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> Today's Topics:
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> 1. Re: GSoC Participation (Marco Fossati)
> 2. Fact extraction from wikipedia text (Emilio Dorigatti)
> 3. Want to Contribute in GSOC 2015 for DBpedia (Harsh Garg)
> 4. Fwd: Re: [Dbpedia-discussion] Getting started with DBpedia
> (GSoC 2015) (Marco Fossati)
> 5. Re: Fwd: GSOC_2015 Fact Extraction from Wikipedia Text
> (Marco Fossati)
> 6. Re: Fact extraction from wikipedia text (Marco Fossati)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 02 Mar 2015 10:58:47 +0100
> From: Marco Fossati <[email protected]>
> Subject: Re: [Dbpedia-gsoc] GSoC Participation
> To: Emilio Dorigatti <[email protected]>,
> [email protected]
> Message-ID: <[email protected]>
> Content-Type: text/plain; charset=utf-8; format=flowed
>
> Hi Emilio,
>
> The following page will give you all the details of our ideas:
> http://wiki.dbpedia.org/gsoc2015/ideas#h460-6
>
> Cheers!
>
> On 2/23/15 8:07 PM, Emilio Dorigatti wrote:
> > Hello,
> > my name is Emilio Dorigatti. Currently I am attending the second year of
> > a bachelor degree in computer science at the university of Povo (Trento,
> > Italy). I am very interested in artificial intelligence and plan to get
> > a master degree in that field; in the meantime I am getting my hands
> > dirty with some projects for fun, especially about machine learning and
> > planning. Lately I've been taking an interest for linked data and the
> > semantic web and I would really love to deepen my knowledge on this
> > subject, dbpedia seems to be just the right project!
> >
> > Nowadays I program mostly in python, but I also know C, C# and, to some
> > extent, java. I am proficient in object oriented programming and have a
> > fairly good knowledge of functional programming. I will start with some
> > of the warm up tasks on github to get acquainted with scala and the
> > codebase, and I will also read about the proposed projects for the
> > Summer of Code. Is there anything else I can do?
> >
> > Emilio.
> >
> >
> >
> ------------------------------------------------------------------------------
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> > _______________________________________________
> > Dbpedia-gsoc mailing list
> > [email protected]
> > https://lists.sourceforge.net/lists/listinfo/dbpedia-gsoc
> >
>
> --
> Marco Fossati
> http://about.me/marco.fossati
> Twitter: @hjfocs
> Skype: hell_j
>
>
>
> ------------------------------
>
> Message: 2
> Date: Mon, 2 Mar 2015 11:40:48 +0100
> From: Emilio Dorigatti <[email protected]>
> Subject: [Dbpedia-gsoc] Fact extraction from wikipedia text
> To: [email protected]
> Message-ID:
> <CAJi717dD6f=
> [email protected]>
> Content-Type: text/plain; charset="utf-8"
>
> Hello,
> I am also interested in working in the project about fact extraction from
> wikipedia text, I would like to ask for some clarifications about the
> machine learning part of it. The core of the project is to train a
> classifier using a training set built following the approaches described in
> the linked papers. As I understood it, the following tasks are needed;
> given a sentence
>
> 1a. Identify all the LUs using NLP techniques;
> 2b. Identify all the entities in the sentence which may represent FEs
> using again NLP techniques (ASRL perhaps?)
> 2. Use the FrameNet definition for the identified LUs to find the required
> FEs;
> 3. Ask the user whether a certain entity fits a certain FE (for all
> entities and FEs);
> 4. Understand which is the correct LU based on the meanings given in step
> (3).
>
> In the linked papers few is mentioned about steps (1a) and (1b) (but
> clarification has already been asked for), step (2) is straightforward and
> step (4) has already been implemented, the classifier is needed for step
> (3). Thus, it has to answers questions such as "can this entity be this
> FE?" or "is this entity this FE in this context?" (the latter being a lot
> harder in my opinion). It is not clear to me, though, which features should
> be used to train this classifier.
>
> Frequently, in text classification, there is an one-to-one mapping between
> words and features; in this case FEs have to be used instead of words
> (FrameNet currently recognizes slightly more than 10k FEs). There is also a
> need for features identifying the possible entities, but clearly we cannot
> use the whole DBpedia knowledge base (roughly 4.6 million entities) for
> this. I see that FEs belonging to a frame are usually of different types,
> so I think using *classes* instead of *instances* could be a promising
> alternative (DBpedia has 685 classes). Probably other features are needed
> though.
>
> Sorry for the long wall of text, I tried to express my thoughts in the
> shortest way I could. What do you think?
>
> Emilio.
> -------------- next part --------------
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>
> ------------------------------
>
> Message: 3
> Date: Mon, 2 Mar 2015 16:42:22 +0530
> From: Harsh Garg <[email protected]>
> Subject: [Dbpedia-gsoc] Want to Contribute in GSOC 2015 for DBpedia
> To: [email protected]
> Message-ID:
> <
> cakq5ir5retrs5d97sghch9h92xby+b_ef8tavf3j6qt5uo-...@mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hello sir,
>
> sir myself Harsh Garg.
> Currently I am pursuing my B-tech degree from jaypee institute of
> information technology(3rd year).I want to contribute in this DBpedia
> project.I know i am a newbie. but i write many small programs like twiiter
> crawler using twittersearch libray in python and text file compression
> using c++,videos related website and many more as an academic project .
> I can easily code in python and c++ language.Sir please help me how i start
> this project.
> I am interested in
> *New Dynamic Extractors from Wikipedia Content with JSONpedia Faceted
> Browser*
> -------------- next part --------------
> An HTML attachment was scrubbed...
>
> ------------------------------
>
> Message: 4
> Date: Mon, 02 Mar 2015 12:27:15 +0100
> From: Marco Fossati <[email protected]>
> Subject: [Dbpedia-gsoc] Fwd: Re: [Dbpedia-discussion] Getting started
> with DBpedia (GSoC 2015)
> To: dbpedia-gsoc <[email protected]>
> Message-ID: <[email protected]>
> Content-Type: text/plain; charset=utf-8; format=flowed
>
> Forwarding this to the specific mailing list.
> @Alberto, please continue the conversation here.
> Cheers!
>
>
> -------- Forwarded Message --------
> Subject: Re: [Dbpedia-discussion] Getting started with DBpedia (GSoC 2015)
> Date: Mon, 02 Mar 2015 12:25:37 +0100
> From: Marco Fossati <[email protected]>
> To: Dimitris Kontokostas <[email protected]>, Alberto Nicoletti
> <[email protected]>
> CC: [email protected]
> <[email protected]>
>
> Hi Alberto,
>
> have a look at our idea page:
> http://wiki.dbpedia.org/gsoc2015/ideas#h460-6
>
> Cheers!
>
> On 2/27/15 9:52 AM, Dimitris Kontokostas wrote:
> > Hi Alberto and welcome to DBpedia
> >
> > please look at the suggested topics we provided. Then, depending on you
> > preferences we could give you some warm-up tasks related to the topics
> > of your interest.
> >
> > For everyone, a very good introduction to DBpedia is the following
> article:
> > Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris
> > Kontokostas, Pablo N. Mendes, Sebastian Hellmann, Mohamed Morsey,
> > Patrick van Kleef, S?ren Auer, Christian Bizer. DBpedia ? A Large-scale,
> > Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web
> > Journal, Vol. 6 No. 2, pp 167?195, 2015.
> >
> > Cheers,
> > Dimtiris
> >
> > On Tue, Feb 24, 2015 at 8:44 PM, Alberto Nicoletti <[email protected]
> > <mailto:[email protected]>> wrote:
> >
> > Hi everyone,
> > I'm a Computer Science student from University of Bologna, in Italy,
> > i'm looking forward to this year's Google Summer of Code and i've
> > seen DBpedia has been selected in 2013 and 2014 so, hoping it will
> > be selected this year too, i'm interested in this organization :)
> >
> > I just wanted to ask if you could give me some advice to get me
> > started and some documentation I can read to comprehend your work.
> >
> > I noticed there are some issues on GitHub tagged as "GSoC Warmup
> > task", so I think they are some little issues that can be resolved
> > from a "newbie" like me, am I right?
> >
> > I'm also new to open source development in a real organization so if
> > there is something I should know, I please you to let me know.
