OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology
1. Introduction and Motivation
2. Background and Related Work
2.1. Named Entity Disambiguation Approximations
- knowledge-based, also known as knowledge-rich, relying on lexical resources such as ontologies, machine-readable dictionaries, or thesauri;
- corpus-based, also known as knowledge-poor, which do not employ sense-labeled knowledge sources.
2.2. Early Approaches
2.3. Recent Deep Learning Approaches
2.4. Conclusions and Current Limitations
3. Materials and Methods
3.1. Notations and Terminology
- The Wikipedia articles are also referred to as Wikipedia entities, denoted as p.
- A text hyperlink to a Wikipedia page is denoted as a mention.
- Text hyperlink anchors within Wikipedia pointing to another page or article are referred to as anchors and denoted as a. Indices are used for referral to specific items in the anchor sequence as follows: a0 is the first anchor, i + 1 and so on. The number of anchors of a text input is cited as m.
- The notation pa refers to one of the candidate Wikipedia page senses of the anchor a.
- The set of linkable Wikipedia entities to an anchor a is denoted as Pg(a).
- The ensemble of inbound links to a given Wikipedia entity p is represented using in(p).
- The size of the Wikipedia entities ensemble is cited as |W|.
- link(a) refers to the cardinality of the count of an anchor’s indices as a mention.
- freq(a) denotes the total occurrence count of an anchor text within a corpus, including free text and hyperlinks.
- lp denotes the link probability of a text segment.
3.2. Knowledge Extraction
- Anchor ID: by keeping an identifier encoding for each text segment encountered as a hyperlink on the processed Wikipedia snapshot.
- Mention entity ID: the Wikipedia ID pointed to by a mention. Maintaining this information is necessary for deriving relatedness and commonness statistics.
- Source article ID: the Wikipedia article ID where an individual mention is encountered. This is necessary for relatedness calculations.
3.3.1. Extraction, Transformation, and Loading
3.3.2. Coherence-Based Dimensionality Reduction
3.3.3. Named Entity Disambiguation
- class 1: the compatibility of the mention in the given context;
- class 0: the incompatibility of that mention in the given context.
3.3.4. Quantification of Uncertainty
3.4. Evaluation Process
4.1. Experimental Analysis Discussion
4.2. Quantification of Certainty Evaluation
5. Conclusions and Future Work
Conflicts of Interest
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|CNM 1 (Precision)||0.7554||0.8362||0.8738||0.9337||0.9678||0.9866|
|CNM 1 (F1)||0.8606||0.8667||0.8351||0.7287||0.5659||0.3325|
|OTNEL 2 (Precision)||0.8290||0.8897||0.9180||0.9589||0.9789||0.9896|
|OTNEL 2 (F1)||0.9065||0.8948||0.8548||0.7381||0.5678||0.3327|
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Makris, C.; Simos, M.A. OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology. Big Data Cogn. Comput. 2020, 4, 31. https://doi.org/10.3390/bdcc4040031
Makris C, Simos MA. OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology. Big Data and Cognitive Computing. 2020; 4(4):31. https://doi.org/10.3390/bdcc4040031Chicago/Turabian Style
Makris, Christos, and Michael Angelos Simos. 2020. "OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology" Big Data and Cognitive Computing 4, no. 4: 31. https://doi.org/10.3390/bdcc4040031