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Posted to dev@stanbol.apache.org by Dileepa Jayakody <di...@gmail.com> on 2013/07/31 23:06:17 UTC
[GSOC] FOAF Co-reference based Entity Disambiguation WorkFlow
Hi All,
As the third milestone of my project I will describe my initial design of
the FOAF Co-reference based entity disambiguation engine here.
The main disambiguation technique used here is FOAF co-reference. This aims
to merge multiple fise:EntityAnnotations identified by different surface
mentions in the text to a single FOAF entity by identifying co-reference
relationships between the entity-labels. I have an idea to introduce 2 new
fise properties called fise:coref, fise:not-coref to denote the coreference
relationships between the entities. Would like your thoughts on this idea.
Contextual information extracted from the ContentItem will be used to
identify the most suitable entity-annotation to the selected-text context.
The co-reference calculation will be initially done in a Rule-based manner.
Later a machine learning approach (SVM based) will be followed to upgrade
the system.
The disambiguation engine will calculate a disambiguation score (ds) by
performing FOAF co-reference operations on the contextual information
extract from the content items and modify the fise:confidence value for
each EntitySuggestions.
Basic co-reference rule to be used is :
{?p a owl:IFP. ?a ?p ?x. ?b ?p ?x) => {?a :coref ?b}
{?p a owl:FP . ?a ?p ?x. ?a ?p ?y.) => { ?x :coref ?y}
IFP : inverse-functional property
FP : functional property
coref : co-referent
The co-reference operations will be mainly 3 types. These 3 types will be
implemented as sub-modules in the disambiguation-engine. The
Map<TextAnnotation,Set<Suggestions>> will go through each module (in a
chain-mode) for improved disambiguation results. Mainly the disambiguation
process aims at People disambiguation;it also could be used for the
Organization type disambiguation.
The 3 sub-modules in the engine are as follows.
1. Co-reference by foaf-field literal matching :
This will perform a similarity matching of entity-label fields with FOAF
fields like foaf:name, givenName, firstName, familyName, nick and update
confidence values for co-referring entities (eg: matching firstName,
givenName and nickname mentions in the content: 'Tim Bernes' Lee is also
identified as 'timbl' as a nickname).
It should also detect TextAnnotations of email addresses (if available) and
match them with foaf:mbox,foaf:personalMailbox fields and phone numbers
with foaf:phone.
In this module, direct literal matching is performed.
2. Co-reference by relationship links :
This module perform neighborhood comparison with other People,
Organizations mentioned in the context. The relationships will be analysed
via foaf:knows field. foaf:seeAlso, foaf:sameAs will be used as the main
co-reference field to detect different EntityAnnotations referring to the
same Entity.
To detect relationships with organizations, foaf:schoolHomePage,
foaf:workplaceHomePage will be used.
To detect membership in groups, foaf:Group, foaf:member will be used as
keys.
To detect gender of the person in the context, the surface mention he/she
will be matched against the foaf:gender.
3. Topic based matching :
The fise:TopicAnnotations will be matched against foaf:interest (links to a
document), foaf:TopicInterest (links to an agent/entity), foaf:topic and
foaf:primaryTopic.
I will use the same algorithm to calculate the disambiguation score as used
in SolrMLT disambiguation engine in Stanbol.
The algorithm:
dc := (oc* cw / ( cw + dw)) + (ds * dw / ( cw + dw))
oc ... original-confidence [0..1]
ds ... disambiguation-score [0..1]
dc ... disambiguated - confidence [0..1]
cw ... original-confidence-weight
dw ... disambiguation-weight
Some questions I have:
- Is it a good idea to chain many enhancement engines other than my
foaf-site-engine such as NLP-Engines, TokenizerEngine, POSEngine to provide
many Entity Suggestions as possible before executing disambiguation?
- Can I use 'topic' enhancement engine in Stanbol to provide
fise:TopicAnnotations required in the 3rd module?
- Does SentimentAnalysis engine work? if so will
fise:SentimentAnnotations be useful for Topic based matching?
Would like your suggestions, ideas as much as possible to improve my FOAF
co-reference based disambiguation engine.
Below is a block diagram of the workflow.
[image: Inline image 1]
source :
http://creately.com/diagram/example/hjs4yd0e1/FOAF_Disambiguation_WorkFlow
Thanks,
Dileepa
Reference :
1. "Computing FOAF Co-reference Relations with Rules and Machine
Learning",Jennifer Sleeman and Tim Finin, University of Maryland, Baltimore
County, In proceedings of The Third International Workshop on Social Data
on the Web, November 2010
Re: [GSOC] FOAF Co-reference based Entity Disambiguation WorkFlow
Posted by Dileepa Jayakody <di...@gmail.com>.