> >
> > Thank you very much in forward,
> > Alberto Nicoletti
> >
> >
>
> ------------------------------------------------------------------------------
> > Dive into the World of Parallel Programming The Go Parallel Website,
> > sponsored
> > by Intel and developed in partnership with Slashdot Media, is your
> > hub for all
> > things parallel software development, from weekly thought leadership
> > blogs to
> > news, videos, case studies, tutorials and more. Take a look and join
> the
> > conversation now. http://goparallel.sourceforge.net/
> > _______________________________________________
> > Dbpedia-discussion mailing list
> > [email protected]
> > <mailto:[email protected]>
> > https://lists.sourceforge.net/lists/listinfo/dbpedia-discussion
> >
> >
> >
> >
> > --
> > Kontokostas Dimitris
> >
> >
> >
> ------------------------------------------------------------------------------
> > Dive into the World of Parallel Programming The Go Parallel Website,
> sponsored
> > by Intel and developed in partnership with Slashdot Media, is your hub
> for all
> > things parallel software development, from weekly thought leadership
> blogs to
> > news, videos, case studies, tutorials and more. Take a look and join the
> > conversation now. http://goparallel.sourceforge.net/
> >
> >
> >
> > _______________________________________________
> > Dbpedia-discussion mailing list
> > [email protected]
> > https://lists.sourceforge.net/lists/listinfo/dbpedia-discussion
> >
>
> --
> Marco Fossati
> http://about.me/marco.fossati
> Twitter: @hjfocs
> Skype: hell_j
>
>
>
>
>
> ------------------------------
>
> Message: 5
> Date: Mon, 02 Mar 2015 12:53:51 +0100
> From: Marco Fossati <[email protected]>
> Subject: Re: [Dbpedia-gsoc] Fwd: GSOC_2015 Fact Extraction from
> Wikipedia Text
> To: kasun perera <[email protected]>, dbpedia-gsoc
> <[email protected]>
> Message-ID: <[email protected]>
> Content-Type: text/plain; charset=utf-8; format=flowed
>
> Hi Kasun and thanks for the feedback on the project idea!
> You can find my answers inline.
> Cheers!
>
> On 3/2/15 5:10 AM, kasun perera wrote:
> >
> > Forwarding my last email since I didn't get any feedback.
> > Thanks
> >
> > ---------- Forwarded message ----------
> >
> > Hi Marco and others
> >
> > I like to work on the Gsoc project "Fact Extraction from Wikipedia Text"
> > during this summer.
> >
> > I went through the project description and the research papers mentioned
> > under the description. I have few questions to clarify.
> >
> > 1- As mentioned in the project idea the main objective is the
> > implementation of a new text extractor. Will this need to be implemented
> > inside the current extraction-framework?
> Ideally yes.
> > Or would it be a completely new
> > tool?
> >
> > 2- Also it mentioned the use of NLP techniques to process Wikipedia
> > text. Does this means extraction of Dependency relationships to get the
> > frame elements (FE) and lexical unit(LU)?
> Dependency parsing may not be needed, since entity linking can be
> applied to fulfill the task.
> > There are several NLP
> > libraries like Stanford parser, RelEx, NLTK etc. Is there any decision
> > made which NLP library to use?
> NLTK could be a way to go if we decide to use Python, but there is no
> constraint on libraries.
> The ones that serve our purposes are the good ones. :-)
> >
> > 3- Also regarding the content of a Wikipedia page; do we use all the
> > sentences from the Wikipedia page? My idea is it's better if we can use
> > important sentences rather than all the sentences. If that is the better
> > idea we have to come up with a criteria to select important sentences.
> Good point.
> I would first proceed with a domain-specific use case (i.e., soccer) to
> assess the feasibility of the idea. Then, we can generalize.
> Hence, we want to extract specific facts from sentences that may trigger
> soccer-related frames.
> Verb extraction and ranking (i.e., step A of the idea) would cater for
> this task.
>
> Cheers!
