Abstract:One of the challenging tasks in the context of Ontological Engineering is to automatically or semi-automatically support the process of Ontology Learning and Ontology Population from semi-structured documents (texts). In this paper we describe a Semi-Automatic Ontology Instantiation method from natural language text, in the domain of Risk Management. This method is composed from three steps 1)Annotation with part-of-speech tags, 2) Semantic Relation Instances Extraction, 3) Ontology instantiation process. It's based on combined NLP techniques using human intervention between steps 2 and 3 for control and validation. Since it heavily relies on linguistic knowledge it is not domain dependent which is a good feature for portability between the different fields of risk management application. The proposed methodology uses the ontology of the PRIMA 1 project (supported by the European community) as a Generic Domain Ontology and populates it via an available corpus. A first validation of the approach is done through an experiment with Chemical Fact Sheets from Environmental Protection Agency 2 .
many authors can share the same name and this constitutes a serious problem that affects the relevancy of retrieval results and constitutes our motivation of finding such approach to cover this issue at the author names entity level. Solving such a problem may return with positive gain at the level of document retrieval, web search and the quality of data. This entity resolution task can be tackled as an unsupervised problem, where there are set of features that can be employed for the resolution job, or as supervised problem to compute the similarities among two citations and then classify if they are the same or not. Recent approaches usually utilize features such as: co-author, venue, topic similarity, affiliations and title of publications to deal with author ambiguity. In this paper, three attributes are used to treat this problem sequentially. The coauthorship firstly which is a well-known attribute, and then the topic and affiliation extracted from biographies, which can be found inside the publication, and this is our novelty frame in this paper.
Social media platforms (SMP) are new resource for data analytics. Multiple aspects can be studied by using its variety of features. Sentiment analysis (SA) is a rising research topic in SMPs. SA approaches on studying and analysing events are still missing several shortcomings. In this paper, we address the problem of ranking event entities and propose a novel approach for this goal. An entity is a person who presents some task in such event, for e.g., a researcher in a conference. To achieve our target, we employ the lexical approach, in addition to associating features from both Facebook and Twitter platforms. We used Facebook reactions also, that not been used in the state-of-the-art approaches. Our results have shown that by associating both features from Facebook and Twitter and by using reactions, we can successfully rank entities participating in a specific event having high precision.Lexicon-based sentiment analysis approach for ranking event entities
With the enormous growth of data, retrieving information from the Web became more desirable and even more challenging because of the Big Data issues (e.g. noise, corruption, bad quality…etc.). Expert seeking, defined as returning a ranked list of expert researchers given a topic, has been a real concern in the last 15 years. This kind of task comes in handy when building scientific committees, requiring to identify the scholars' experience to assign them the most suitable roles in addition to other factors as well. Due to the fact the Web is drowning with plenty of data, this opens up the opportunity to collect different kinds of expertise evidence. In this paper, we propose an expert seeking approach with specifying the most desirable features (i.e. criteria on which researcher's evaluation is done) along with their estimation techniques. We utilized some machine learning techniques in our system and we aim at verifying the effectiveness of incorporating influential features that go beyond publications.
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