Unsubscribe > Le 27 janv. 2015 à 18:38, dbpedia-discussion-requ...@lists.sourceforge.net a > écrit : > > Send Dbpedia-discussion mailing list submissions to > dbpedia-discussion@lists.sourceforge.net > > To subscribe or unsubscribe via the World Wide Web, visit > https://lists.sourceforge.net/lists/listinfo/dbpedia-discussion > or, via email, send a message with subject or body 'help' to > dbpedia-discussion-requ...@lists.sourceforge.net > > You can reach the person managing the list at > dbpedia-discussion-ow...@lists.sourceforge.net > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of Dbpedia-discussion digest..." > > > Today's Topics: > > 1. Re: Freebase, Wikidata and the future of DBpedia (Paul Houle) > 2. CfP: 3rd Linked Data Mining Challenge at Know@LOD / ESWC 2015 > (Petar Ristoski) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Tue, 27 Jan 2015 12:27:05 -0500 > From: Paul Houle <ontolo...@gmail.com> > Subject: Re: [Dbpedia-discussion] Freebase, Wikidata and the future of > DBpedia > To: "M. Aaron Bossert" <maboss...@gmail.com> > Cc: Martin Br?mmer <bruem...@informatik.uni-leipzig.de>, > "dbpedia-discussion@lists.sourceforge.net" > <dbpedia-discussion@lists.sourceforge.net> > Message-ID: > <CAE__kdSgFq2veTsTHY+48c_Uynmu=nrgefcspoulz4yd+cs...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > DBpedia has a mission that is focused around extracting data from > Wikipedia. Importing data wholesale from Wikidata or something like that > seems to be inconsistent with that mission, but there are all kinds of > temporal and provenance things that could be teased out of Wikipedia, if > not out of the Infoboxes. > > I think most query scenarios are going to work like this > > [Pot of data with provenance information] -> [Data Set Representing a POV] > -> query > > I've been banging my head on the temporal aspect for a while and I am > convinced that the practical answer to a lot of problems is to replace > times with time intervals. Intervals can be used to model duration and > uncertainty and the overloading between those functions is not so bad > because usually you know from the context what the interval is being used > to represent. > > There is a lot of pain right now if you want to work with dates from either > DBpedia or Freebase because different kinds of dates are specified to > different levels of detail. If you make a plot of people's birthdays in > Freebase for instance you find a lot of people born on Jan 1 I think > because that is something 'plausible' to put in. > > A "birth date" could be resolved to a short interval (I know was I born at > 4:06 in the afternoon) and astrologers would like to know that, but the > frequent use of a calendar day is a statement about imprecision, although > defining my "birthday" as a set of one day intervals the interval is > reflecting a social convention. > > Anyway, there is an algebra over time intervals that is well accepted > > http://docs.jboss.org/drools/release/latest/drools-docs/html/DroolsComplexEventProcessingChapter.html#d0e10852 > > and could be implemented either as a native XSD data type or by some > structure involving "blank" nodes. > > > > On Tue, Jan 27, 2015 at 11:22 AM, M. Aaron Bossert <maboss...@gmail.com> > wrote: > >> Martin, >> >> When I first started working with RDF, I didn't fully "get" the full >> expressivity of it. All of the things you are saying can't be done >> (perhaps, easily?) are quite simple to implement. When compared to the >> property graph model, RDF, at first glance, seems inferior, but in reality, >> is much more expressive, in my opinion. Through reification, you can >> express all of the concepts that you are wanting to (provenance, date >> ranges, etc). At the end of the day, RDF's expressivity comes at the cost >> of verbosity, which, in my opinion is well worth it. >> >> If you would like some help in modeling your graph to represent the >> missing concepts that you are after, I will be happy to help you out with >> some more specific examples and pointers if it would be helpful to you. >> >> Aaron >> >>>> On Jan 27, 2015, at 06:33, Martin Br?mmer < >>> bruem...