Hi Reto,
Thanks a lot for the pointer.
I will look at the clerezza, rdf smusher to get a better idea.
Regards,
Dileepa
On Mon, Aug 26, 2013 at 6:17 PM, Reto Bachmann-Gmür <re...@wymiwyg.com>wrote:
> Hi Dileepa
>
>
> Basic co-reference rule to be used is :
> > {?p a owl:IFP. ?a ?p ?x. ?b ?p ?x) => {?a :coref ?b}
> > {?p a owl:FP . ?a ?p ?x. ?a ?p ?y.) => { ?x :coref ?y}
> >
> > IFP : inverse-functional property
> > FP : functional property
> > coref : co-referent
> >
>
> :coref is owl:sameAs.
>
> You might be interested in checking out
>
> http://clerezza.apache.org/apidocs/org/apache/clerezza/rdf/utils/Smusher.htmlwhich
> does smashing based on IFP, it should be quite straight forward to
> support FP as well. Also a map could be used to reduce the time complexity.
>
> For guessing identity when there is no IFP or FP you might use a tool like
> LIMES or SILK.
>
> Cheers,
> Reto
>
Re: [GSOC] FOAF Co-reference based Entity Disambiguation WorkFlow
Posted by Reto Bachmann-Gmür <re...@wymiwyg.com>.
Hi Dileepa
Basic co-reference rule to be used is :
> {?p a owl:IFP. ?a ?p ?x. ?b ?p ?x) => {?a :coref ?b}
> {?p a owl:FP . ?a ?p ?x. ?a ?p ?y.) => { ?x :coref ?y}
>
> IFP : inverse-functional property
> FP : functional property
> coref : co-referent
>
:coref is owl:sameAs.
You might be interested in checking out
http://clerezza.apache.org/apidocs/org/apache/clerezza/rdf/utils/Smusher.htmlwhich
does smashing based on IFP, it should be quite straight forward to
support FP as well. Also a map could be used to reduce the time complexity.
For guessing identity when there is no IFP or FP you might use a tool like
LIMES or SILK.
Cheers,
Reto
Re: [GSOC] FOAF Co-reference based Entity Disambiguation WorkFlow
Posted by Rupert Westenthaler <ru...@gmail.com>.
Hi
regarding:
On Tue, Aug 27, 2013 at 9:41 AM, Dileepa Jayakody
<di...@gmail.com> wrote:
>> >
>> > - Can I use 'topic' enhancement engine in Stanbol to provide
>> > fise:TopicAnnotations required in the 3rd module?
>> >
>> > If you can train a model based on your data it should work. The engine
>> would give your topics for the parsed text and your disambiguation engine
>> would compare the topics referenced of possible FOAF profiles with the one
>> detected by the TopicEngine for the text.
>>
>> But I am wondering where you can get the trainings data for such a model.
>> You would need a set of documents for all the categories used by FOAF
>> files.
>>
>
> I think I haven't yet grasped the topic-annotation concept in stanbol
> properly. I was expecting to use the topic-engine configured in the
> enhancement chain, and retrieve TopicAnnotations out of the box and use
> those TopicAnnotations to match against foaf:primaryTopic, foaf:interest
> properties in my engine...to train a model can I use an existing model
> rather than training it with foaf site I have implemented? Forgive me if
> this is a stupid question :)
>
There is no existing model for general purpose topics. ogrisel had
started with such (based on dbpedia categories) but never finished his
work. So you would need to train your own model.
>>
>> > - Does SentimentAnalysis engine work? if so will
>> > fise:SentimentAnnotations be useful for Topic based matching?
>> >
>> > The sentiment engines do work, but I do not see how they can improve
>> topic
>> based matching. Can you maybe explain your intensions.
>>
> I was initially thinking that, topics and sentiment-summaries can be
> co-related, therefore use thse sentimentAnnotations to map with
> foafi:primaryTopic/interest in above suggested 3rd module.
> Maybe this is something not so practical :)
>
The sentiment engines do select sections of the text and provide you
with a sentiment value [-1 .. +1] for those. The topic engine provides
you categories for the whole document. I do not se how you could
possible co-relate those information.
If you would also run the topic engine on smaller sections of the text
you could possible detect if a "topic" is mentioned in a positive or
negative context.
best
Rupert
> I will start implementing with a simple model to use several entity-linking
> engines to propose entityAnnotations as much as possible and use the 2nd
> module's approach to co-ref foaf relationships.
