I have a problem in Protege to represent this sentence in ontology protege
"DeveloperCSharp is an Engineer who is expert in C# not in Java and Python" My ontology image
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I have made a diagram on paper for creating ontology that will describe the nodes and their connection. I do not have python or javascript for it. Can I still create an ontology in Neo4j?
I am working on Answer Sentence Selection Problem, and I want to compare dependency trees of two sentences. I am retrieving the dependency tree from spaCy and now I want to compare dependency trees. Is there any way or library in Python that I could use?
In my application I am using Stanford CoreNLP for parsing english text into a graph data structure (Universal Dependencies).
After some modifications of the graph I need to generate a natural language output for which I am using SimpleNLG: https://github.com/simplenlg/simplenlg
However SimpleNLG is using Phrase Grammar.
Therefore in order to successfully use SimpleNLG for natural language generation I need to convert from Universal Dependencies into Phrase Grammar.
What is the easiest way of achieving this?
So far I have only come across this article on this topic:
http://delivery.acm.org/10.1145/1080000/1072147/p14-xia.pdf?ip=86.52.161.138&id=1072147&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&CFID=642131329&CFTOKEN=21335001&acm=1468166339_844b802736ce07dab89064efb7f8ede9
I am hoping that someone might have some more practical code examples to share on this issue?
Phrase-structure trees contain more information than dependency trees and therefore you cannot deterministically convert dependency trees to phrase-structure trees.
But if you are using CoreNLP to parse the sentences, take a look at the parse annotator. Unlike the dependency parser, this parser also outputs phrase-structure trees, so you can use this annotator to directly parse your sentences to phrase-structure trees.
Both of them are widely used to type DBpedia resources but it seems that YAGO has much more classes or concepts organized using rdfs:subClassOf predicate. Despite this, it is not clear if, for example, that class hierarchy is a DAG (like in DBpedia), how many classes conform it, etc.
DBpedia is a community effort to extract structured information from Wikipedia. In this sense, both YAGO and DBpedia share the same goal of generating a structured ontology. The projects differ in their foci. In YAGO, the focus is on precision, the taxonomic structure, and the spatial and temporal dimension. For a detailed comparison of the projects, see Chapter 10.3 of our AI journal paper "YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia".
[Link: http://resources.mpi-inf.mpg.de/yago-naga/yago/publications/aij.pdf]
I'm trying to do some date mining with DBpedia. Now I have a dataset with properties of DBpedia ontology and DBpedia mapping and I'm not sure about the difference between those two.
What is the difference between DBpedia ontology and DBpedia mapping?
In short, DBpedia a very valuable resource for the semantic web community, but compared to Wikipedia it is quite small. Also, due to contribution of various people to Wikipedia, the infobox information is no harmonised. Therefore, a mapping language has been created to define synonymy between infobox relations and DBpedia properties.
One of the challenges in extracting information from Wikipedia is that the same concepts can be expressed using different parameters in infobox and other templates, such as |birthplace= and |placeofbirth=. Because of this, queries about where people were born would have to search for both of these properties in order to get more complete results. As a result, the DBpedia Mapping Language has been developed to help in mapping these properties to an ontology while reducing the number of synonyms. Due to the large diversity of infoboxes and properties in use on Wikipedia, the process of developing and improving these mappings has been opened to public contributions.