I am interested in the filed on Computational Linguistics and NLP. I read a lot about Grammatical Framework (GF), which is divided into abstract syntax and concrete syntax. And I know a little bit about OWL, RDF and WordNet. I am confused about the differences between the 2 technologies.
Can we use GF rather than OWL as syntax builders?
Can we eliminate Parser by using GF?
Does GF contains all terms so we don't need to use WordNet?
One of the formal definitions of Grammatical Framework is:
Grammatical Framework (GF), grammaticalframework.org, is a multilingual grammar formalism based on the idea of a shared abstract syntax and mappings between the abstract syntax and concrete languages. GF has hundreds of users all over the world.
The way GF is connected to the Semantic Web is through lemon:
Lemon is a proposed model for modeling lexicon and machine-readable dictionaries and linked to the Semantic Web and the Linked Data cloud.It was designed to meet the following challenges:
RDF-native form to enable leverage of existing Semantic Web technologies (SPARQL, OWL, RIF etc.).
Linguistically sound structure based on LMF to enable conversion to existing offline formats.
Separation of the lexicon and ontology layers, to ensure compatability with existing OWL models.
Linking to data categories, in order to allow for arbitrarily complex linguistic description.
So to answer your first question, GF and OWL complement each other. GF is essentially a set of grammatical rules that can be mapped between languages, but depending on the task at hand, you can use GF to develop powerful Semantic Web tools. For example, GF can be used to verbalise ontologies, as it has been demonstrated in lemon papers.
For the second question, yes. Since the intermediate level of GF is a set of logical rules, you don't need a parser anymore. The morphology and basic syntax mapping can be enough (again, what is your goal? As the definition says, GF covers basic syntax.)
As for WordNet:
WordNet® is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms (synsets), each expressing a distinct concept. Synsets are interlinked by means of conceptual-semantic and lexical relations.
WordNet can be perceived as an ontology, but it is not. It cannot even be called a linguistic ontology. Having hypernym and hyponym relations does not make a dataset into an ontology.
What lemon or ontolex are trying to achieve is to create an ontology that can be used for linguistic purposes. This purpose could be annotation, corpus study, modelling dictionaries, and etc. However, the power of WordNet lies within its synsets (Words from the same lexical category that are roughly synonymous are grouped into synsets.); but the power of RDF/OWL lies within inference.
In the 4 years since this question was first asked, there have been some updates in GF. Most importantly, we now have a WordNet ported into GF, currently for 13 languages, with full inflection tables. You can find the repository in https://github.com/GrammaticalFramework/gf-wordnet#readme and a multilingual web interface in http://www.grammaticalframework.org/~krasimir/gf-wordnet.html. Some examples how to use the interface:
English inflection table:
Finnish inflection table:
Related
I am using Wordnet for finding synonyms of ontology concepts. How can i find choose the appropriate sense for my ontology concept. e.g there is an ontlogy concept "conference" it has following synsets in wordnet
The noun conference has 3 senses (first 3 from tagged texts)
(12) conference -- (a prearranged meeting for consultation or exchange of information or discussion (especially one with a formal agenda))
(2) league, conference -- (an association of sports teams that organizes matches for its members)
(2) conference, group discussion -- (a discussion among participants who have an agreed (serious) topic)
now 1st and 3rd synsets have apprpriate sense for my ontology concept. How can i choose only these two from wordnet?
The technology you're looking for is in the direction of semantic disambiguation / representation.
The most "traditional approach" is Word Sense Disambiguation (WSD), take a look at
https://en.wikipedia.org/wiki/Word-sense_disambiguation
https://stackoverflow.com/questions/tagged/word-sense-disambiguation
Anyone know of some good Word Sense Disambiguation software?
Then comes the next generation of Word Sense induction / Topic modelling / Knowledge representation:
https://en.wikipedia.org/wiki/Word-sense_induction
https://en.wikipedia.org/wiki/Topic_model
https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning
Then comes the most recent hype:
Word embeddings, vector space models, neural nets
Sometimes people skip the semantic representation and goes directly to do text similarity and by comparing pairs of sentences, the differences/similarities before getting to the ultimate aim of the text processing.
Take a look at Normalize ranking score with weights for a list of STS related work.
On the other direction, there's
ontology creation (Cyc, Yago, Freebase, etc.)
semantic web (https://en.wikipedia.org/wiki/Semantic_Web)
semantic lexical resources (WordNet, Open Multilingual WordNet, etc.)
Knowledge base population (http://www.nist.gov/tac/2014/KBP/)
There's also a recent task on ontology induction / expansion:
http://alt.qcri.org/semeval2015/task17/
http://alt.qcri.org/semeval2016/task13/
http://alt.qcri.org/semeval2016/task14/
Depending on the ultimate task, maybe either of the above technology would help.
