Greek language, we use linguistic analysis tools to convert it into a graph structure by employing predefined rules and adhering to a graph database schema. This methodology enables the handling of different question types and the efficient extraction of relations, ensuring accurate mapping of linguistic structures to database queries. The representation of a question as a knowledge graph enables its direct translation to a Cypher query, facilitating the extraction of related answers. The approach can handle complex questions and is language independent.
INTEGRA Papakonstantinou, Aris; Kondylakis, Haridimos; Marakakis, Emmanouil
Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments,
06/2020
Conference Proceeding
Diagnosis is one of the most important tasks of health providers with a tremendous impact on patient's health and well-being. Although many diagnostic decision support systems have been proposed so ...far, with several advantages and disadvantages, we have not yet seen approaches trying to integrate the decision of individual systems to improve the quality of the final diagnosis. To this direction, we present INTEGRA a novel decision support system, allowing multiple underlying diagnostic decision-support sub-systems to work in parallel, effectively integrating the individual decisions, increasing as such the quality of the final diagnosis. In addition, INTEGRA enables health providers to get explanation of the decisions based on the combined diagnosis and further explore the recommendations of the individual decision support sub-systems.
COSMOS Giannoulis, Michail; Kondylakis, Haridimos; Marakakis, Emmanouil
Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference,
06/2018
Conference Proceeding
The huge diversity, big quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and ...reasoning. In this paper, we present COSMOS, a knowledge system that fully addresses these challenges, in an efficient way, paving the way for a new generation of knowledge systems. Using our approach, it is possible for domain experts to generate temporal knowledge rules. As those rules are saved to our knowledge base, a conflict detection mechanism detects and solves rule conflicts. Then, an inference engine is able to perform efficiently, accurate decisions, based on available factual information using reasoning and handling uncertainty. Ontologies are used to model both the factual information and the data items in the rules enabling also interoperability with existing systems. To validate our approach, as an application scenario, we deploy our infrastructure in a health environment where doctors provide rules that are activated over a patient health record. Preliminary results indicate the benefits of our approach for decision support based on health data, successfully identifying adverse events and enabling intelligent patient monitoring.
COVID-19 is a disease that has infected almost the whole world and has been pronounced as a global pandemic. The digital health domain has already tried to respond to the pandemic challenges by ...developing algorithms and applications that make predictions on the infection and the corresponding outcome in case of infection. In this direction, in this paper, we present "COVID-19 Detect & Predict", an application that can detect and predict COVID-19 infection through probabilistic logic reasoning, and in case of infection, it can also predict the possibility a patient to recover. We demonstrate the effectiveness of our solution on realistic datasets, showing its potential benefits. Our approach is the first to detect and predict COVID-19 infection through probabilistic logic reasoning to the best of our knowledge.
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