semantics

Ok so i have this query PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> SELECT DISTINCT (COUNT(?instance) AS ?count) WHERE { ?instance a <http://dbpedia.org/ontology/Ambassador> . } and the result is 286. Cool. Now I want to get the number of ambassadors that have http://dbpedia.org/property/name property. But PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> SELECT DISTINCT (COUNT(?instance) AS ?count) WHERE { ?instance a <http://dbpedia.org/ontology/Ambassador> . ?instance <http://dbpedia.org/property/name> ?name } results in 533 :(. So it is counting more because there are people

There is probably an easy to answer to this, but I can't even figure out how to formulate the Google query to find it. I'm writing SPARQL construct queries against a dataset that includes blank nodes. So if I do a query like CONSTRUCT {?x ?y ?z .} WHERE {?x ?y ?z .} Then one of my results might be: nm:John nm:owns _:Node Which is a problem if all of the _:Node nm:has nm:Hats triples don't also get into the query result somehow (because some parsers I'm using like rdflib for Python really don't like dangling bnodes). Is there a way to write my original CONSTRUCT query to recursively add all

i came across this as an interview question. This question seemed interesting. So, i am posting it here. Consider the operation which gives semantic error like division by zero. By default, python compiler gives output like "Invalid Operation" or something. Can we control the output that is given out by Python compiler, like print some other error message, skip that division by zero operation, and carry on with rest of the instructions? And also, how can i evaluate the cost of run-time semantic checks? There are many python experts here. I am hoping someone will throw some light on this.

I realise this is more of a semantic quest rather than a functionality quest. I have three types of compile-scope dependencies: Compile-only scope, not used at run-time. GWT client-side dev, MVP4G, RestyGWT, Source retention annotation processors. I use REST, so I don't need GWT server side. Provided - Hibernate jars required for compilation but provided by JBoss. Compile + runtime jars. For case 2, we could use provided scope. Case 3, we would use compile scope. However for case 1, I use provided scope, even though JBoss does not provide those files at all. Nor are they needed at run-time.

Do you know some paper or algorithm in NLP that is able to extract sentences from text that are related to given entity (term). I would like to process some reviews (mainly tech), but I found out that many reviews mention more then one product (they do comparation). I would like to extract from that text just sentences that are relevant to one product, or delete sentences that are irrelevant to particular named entity (product). My questin is how to do it? Is there some related papers? Is something like this done by some toolkit or api? What you want is a Named Entity Recognizer (NER). Given

Simple (I hope), HTML question. Let's say I have a column group that spans 3 columns. But it also spans 9 rows. But in actuality, I want there to be 3 levels of columns (so basically, 3 columns, split across 9 rows). The only objectives really are: a) avoid embedding tables (for various reasons) b) keep the sections modular. c) allow for styling of the semantically modular areas. So in the end, I would have something visually like: | col 1 | col 2 | col 3 | | row 1 | row 1 | row 1 | | row 2 | row 2 | row 2 | | row 3 | row 3 | row 3 | | col 4 | col 5 | col 6 | | row 4 | row 4 | row 4 | | row 5

The German website nandoo.net offers the possibility to shorten a news article. If you change the percentage value with a slider, the text changes and some sentences are left out. You can see that in action here: http://www.nandoo.net/read/article/299925/ The news article is on the left side and tags are marked. The slider is on the top of the second column. The more you move the slider to the left, the shorter the text becomes. How can you offer something like that? Are there any algorithms which you can use to achieve that? My idea was that their algorithm counts the number of tags and nouns