> Will update the thread with my progress..
>
> Thanks a lot for your valuable insight.
>
> Regards,
> Dileepa
>
>> best
>> Rupert
>>
>>
>> >
>> >
>> > Would like your suggestions, ideas as much as possible to improve my FOAF
>> > co-reference based disambiguation engine.
>> >
>> > Below is a block diagram of the workflow.
>> >
>> > [image: Inline image 1]
>> >
>> > source :
>> >
>> http://creately.com/diagram/example/hjs4yd0e1/FOAF_Disambiguation_WorkFlow
>> >
>> > Thanks,
>> > Dileepa
>> >
>> > Reference :
>> > 1. "Computing FOAF Co-reference Relations with Rules and Machine
>> > Learning",Jennifer Sleeman and Tim Finin, University of Maryland,
>> Baltimore
>> > County, In proceedings of The Third International Workshop on Social Data
>> > on the Web, November 2010
>> >
>> >
>> >
>> >
>> >
>> >
>> >
>>
>>
>> --
>> | Rupert Westenthaler rupert.westenthaler@gmail.com
>> | Bodenlehenstraße 11 ++43-699-11108907
>> | A-5500 Bischofshofen
>>
--
| Rupert Westenthaler rupert.westenthaler@gmail.com
| Bodenlehenstraße 11 ++43-699-11108907
| A-5500 Bischofshofen
Re: [GSOC] FOAF Co-reference based Entity Disambiguation WorkFlow
Posted by Dileepa Jayakody <di...@gmail.com>.
Hi Rupert and All,
On Mon, Aug 26, 2013 at 5:58 PM, Rupert Westenthaler <
rupert.westenthaler@gmail.com> wrote:
> Hi Dileepa,
>
> Sorry for the late response, but I was on vacation from 31st Jul and after
> coming back I had overlooked your mail. Thx for remembering me via IRC.
>
>
> On Wed, Jul 31, 2013 at 11:06 PM, Dileepa Jayakody <
> dileepajayakody@gmail.com> wrote:
>
> > Hi All,
> >
> > As the third milestone of my project I will describe my initial design of
> > the FOAF Co-reference based entity disambiguation engine here.
> >
> > The main disambiguation technique used here is FOAF co-reference. This
> > aims to merge multiple fise:EntityAnnotations identified by different
> > surface mentions in the text to a single FOAF entity by identifying
> > co-reference relationships between the entity-labels. I have an idea to
> > introduce 2 new fise properties called fise:coref, fise:not-coref to
> denote
> > the coreference relationships between the entities. Would like your
> > thoughts on this idea.
> >
> > Contextual information extracted from the ContentItem will be used to
> > identify the most suitable entity-annotation to the selected-text
> context.
> > The co-reference calculation will be initially done in a Rule-based
> > manner. Later a machine learning approach (SVM based) will be followed to
> > upgrade the system.
> > The disambiguation engine will calculate a disambiguation score (ds) by
> > performing FOAF co-reference operations on the contextual information
> > extract from the content items and modify the fise:confidence value for
> > each EntitySuggestions.
> >
> > Basic co-reference rule to be used is :
> > {?p a owl:IFP. ?a ?p ?x. ?b ?p ?x) => {?a :coref ?b}
> > {?p a owl:FP . ?a ?p ?x. ?a ?p ?y.) => { ?x :coref ?y}
> >
> > IFP : inverse-functional property
> > FP : functional property
> > coref : co-referent
> >
>
> I would advice to use "all" properties defined by the ontology and not only
> functional one (as indicated above).
>
> I guess you are referring to the properties defined in
propertyfilter.config during indexing?
Yes I have configured it with foaf:* to include all foaf properties during
indexing..
> >
> > The co-reference operations will be mainly 3 types. These 3 types will
> be
> > implemented as sub-modules in the disambiguation-engine. The
> > Map<TextAnnotation,Set<Suggestions>> will go through each module (in a
> > chain-mode) for improved disambiguation results. Mainly the
> disambiguation
> > process aims at People disambiguation;it also could be used for the
> > Organization type disambiguation.
> >
> > The 3 sub-modules in the engine are as follows.
> >
> > 1. Co-reference by foaf-field literal matching :
> > This will perform a similarity matching of entity-label fields with FOAF
> > fields like foaf:name, givenName, firstName, familyName, nick and update
> > confidence values for co-referring entities (eg: matching firstName,
> > givenName and nickname mentions in the content: 'Tim Bernes' Lee is also
> > identified as 'timbl' as a nickname).
