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.
Related
I have a corpus of a few 100-thousand legal documents (mostly from the European Union) – laws, commentary, court documents etc. I am trying to algorithmically make some sense of them.
I have modeled the known relationships (temporal, this-changes-that, etc). But on the single-document level, I wish I had better tools to allow fast comprehension. I am open for ideas, but here's a more specific question:
For example: are there NLP methods to determine the relevant/controversial parts of documents as opposed to boilerplate? The recently leaked TTIP papers are thousands of pages with data tables, but one sentence somewhere in there may destroy an industry.
I played around with google's new Parsey McParface, and other NLP solutions in the past, but while they work impressively well, I am not sure how good they are at isolating meaning.
In order to make sense out of documents you need to perform some sort of semantic analysis. You have two main possibilities with their exemples:
Use Frame Semantics:
http://www.cs.cmu.edu/~ark/SEMAFOR/
Use Semantic Role Labeling (SRL):
http://cogcomp.org/page/demo_view/srl
Once you are able to extract information from the documents then you may apply some post-processing to determine which information is relevant. Finding which information is relevant is task related and I don't think you can find a generic tool that extracts "the relevant" information.
I see you have an interesting usecase. You've also mentioned the presence of a corpus(which a really good plus). Let me relate a solution that I had sketched for extracting the crux from research papers.
To make sense out of documents, you need triggers to tell(or train) the computer to look for these "triggers". You can approach this using a supervised learning algorithm with a simple implementation of a text classification problem at the most basic level. But this would need prior work, help from domain experts initially for discerning "triggers" from the textual data. There are tools to extract gists of sentences - for example, take noun phrases in a sentence, assign weights based on co-occurences and represent them as vectors. This is your training data.
This can be a really good start to incorporating NLP into your domain.
Don't use triggers. What you need is a word sense disambiguation and domain adaptation. You want to make sense of is in the documents i.e understand the semantics to figure out the meaning. You can build a legal ontology of terms in skos or json-ld format represent it ontologically in a knowledge graph and use it with dependency parsing like tensorflow/parseymcparseface. Or, you can stream your documents in using a kappa based architecture - something like kafka-flink-elasticsearch with added intermediate NLP layers using CoreNLP/Tensorflow/UIMA, cache your indexing setup between flink and elasticsearch using redis to speed up the process. To understand relevancy you can apply specific cases from boosting in your search. Furthermore, apply sentiment analysis to work out intents and truthness. Your use case is one of an information extraction, summarization, and semantic web/linked data. As EU has a different legal system you would need to generalize first on what is really a legal document then narrow it down to specific legal concepts as they relate to a topic or region. You could also use here topic modelling techniques from LDA or Word2Vec/Sense2Vec. Also, Lemon might also help from converting lexical to semantics and semantics to lexical i.e NLP->ontology ->ontology->NLP. Essentially, feed the clustering into your classification of a named entity recognition. You can also use the clustering to assist you in building out the ontology or seeing what word vectors are in a document or set of documents using cosine similarity. But, in order to do all that it be best to visualize the word sparsity of your documents. Something like commonsense reasoning + deep learning might help in your case as well.
I mean, for example, when we say some things to describe a scene, Can Watson understand how that scene is or look like? Cyc cop tried to design common sense for AI, like understanding and inferring from sentences from knowledge base systems, but Watson uses statistical methods for Natural Language Processing (NLP). Cyc Corp believes, with statistical analysis cannot make AI to infer and we need underlying knowledge to infer.
You can use IBM Watson Natural Language Classifier to use the contextual information within a sentence to place it in a particular class. For example if you have 100 different "types" of scene (outdoors, beach, mountain, indoor, urban etc) you could create a classifier that recognises the different descriptions and is able to correctly categorise sentences it has not seen before into their respective scene type.
NLC is a machine learning system, not just statistical analysis so it is able to learn how particular word and phrases relate to a particular type of scene and also understands the links between similar words using modern nlp techniques (i.e. Word vectors)
The drawback of this approach is that you need to build a set of scene descriptions and their respective type in order to train the classifier (probably 5-10 samples of each type) which can be a time consuming task. However this approach does scale up nicely (if you want to be able to recognise a new scene type you just need some examples of how you might describe it)
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 have been doing some capability testing with Apache OpenNLP, Which has the capability to Sentence detection, Tokenization, Name entity recognition. Now when i started looking at UIMA documents it is mentioned on the UIMA home page - "language identification" => "language specific segmentation" => "sentence boundary detection" => "entity detection (person/place names etc.)".
Which says that i can use UIMA to do the same task as done by OpenNLP. What added feature both have ? I am new to this area, Please help me to understand the uses and capability perspective of both.
As I understand the question, you are asking for the differences between the feature sets of Apache UIMA and Apache OpenNLP. Their feature sets barely have anything in common as these two projects have very different aims.
Apache UIMA is an open source implementation of the UIMA specification. The latter defines a conceptual framework for augmenting unstructured information (such as natural language produced by humans) with structured metadata so that computers can work with it.
As an example for an application working with unstructured information, let us take a an application that takes natural language text as input and marks all named entities in the given text, e.g.
Input text = "Bob's cat Charlie is chasing a mouse."
Result = "<NE>Bob</NE>'s cat <NE>Charlie</NE> is chasig a mouse."
To identify the named entities in this example (i.e. Bob and Charlie), several steps of natural language processing have to be performed. Without going into detail about what each of the steps does, a hypothetical system for named entity recognition might involve the following steps:
Data preparation
Sentence splitting
Tokenization
Token lemmatization
Part-of-speech tagging
Phrase detection
Classifying phrases as named entities or not
As you can see, such applications can be very intuitively modelled as sequences of components, and this is exactly what UIMA does. It models applications dealing with unstructed information as pipelines of components (called analytics in UIMA parlance). As you can imagine, many of the pipeline components listed above can be used for other tasks and so the architecture design of UIMA emphasizes reusability of components.
To avoid confusion, the UIMA standard itself doesn't provide any specific components, but defines an infrastructure for UIM (Unstructured Information Management) applications, e.g. workflows, data types, inter-component communication, and so on.
Apache OpenNLP on the other hand does exactly that, namely provide concrete implementations of NLP algorithms dealing with very specific tasks (sentence splitting, POS-tagging, etc.). The source of your confusion might be that it is possible to write Apache UIMA components that wrap OpenNLP tools. The OpenNLP project actually provides such components.
Whether you want to use the UIMA framework for your UIM applications depends on the size of the project. If it is small, I would go without UIMA and just use OpenNLP directly, as UIMA is rather heavy-weight and thus only adds complex yet (for small applications) unnecessary overhead. Also, due to its complexity, it takes a good amount of time to learn how to use it.
Summing up, Apache UIMA and Apache OpenNLP solve different problems, but since both deal with unstructured information, they can be combined profitably.
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: