Apache UIMA and Semantic Search

Tommaso Teofili , Software Engineer at Adobe
this is because the original slide deck was made using Apple Keynote
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Tony Su
Believe this slide cannot be downloaded, the link points to a 'key' file instead of a PDF or other typical presentation file.
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Tommaso Teofili , Software Engineer at Adobe
the code snippet should've appeared with tags to highlight annotations:

||book||title||Pride and Prejudice||title|| was written by||author||
Jane Austen||author||||book||, while ||book||||author||Joseph
Conrad||author|| wrote ||title||Heart of Darkness||title||||book||.
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Tommaso Teofili , Software Engineer at Adobe
It means that if you use Lucas to 'write down' your documents' annotations in fields then you'll have documents enriched and that is good but people often have different understandings of the 'semantic search' topic. Infact even if you have done named entity recognition with UIMA and then written down named entities annotations to Lucene fields then you won't be able to make queries which take relations and context in count.
Take the following example borrowed from a previous conversation I had with Thilo Goetz:

Pride and Prejudice was written by
Jane Austen, while Joseph
Conrad wrote Heart of Darkness.

'If you take the Lucas approach, you will not be able to
find out what books Jane Austen has written, because authors
and titles are indexed in different fields, independently.'

I hope this clarifies things a little :-)
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bolonudo
what does 'Lucas example' mean? at the side number 4
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Hello everybody,
I am Tommaso Teofili, I am from Rome and I work in Sourcesense and I&#x2019;m an Apache UIMA committer.
In this talk I am going to tell you about Apache UIMA & Semantic Search.

Apache UIMA stands for Unstructerd Information Management Architecture, it's an architectural framework to manage large volumes of unstructured data.
It was an IBM project donated to Apache.
It's currently an Incubator podling and the new 2.3.0 release is coming.
It's also been approved as an Oasis standard.
By the way, I became Apache UIMA committer on August.

UIMA is made of components, which can be put in a pipeline to be executed.
Every Apache UIMA component is described via XML descriptors.
The most important of which is the Analysis Engine, that is responsible for the very analysis of documents.
Each AE can be aggregated using an aggregate XML descriptor.
To manage large volumes of data UIMA can scale using Asynchronous Scaleout.
Each UIMA pipeline is made of AEs, one or more flow controllers and CAS consumers, the component which is responsible of treating the output (usually CASs).

Apache UIMA is not inherently a semantic search tool.
The semantic search tool package for UIMA is not open source.
Lucas is a CAS consumer for UIMA capable of putting UIMA annotations inside Lucene indexes, a semi-semantic approach.

UIMA is well suited as a metadata generation engine for CM systems.
It can enrich data with annotations, entities, categories and so on, eventually linking them.
For those who know the US Jeopardy game, IBM it's building a system able to answer open-domain questions against humans and it's based on UIMA-AS.
We'll see two examples of the power of UIMA to boost semantic search.

Here's the first one:
Recently during my college studies I used UIMA to extract metadata to build semantic searches on a real estate market analysis tool called Macpi.

zone statistics -> UIMA to find zones, price, filter out bad matchings
frequent announces -> disambiguation, data filtering

Another little demo I&#x2019;m going to show is about integrating Apache UIMA and AlchemyAPI from Ochestr8.
AlchemyAPI provides, you guess, services to extract Entities from text, but what we are interested in here is using the linked data, to exploit, for example DBPedia, Freebase and so on data clouds.
So the power of UIMA here is again putting together in a pipeline many components, once my system learned I can easily substitute this component with a UIMA Dictionary using the ConceptMapper.
I get Alchemy component then I can put the OpenCalais component, my personal dictionary of specific terms, a machine learning engine based on what UIMA extracts and so on.
Here we&#x2019;ll see indexing documents by concepts.

Ok, let&#x2019;s go to the demos!