> > It should also detect TextAnnotations of email addresses (if available)
> > and match them with foaf:mbox,foaf:personalMailbox fields and phone
> numbers
> > with foaf:phone.
> > In this module, direct literal matching is performed.
> >
> >
> To suggest possible Entities for mentions in the text is the responsibility
> of EntityLinking. IMO this first part has not much to do with
> disambiguation, but is mainly needed to get initial suggestions
> (fise:TextAnnotation with linked fise:EntityAnnotation). Those annotations
> will then be used for disambiguation in step (2) and (3).
There are already EnhancementEngines that can be used for linking against
> names, family names and nick names. For email and phone number you might
> need to write your won engines (maybe regex based).
>
> I would not recommend to create fise:EntityAnnotations for given names, as
> there will be way to much possibilities. With regard to that you should
> have a look at the Entity co-mention engine. This is able to suggest "Tim
> Bernes' Lee" for mentions of "Tim" if "Tim Bernes' Lee" was already
> mentioned by its full name earlier in the text.
>
> Thanks for the pointer, I will take a look at entitycomention engine for
this purpose..
>
>
> > 2. Co-reference by relationship links :
> > This module perform neighborhood comparison with other People,
> > Organizations mentioned in the context. The relationships will be
> analysed
> > via foaf:knows field. foaf:seeAlso, foaf:sameAs will be used as the main
> > co-reference field to detect different EntityAnnotations referring to the
> > same Entity.
> > To detect relationships with organizations, foaf:schoolHomePage,
> > foaf:workplaceHomePage will be used.
> > To detect membership in groups, foaf:Group, foaf:member will be used as
> > keys.
> > To detect gender of the person in the context, the surface mention he/she
> > will be matched against the foaf:gender.
> >
>
> > 3. Topic based matching :
> > The fise:TopicAnnotations will be matched against foaf:interest (links to
> > a document), foaf:TopicInterest (links to an agent/entity), foaf:topic
> and
> > foaf:primaryTopic.
> >
> >
> Those two suggestions look fine to me.
>
>
>
> > I will use the same algorithm to calculate the disambiguation score as
> > used in SolrMLT disambiguation engine in Stanbol.
> >
> > The algorithm:
> >
> > dc := (oc* cw / ( cw + dw)) + (ds * dw / ( cw + dw))
> >
> > oc ... original-confidence [0..1]
> > ds ... disambiguation-score [0..1]
> > dc ... disambiguated - confidence [0..1]
> > cw ... original-confidence-weight
> > dw ... disambiguation-weight
> >
> >
> > Some questions I have:
> >
> > - Is it a good idea to chain many enhancement engines other than my
> > foaf-site-engine such as NLP-Engines, TokenizerEngine, POSEngine to
> provide
> > many Entity Suggestions as possible before executing disambiguation?
> >
> > IMHO (1) "Co-reference by foaf-field literal matching " as you named it -
> or "Entity Linking" as I would call it - can be a combination of many
> engine. POS tagging, Named Entity Recognition, EntityhubLinking engines
> (configured for different foaf properties of your profiles), some Regex
> based engines for mail addresses and phone numbers, ... could all
> contribute to this.
> Also (2) and (3) could be done in multiple engines, but in that case you
> would need to find a solution to correctly calculate the final
> disambiguated fise:confidence value based on the individual results of the
> different disambiguation engines. Meaning that you will need to add some
> intermediate information to the RDF enhancement graph. If you do it in a
> single engine you could use an Java model for that. because of that IMO it
> should be simpler to start with a single engine.
>
> Yes, initially I will start developing it in a single engine :)
>
> >
> > - Can I use 'topic' enhancement engine in Stanbol to provide
> > fise:TopicAnnotations required in the 3rd module?
> >
> > If you can train a model based on your data it should work. The engine
> would give your topics for the parsed text and your disambiguation engine
> would compare the topics referenced of possible FOAF profiles with the one
> detected by the TopicEngine for the text.
>
> But I am wondering where you can get the trainings data for such a model.
> You would need a set of documents for all the categories used by FOAF
> files.
>
I think I haven't yet grasped the topic-annotation concept in stanbol
properly. I was expecting to use the topic-engine configured in the
enhancement chain, and retrieve TopicAnnotations out of the box and use
those TopicAnnotations to match against foaf:primaryTopic, foaf:interest
properties in my engine...to train a model can I use an existing model
rather than training it with foaf site I have implemented? Forgive me if
this is a stupid question :)
>
>
>
> > - Does SentimentAnalysis engine work? if so will
> > fise:SentimentAnnotations be useful for Topic based matching?