You can also try Babelfy, which provides Word Sense Disambiguation and Named Entity Disambiguation.
Demo:
http://babelfy.org/
API:
http://babelfy.org/guide
Take a look at this list: 100 Best GitHub: Word-sense Disambiguation
and search by WordNet - there are several appropriate libraries.
I didn't use any of them, but this one seems to be promising, because it is based on classic yet effective idea (namely, Lesk algorithm) upgraded by modern word-embedding methods. Actually, before finding it, I was going to suggest to try almost the same ideas.
Note also that all methods try to find the meaning (WordNet sysnet, in your case) that is most similar to the context of the current word/collocation, so it is crucial to have context of the words you're trying to disambiguate. For example, words can come from some text and most libraries rely on that.
What is the difference between 'semantic' in nlp and 'semantic' in ontology accessed through an api such as jena??
As you can find from a quick search, semantics is
the branch of linguistics and logic concerned with meaning. The two main areas are logical semantics, concerned with matters such as sense and reference and presupposition and implication, and lexical semantics, concerned with the analysis of word meanings and relations between them.
This is the correct definition for NLP semantics. For Semantic Web, semantics is specifically the semantics of logical languages defined for the Semantic Web, i.e., RDF, RDFS, OWL (1 and 2). The main difference between the semantics of these languages and that of other languages (logical and not) is the restrictions that are applied to RDF/S and OWL (1/2) DL to make them machine understandable; this means that, in these languages, all implications can be made explicit in finite time.
You can find a lot of material on RDFS and OWL on the W3C pages:
http://www.w3.org/TR/owl2-overview/
I am using Wordnet for finding synonyms of ontology concepts. How can i find choose the appropriate sense for my ontology concept. e.g there is an ontlogy concept "conference" it has following synsets in wordnet
The noun conference has 3 senses (first 3 from tagged texts)
(12) conference -- (a prearranged meeting for consultation or exchange of information or discussion (especially one with a formal agenda))
(2) league, conference -- (an association of sports teams that organizes matches for its members)
(2) conference, group discussion -- (a discussion among participants who have an agreed (serious) topic)
now 1st and 3rd synsets have apprpriate sense for my ontology concept. How can i choose only these two from wordnet?
The technology you're looking for is in the direction of semantic disambiguation / representation.
The most "traditional approach" is Word Sense Disambiguation (WSD), take a look at
https://en.wikipedia.org/wiki/Word-sense_disambiguation
https://stackoverflow.com/questions/tagged/word-sense-disambiguation
Anyone know of some good Word Sense Disambiguation software?
Then comes the next generation of Word Sense induction / Topic modelling / Knowledge representation:
https://en.wikipedia.org/wiki/Word-sense_induction
https://en.wikipedia.org/wiki/Topic_model
https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning
Then comes the most recent hype:
Word embeddings, vector space models, neural nets
Sometimes people skip the semantic representation and goes directly to do text similarity and by comparing pairs of sentences, the differences/similarities before getting to the ultimate aim of the text processing.
Take a look at Normalize ranking score with weights for a list of STS related work.
On the other direction, there's
ontology creation (Cyc, Yago, Freebase, etc.)
semantic web (https://en.wikipedia.org/wiki/Semantic_Web)
semantic lexical resources (WordNet, Open Multilingual WordNet, etc.)
Knowledge base population (http://www.nist.gov/tac/2014/KBP/)
There's also a recent task on ontology induction / expansion:
http://alt.qcri.org/semeval2015/task17/
http://alt.qcri.org/semeval2016/task13/
http://alt.qcri.org/semeval2016/task14/
Depending on the ultimate task, maybe either of the above technology would help.
You can also try Babelfy, which provides Word Sense Disambiguation and Named Entity Disambiguation.
Demo:
http://babelfy.org/
API:
http://babelfy.org/guide
Take a look at this list: 100 Best GitHub: Word-sense Disambiguation
and search by WordNet - there are several appropriate libraries.
I didn't use any of them, but this one seems to be promising, because it is based on classic yet effective idea (namely, Lesk algorithm) upgraded by modern word-embedding methods. Actually, before finding it, I was going to suggest to try almost the same ideas.
Note also that all methods try to find the meaning (WordNet sysnet, in your case) that is most similar to the context of the current word/collocation, so it is crucial to have context of the words you're trying to disambiguate. For example, words can come from some text and most libraries rely on that.
I want to perform semantic analysis on some text similar to YAGO[1]. But I have no structure in the text to identify entities and relationships. One way is I use POS tagging and then identify subject and predicates in the sentences[2]. But still I cannot establish what relationships exist between them.
How should I go about this?