@informatik.uni-leipzig.de> wrote: >>> >>> Hi DBpedians! >>> >>> As you surely have noticed, Google has abandoned Freebase and it will >>> merge with Wikidata [1]. I searched the list, but did not find a >>> discussion about it. So here goes my point of view: >>> >>> When Wikidata was started, I hoped it would quickly become a major >>> contributor of quality data to the LOD cloud. But although the project >>> has a potentially massive crowd and is backed by Wikimedia, it does not >>> really care about the Linked Data paradigm as established in the >>> Semantic Web. RDF is more of an afterthought than a central concept. It >>> was a bit disappointing to see that Wikidata's impact on the LOD >>> community is lacking because of this. >>> >>> Now Freebase will be integrated into Wikidata as a curated, Google >>> engineering hardened knowledge base not foreign to RDF and Linked Data. >>> How the integration will be realized is not yet clear it seems. One >>> consequence is hopefully, that the LOD cloud grows by a significant >>> amount of quality data. But I wonder what the consequences for the >>> DBpedia project will be? If Wikimedia gets their own knowledge graph, >>> possible curated by their crowd, where is the place for the DBpedia? Can >>> DBpedia stay relevant with all the problems of an open source project, >>> all the difficulties with mapping heterogeneous data in many different >>> languages, the resulting struggle with data quality and consistency and >>> so on? >>> >>> So I propose being proactive about it: >>> >>> I see a large problem of the DBpedia with restrictions of the RDF data >>> model. Triples limit our ability to make statements about statements. I >>> cannot easily address a fact in the DBpedia and annotate it. This means: >>> >>> -I cannot denote the provenance of a statement. I especially cannot >>> denote the source data it comes from. Resource level provenance is not >>> sufficient if further datasets are to be integrated into DBpedia in the >>> future. >>> -I cannot denote a timespan that limits the validity of a statement. >>> Consider the fact that Barack Obama is the president of the USA. This >>> fact was not valid at a point in the past and won't be valid at some >>> point in the future. Now I might link the DBpedia page of Barack Obama >>> for this fact. Now if a DBpedia version is published after the next >>> president of the USA was elected, this fact might be missing from the >>> DBpedia and my link becomes moot. -This is a problem with >>> persistency. Being able to download old dumps of DBpedia is not a >>> sufficient model of persistency. The community struggles to increase >>> data quality, but as soon as a new version is published, it drops some >>> of the progress made in favour of whatever facts are found in the >>> Wikipedia dumps at the time of extraction. The old facts should persist, >>> not only in some dump files, but as linkable data. >>> >>> Being able to address these problems would also mean being able to fully >>> import Wikidata, including provenance statements and validity timespans, >>> and combine it with the DBpedia ontology (which already is an important >>> focus of development and rightfully so). It also means a persistent >>> DBpedia that does not start over in the next version. >>> >>> So how can it be realized? With reification of course! But most of us >>> resent the problems reification brings with it, the complications in >>> querying etc. The reification model itself is also unclear. There are >>> different proposals, blank nodes, reification vocabulary, graph names, >>> creating unique subproperties for each triple etc. Now I won't propose >>> using one of these models, this will surely be subject to discussion. >>> But the DBpedia can propose a model and the LOD community will adapt, >>> due to DBpedia's state and impact. I think it is time to up the standard >>> of handling provenance and persistence in the LOD cloud and DBpedia >>> should make the start. Especially in the face of Freebase and Wikidata >>> merging, I believe it is imperative for the DBpedia to move forward. >>> >>> regards, >>> Martin >>> >>> [1] https://plus.google.com/109936836907132434202/posts/bu3z2wVqcQc >> ------------------------------------------------------------------------------ >>> 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 >>> Dbpedia-discussion@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/dbpedia-discussion >> >> >> ------------------------------------------------------------------------------ >> 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 >> Dbpedia-discussion@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/dbpedia-discussion > > > > -- > Paul Houle > Expert on Freebase, DBpedia, Hadoop and RDF > (607) 539 6254 paul.houle on Skype ontolo...@gmail.com > http://legalentityidentifier.info/lei/lookup > -------------- next part -------------- > An HTML attachment was scrubbed... > > ------------------------------ > > Message: 2 > Date: Tue, 27 Jan 2015 18:38:11 +0100 > From: "Petar Ristoski" <petar.risto...