> >
> > The sentiment engines do work, but I do not see how they can improve
> topic
> based matching. Can you maybe explain your intensions.
>
> I was initially thinking that, topics and sentiment-summaries can be
co-related, therefore use thse sentimentAnnotations to map with
foafi:primaryTopic/interest in above suggested 3rd module.
Maybe this is something not so practical :)
I will start implementing with a simple model to use several entity-linking
engines to propose entityAnnotations as much as possible and use the 2nd
module's approach to co-ref foaf relationships.
Will update the thread with my progress..
Thanks a lot for your valuable insight.
Regards,
Dileepa
> best
> Rupert
>
>
> >
> >
> > Would like your suggestions, ideas as much as possible to improve my FOAF
> > co-reference based disambiguation engine.
> >
> > Below is a block diagram of the workflow.
> >
> > [image: Inline image 1]
> >
> > source :
> >
> http://creately.com/diagram/example/hjs4yd0e1/FOAF_Disambiguation_WorkFlow
> >
> > Thanks,
> > Dileepa
> >
> > Reference :
> > 1. "Computing FOAF Co-reference Relations with Rules and Machine
> > Learning",Jennifer Sleeman and Tim Finin, University of Maryland,
> Baltimore
> > County, In proceedings of The Third International Workshop on Social Data
> > on the Web, November 2010
> >
> >
> >
> >
> >
> >
> >
>
>
> --
> | Rupert Westenthaler rupert.westenthaler@gmail.com
> | Bodenlehenstraße 11 ++43-699-11108907
> | A-5500 Bischofshofen
>
Re: [GSOC] FOAF Co-reference based Entity Disambiguation WorkFlow
Posted by Rupert Westenthaler <ru...@gmail.com>.
Hi Dileepa,
Sorry for the late response, but I was on vacation from 31st Jul and after
coming back I had overlooked your mail. Thx for remembering me via IRC.
On Wed, Jul 31, 2013 at 11:06 PM, Dileepa Jayakody <
dileepajayakody@gmail.com> wrote:
> Hi All,
>
> As the third milestone of my project I will describe my initial design of
> the FOAF Co-reference based entity disambiguation engine here.
>
> The main disambiguation technique used here is FOAF co-reference. This
> aims to merge multiple fise:EntityAnnotations identified by different
> surface mentions in the text to a single FOAF entity by identifying
> co-reference relationships between the entity-labels. I have an idea to
> introduce 2 new fise properties called fise:coref, fise:not-coref to denote
> the coreference relationships between the entities. Would like your
> thoughts on this idea.
>
> Contextual information extracted from the ContentItem will be used to
> identify the most suitable entity-annotation to the selected-text context.
> The co-reference calculation will be initially done in a Rule-based
> manner. Later a machine learning approach (SVM based) will be followed to
> upgrade the system.
> The disambiguation engine will calculate a disambiguation score (ds) by
> performing FOAF co-reference operations on the contextual information
> extract from the content items and modify the fise:confidence value for
> each EntitySuggestions.
>
> Basic co-reference rule to be used is :
> {?p a owl:IFP. ?a ?p ?x. ?b ?p ?x) => {?a :coref ?b}
> {?p a owl:FP . ?a ?p ?x. ?a ?p ?y.) => { ?x :coref ?y}
>
> IFP : inverse-functional property
> FP : functional property
> coref : co-referent
>
I would advice to use "all" properties defined by the ontology and not only
functional one (as indicated above).
>
> The co-reference operations will be mainly 3 types. These 3 types will be
> implemented as sub-modules in the disambiguation-engine. The
> Map<TextAnnotation,Set<Suggestions>> will go through each module (in a
> chain-mode) for improved disambiguation results. Mainly the disambiguation
> process aims at People disambiguation;it also could be used for the
> Organization type disambiguation.
>
> The 3 sub-modules in the engine are as follows.
>
> 1. Co-reference by foaf-field literal matching :
> This will perform a similarity matching of entity-label fields with FOAF
> fields like foaf:name, givenName, firstName, familyName, nick and update
> confidence values for co-referring entities (eg: matching firstName,
> givenName and nickname mentions in the content: 'Tim Bernes' Lee is also
> identified as 'timbl' as a nickname).