For example:
Albert Einstein was born in 1879.
Should result in:
AlbertEinstein BORNIN 1879
subject relation predicate
My aim to look for better approaches to find subjects, predicates and relationships in raw text.
What you are trying to do is essentially Natural Language Understanding, a subfield of Natural Language Processing, which again is a subfield of Computational Linguistics ~ often thought as the engineering arm.
You could do semantic parsing or relation extraction. Either are fine for this task. I decided to read through Suchanek et al (2007) and you will realise that it is ontology based, where the relations are extracted into a predefined ontological template where aixoms are predifed (e.g. BORNIN). I personally think this is far to restrictive for general intelligence but works great with weak ai problems [narrow domains]. Much more interesting work has been happening over the years such as ontology driven information extraction, where the algorithms are trained on the ontology rather than having a corpus annotated by an ontology. One PhD study that comes to mind is McDowell Thesis and the Yildiz & Miksch (2007) paper.
Regardless and without going off topic, there is a really interesting open source Python GUI driven project called iepy at the moment being developed by a firm called Machinalis which is based on django. It allows for rule based and machine learning based information extraction. I highly recommend you check it out -> Tried and tested by myself. Also, I'm not affiliated with this company.
https://github.com/machinalis/iepy
According to the documentation:
IEPY is an open source tool for Information Extraction focused on
Relation Extraction.
To give an example of Relation Extraction, if we are trying to find a
birth date in:
"John von Neumann (December 28, 1903 – February 8, 1957) was a
Hungarian and American pure and applied mathematician, physicist,
inventor and polymath." then IEPY's task is to identify "John von
Neumann" and "December 28, 1903" as the subject and object entities of
the "was born in" relation.
It's aimed at: users needing to perform Information Extraction on a
large dataset. scientists wanting to experiment with new IE
algorithms.
The task you attempt to solve is called relation extraction, while semantic analysis has much broader meaning (honestly, I can't say for sure what does it mean now).
Relation extraction is an open research problem, so I suggest to review the field - for example, start from the chapter 2.3 of Mining text data book or A Review of Relation Extraction paper (which is a little older - 2007). Then continue research by following citing or cited-by links; finally, try to implement approach that looks most promising: for example, if you know that your data is rather formal (all sentences are short and share similar strict structure), then try something like pattern-based approaches; and so on.
Stanford parser can do it :) You need to look at the dependency parser though. Have a look at the bottom of this page: http://nlp.stanford.edu/software/lex-parser.shtml:
subject: nsubj(snapped, rain),
or direct object: dobj(shut, hub))
...
Or have a look at this page (Stanford Dependencies): http://nlp.stanford.edu/software/stanford-dependencies.shtml
And to understand the annotations have a look at this: http://nlp.stanford.edu/software/dependencies_manual.pdf
And for your particular example, use Stanford "collapsed" dependency parser which for a given sentence will produce predicates like born_in(Einstein,1879), which is very similar to what you want.
I'm building a small prototype of a Movies semantic search engine based on the data of LinkedIMDB
I've defined some Query Types as an example of use cases
search by entity name search by
entity type
search common features between two entities ...etc
So far I've developed a SPARQL engine that takes any type of those Queries and send the Query to the endpoint and preview the result.
The problem here is that I want to make a natural language or semi natural language interface for it in order for users to invoke those sentences using Natural language search Queries. But I don't know from where to start.
I've found some papers that are trying to extract triplets from the text but I don't feel that's the key to the solution.
Also I've found some LSA techniques to interpret Natural language search Queries but I feel it's not applicable to semantic search domain.
Any idea or resources to start reading from?
Is there a best practice than the natural language interface?
A lot of work has been done in the field of natural languge -> SQL conversion. Maybe you should take that as a starting point and see how you can modify the available examples for SPARQL. (Also, designing a controlled natural language could make your task easier.)
Another path to explore can be this article: Supporting Domain Experts to Construct Conceptual Ontologies: A Holistic Approach published at the Journal of Web Semantics, http://www.websemanticsjournal.org/index.php/ps/article/view/189 Even though it is about using natural language for ontology construction, the approach explained there (along with open source code) can turn into a fruitful exploration.
Have you seen FREya # https://github.com/nmvijay/freya it is an NLP to SPARQL convertor.
FREyA is an interactive Natural Language Interface for querying ontologies which combines usability enhancement methods such as feedback and clarification dialogs in order to:
1) improve recall by generating the dialog and enriching the domain lexicon from the user's vocabulary, whenever an "unknown" term appears in a question
2) improve precision by resolving ambiguities more effectively through the dialog. The suggestions shown to the user are found through ontology reasoning and are initially ranked using the combination of string similarity and synonym detection. The system then learns from the user's selections, and improves its performance over time.