> >
> >
> >
> > --
> > Regards
> >
> > Kasun Perera
> >
> >
> >
> >
> > --
> > Regards
> >
> > Kasun Perera
> >
>
> --
> Marco Fossati
> http://about.me/marco.fossati
> Twitter: @hjfocs
> Skype: hell_j
>
>
>
> ------------------------------
>
> Message: 6
> Date: Mon, 02 Mar 2015 14:48:39 +0100
> From: Marco Fossati <[email protected]>
> Subject: Re: [Dbpedia-gsoc] Fact extraction from wikipedia text
> To: Emilio Dorigatti <[email protected]>,
> [email protected]
> Message-ID: <[email protected]>
> Content-Type: text/plain; charset=utf-8; format=flowed
>
> Hi Emilio,
>
> On 3/2/15 11:40 AM, Emilio Dorigatti wrote:
> > Hello,
> > I am also interested in working in the project about fact extraction
> > from wikipedia text, I would like to ask for some clarifications about
> > the machine learning part of it. The core of the project is to train a
> > classifier using a training set built following the approaches described
> > in the linked papers. As I understood it, the following tasks are
> > needed; given a sentence
> >
> > 1a. Identify all the LUs using NLP techniques;
> > 2b. Identify all the entities in the sentence which may represent FEs
> > using again NLP techniques (ASRL perhaps?)
> Entity linking is the way to go.
> > 2. Use the FrameNet definition for the identified LUs to find the
> > required FEs;
> FrameNet may be either too specific or too complex for crowdsourcing.
> Hence, we should adapt/simplify the frame and FEs definitions accordingly.
> > 3. Ask the user whether a certain entity fits a certain FE (for all
> > entities and FEs);
> > 4. Understand which is the correct LU based on the meanings given in
> > step (3).
> The correct LU should be already there, and we want to minimize LU
> ambiguity, i.e., how many frames can be triggered by one LU.
> Thus, the selection of LU via verb ranking will be a VERY important step.
> >
> > In the linked papers few is mentioned about steps (1a) and (1b) (but
> > clarification has already been asked for), step (2) is straightforward
> > and step (4) has already been implemented, the classifier is needed for
> > step (3). Thus, it has to answers questions such as "can this entity be
> > this FE?" or "is this entity this FE in this context?" (the latter being
> > a lot harder in my opinion). It is not clear to me, though, which
> > features should be used to train this classifier.
> Good point.
> I already have a baseline including linguistic features other than the
> FEs and frames themselves (that will come as output of the crowdsourced
> annotation).
> We should first test it, and then tune the features if needed.
> >
> > Frequently, in text classification, there is an one-to-one mapping
> > between words and features; in this case FEs have to be used instead of
> > words (FrameNet currently recognizes slightly more than 10k FEs). There
> > is also a need for features identifying the possible entities, but
> > clearly we cannot use the whole DBpedia knowledge base (roughly 4.6
> > million entities) for this. I see that FEs belonging to a frame are
> > usually of different types, so I think using /classes/ instead of
> > /instances/ could be a promising alternative (DBpedia has 685 classes).
> +1 for the entity types. This feature is actually implemented as a
> suggestion mechanism in the referenced workshop paper, and we could
> reuse it as an extra feature.
> But first we need to focus on something that works, then we can tune.
> > Probably other features are needed though.
> >
> > Sorry for the long wall of text, I tried to express my thoughts in the
> > shortest way I could. What do you think?
> That's a great feedback, please keep up with it!
> Cheers!
> >
> > Emilio.
> >
> >
> >
> ------------------------------------------------------------------------------
> > Dive into the World of Parallel Programming The Go Parallel Website,
> sponsored
> > by Intel and developed in partnership with Slashdot Media, is your hub
> for all
> > things parallel software development, from weekly thought leadership
> blogs to
> > news, videos, case studies, tutorials and more. Take a look and join the
> > conversation now. http://goparallel.sourceforge.net/
> >
> >
> >
> > _______________________________________________
> > Dbpedia-gsoc mailing list
> > [email protected]
> > https://lists.sourceforge.net/lists/listinfo/dbpedia-gsoc
> >
>
> --
> Marco Fossati
> http://about.me/marco.fossati
> Twitter: @hjfocs
> Skype: hell_j
>
>
>
> ------------------------------
>
>
> ------------------------------------------------------------------------------
> Dive into the World of Parallel Programming The Go Parallel Website,
> sponsored
> by Intel and developed in partnership with Slashdot Media, is your hub for
> all
> things parallel software development, from weekly thought leadership blogs
> to
> news, videos, case studies, tutorials and more. Take a look and join the
> conversation now. http://goparallel.sourceforge.net/
>
> ------------------------------
>
> _______________________________________________
> Dbpedia-gsoc mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/dbpedia-gsoc
>
>
> End of Dbpedia-gsoc Digest, Vol 12, Issue 2
> *******************************************
>
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Thanks and Regards,
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+918007405601
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