> It should also detect TextAnnotations of email addresses (if available)
> and match them with foaf:mbox,foaf:personalMailbox fields and phone numbers
> with foaf:phone.
> In this module, direct literal matching is performed.
>
>
To suggest possible Entities for mentions in the text is the responsibility
of EntityLinking. IMO this first part has not much to do with
disambiguation, but is mainly needed to get initial suggestions
(fise:TextAnnotation with linked fise:EntityAnnotation). Those annotations
will then be used for disambiguation in step (2) and (3).
There are already EnhancementEngines that can be used for linking against
names, family names and nick names. For email and phone number you might
need to write your won engines (maybe regex based).
I would not recommend to create fise:EntityAnnotations for given names, as
there will be way to much possibilities. With regard to that you should
have a look at the Entity co-mention engine. This is able to suggest "Tim
Bernes' Lee" for mentions of "Tim" if "Tim Bernes' Lee" was already
mentioned by its full name earlier in the text.
> 2. Co-reference by relationship links :
> This module perform neighborhood comparison with other People,
> Organizations mentioned in the context. The relationships will be analysed
> via foaf:knows field. foaf:seeAlso, foaf:sameAs will be used as the main
> co-reference field to detect different EntityAnnotations referring to the
> same Entity.
> To detect relationships with organizations, foaf:schoolHomePage,
> foaf:workplaceHomePage will be used.
> To detect membership in groups, foaf:Group, foaf:member will be used as
> keys.
> To detect gender of the person in the context, the surface mention he/she
> will be matched against the foaf:gender.
>
> 3. Topic based matching :
> The fise:TopicAnnotations will be matched against foaf:interest (links to
> a document), foaf:TopicInterest (links to an agent/entity), foaf:topic and
> foaf:primaryTopic.
>
>
Those two suggestions look fine to me.
> I will use the same algorithm to calculate the disambiguation score as
> used in SolrMLT disambiguation engine in Stanbol.
>
> The algorithm:
>
> dc := (oc* cw / ( cw + dw)) + (ds * dw / ( cw + dw))
>
> oc ... original-confidence [0..1]
> ds ... disambiguation-score [0..1]
> dc ... disambiguated - confidence [0..1]
> cw ... original-confidence-weight
> dw ... disambiguation-weight
>
>
> Some questions I have:
>
> - Is it a good idea to chain many enhancement engines other than my
> foaf-site-engine such as NLP-Engines, TokenizerEngine, POSEngine to provide
> many Entity Suggestions as possible before executing disambiguation?
>
> IMHO (1) "Co-reference by foaf-field literal matching " as you named it -
or "Entity Linking" as I would call it - can be a combination of many
engine. POS tagging, Named Entity Recognition, EntityhubLinking engines
(configured for different foaf properties of your profiles), some Regex
based engines for mail addresses and phone numbers, ... could all
contribute to this.
Also (2) and (3) could be done in multiple engines, but in that case you
would need to find a solution to correctly calculate the final
disambiguated fise:confidence value based on the individual results of the
different disambiguation engines. Meaning that you will need to add some
intermediate information to the RDF enhancement graph. If you do it in a
single engine you could use an Java model for that. because of that IMO it
should be simpler to start with a single engine.
>
> - Can I use 'topic' enhancement engine in Stanbol to provide
> fise:TopicAnnotations required in the 3rd module?
>
> If you can train a model based on your data it should work. The engine
would give your topics for the parsed text and your disambiguation engine
would compare the topics referenced of possible FOAF profiles with the one
detected by the TopicEngine for the text.
But I am wondering where you can get the trainings data for such a model.
You would need a set of documents for all the categories used by FOAF files.
>
> - Does SentimentAnalysis engine work? if so will
> fise:SentimentAnnotations be useful for Topic based matching?
>
> The sentiment engines do work, but I do not see how they can improve topic
based matching. Can you maybe explain your intensions.
best
Rupert
>
>
> Would like your suggestions, ideas as much as possible to improve my FOAF
> co-reference based disambiguation engine.
>
> Below is a block diagram of the workflow.
>
> [image: Inline image 1]
>
> source :
> http://creately.com/diagram/example/hjs4yd0e1/FOAF_Disambiguation_WorkFlow
>
> Thanks,
> Dileepa
>
> Reference :
> 1. "Computing FOAF Co-reference Relations with Rules and Machine
> Learning",Jennifer Sleeman and Tim Finin, University of Maryland, Baltimore
> County, In proceedings of The Third International Workshop on Social Data
> on the Web, November 2010
>
>
>
>
>
>
>
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