@informatik.uni-mannheim.de> > Subject: [Dbpedia-discussion] CfP: 3rd Linked Data Mining Challenge at > Know@LOD / ESWC 2015 > To: <dbpedia-discussion@lists.sourceforge.net> > Message-ID: > <003e01d03a58$05aea8d0$110bfa70$@informatik.uni-mannheim.de> > Content-Type: text/plain; charset="iso-8859-2" > > [Apologies for cross-posting] > > > > ******************************************** > > CALL FOR CHALLENGE PARTICIPATION: > > ******************************************** > > > > 3rd Linked Data Mining Challenge organized in connection with the Know@LOD > 2015 workshop at ESWC Conference (ESWC 2015), May 31 - June 4, 2015. > > > > Venue: Portoroz, Slovenia > > Date: 01 June 2015 > > URL: > http://knowalod2015.informatik.uni-mannheim.de/en/linked-data-mining-challen > ge/ > > > > ******************************************** > > IMPORTANT DATES > > ******************************************** > > > > 27 March 2015: Submission of papers and solution deadline > > 03 April 2015: Notification of acceptance > > > > ******************************************** > > GENERAL OVERVIEW OF THE CHALLENGE > > ******************************************** > > > > Linked data represents a novel type of data source that has been so far > nearly untouched by advanced data mining methods. It breaks down many > traditional assumptions on source data and thus represents a number of > challenges: > > > > - While the individual published datasets typically follow a relatively > regular, relational-like (or hierarchical, in the case of taxonomic > classification) structure, the presence of semantic links among them makes > the resulting 'hyper-dataset' akin to general graph datasets. On the other > hand, compared to graphs such as social networks, there is a larger variety > of link types in the graph. > > - The datasets have been published for entirely different purposes, such as > statistical data publishing based on legal commitment of government bodies > vs. publishing of encyclopedic data by internet volunteers vs. data sharing > within a researcher community. This introduces further data modeling > heterogeneity and uneven degree of completeness and reliability. > > - The amount and diversity of resources as well as their link sets is > steadily growing, which allows for inclusion of new linked datasets into the > mining dataset nearly on the fly, at the same time, however, making the > feature selection problem extremely hard. > > > > The Linked Data Mining Challenge 2015 (LDMC) will consist of one task, which > is the prediction of the review class of movies. > > > > The best participant in the challenge will be awarded. The ranking of the > participants will be made by the LDMC organizers, taking into account both > the quality of the submitted LDMC paper (evaluated by Know@LOD workshop PC > members) and the prediction quality (i.e., accuracy, see below). > > > > ******************************************** > > TASK OVERVIEW > > ******************************************** > > > > The task concerns the prediction of a review of movies, i.e., "good" and > "bad". The initial dataset is retrieved from Metacritic, which offers an > average rating of all time reviews for a list of movies. The ratings were > used to divide the movies into classes, i.e., movies with score above 60 are > regarded as "good" movies, while movies with score less than 40 are regarded > as "bad" movies. For each movie we provide the corresponding DBpedia URI. > The mappings can be used to extract semantic features from DBpedia or other > LOD repositories to be exploited in the learning approaches proposed in the > challenge. > > > > ******************************************** > > SOURCE DATA, REQUIRED RESULTS, AND EVALUATION > > ******************************************** > > > > The dataset is available for download here: > http://knowalod2015.informatik.uni-mannheim.de/en/linked-data-mining-challen > ge/. It consists of training data of 1,600 instances for learning the > predictive models (this data contains the value of the target attribute) and > testing data of 400 instances for evaluating the models (this data is > without the target attribute). The target attribute to be predicted is the > Label attribute. > > The datasets contain semicolon separated values: movie's title "Movie", > movie's release date "Release date", movie's DBpedia URI "DBpedia_URI", > movie's label "Label" (only for the training set), and "id". A sample of the > training CSV file is as follows: > > > > "Movie";"Release date";"DBpedia_URI";"Label";"id" > > "Best Kept Secret";9/6/13 12:00 > AM;"http://dbpedia.org/resource/Best_Kept_Secret_(film)";"good";1.0 > > > > The participants have to submit the achieved results on testing data, i.e. > label of the movie. The results have to be delivered in a (syntactically > correct) CSV format that includes the predicted label. The submitted results > will be evaluated on a gold standard with respect to the accuracy. > > > > Beside the CSV file containing the predictions, the participants are > expected to submit a paper describing the used methods and techniques, as > well as the results obtained, i.e., the hypotheses perceived as interesting > either by the computational method or by the participants themselves. > > The participants should provide a detailed description of their approach, so > that it can be easily reproduced. For example, it should be clearly stated > what are the used feature sets (and how they have been created), the > preprocessing steps, the type of the predictor, the model parameters' values > and tuning, etc. > > > > The papers will be evaluated by the evaluation panel, both with respect to > the soundness and originality of the methods used and with respect to the > validity of the hypotheses and nuggets found. It should meet the standard > norms of scientific writing. > > > > ******************************************** > > ALLOWED DATASETS > > ******************************************** > > > > For building the movie review predictor, any dataset that follows the Linked > Open Data principles is allowed to be used. > > Non-LOD datasets are allowed to be used only if the participants later > publish those datasets in a way that would make them accessible using some > of the standard Semantic Web technologies, e.g., RDF, SPARQL, etc. > > For example, one may map the movies from the dataset to the corresponding > movies in a non-LOD dataset X, allowing to retrieve additional data from the > dataset X. Then, it is expected from the participants to publish the DBpedia > mappings to the dataset X movies, and the additional data retrieved from the > dataset X, for example, using RDF. > > > > *IMPORTANT: Since the Metacritic dataset is publicly available, we kindly > ask the participants not to use the Metacritic movies' rating score to tune > the predictor for the movies in the test set. Any submission found not to > comply with this rule will be disqualified. > > However, other information than the movies' rating score retrieved from > Metacritic is allowed, e.g., users' textual reviews for a given movie. > > > > ******************************************** > > SUBMISSION PROCEDURE > > ******************************************** > > > > Results submission > > > > - Register using the registration web form available at: > http://ldmc15.informatik.uni-mannheim.de/signup > > - Build a prediction model on the training set. > > - Apply the model on the test set to predict the label. > > - Submit the results at: http://ldmc15.informatik.uni-mannheim.de/submit > > - Your final score will be the one computed with respect to the last result > submission made before Friday March 27th > > > > ----- > > Paper submission > > > > - In addition to your results, you have to submit a paper describing your > solution > > - The paper format is Springer LNCS, with a limit of four pages > > - Papers are submitted online via Easychair before Friday March 27th > > > > ----- > > Presentation of Results > > > > - Challenge papers will be included in the workshop proceedings of Know@LOD > > - The authors of the best performing systems will be asked to present their > solution at the workshop > > > > For any questions related to the submission procedure, please address the > contact persons below. > > > > ******************************************** > > ORGANIZATION > > ******************************************** > > > > Petar Ristoski, University of Mannheim, Germany, petar.ristoski (at) > informatik.uni-mannheim.de > > Heiko Paulheim, University of Mannheim, Germany, heiko (at) > informatik.uni-mannheim.de > > Vojt?ch Sv?tek, University of Economics, Prague, svatek (at) vse.cz > > V?clav Zeman, University of Economics, Prague, vaclav.zeman (at) vse.cz > > > > > > -- > > Petar Ristoski > > Data and Web Science Group > > University of Mannheim > > Phone: +49 621 181 3705 > > B6, 26, Room C1.07 > > D-68159 Mannheim > > > > Mail: <mailto:petar.risto...@informatik.uni-mannheim.de> > petar.risto...@informatik.uni-mannheim.de > > Web: dws.informatik.uni-mannheim.de > > > > -------------- next part -------------- > An HTML attachment was scrubbed... > > ------------------------------ > > ------------------------------------------------------------------------------ > 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 > Dbpedia-discussion@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/dbpedia-discussion > > > End of Dbpedia-discussion Digest, Vol 95, Issue 24 > **************************************************
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