Hi Rupert, I created jira https://issues.apache.org/jira/browse/STANBOL-1121. As you suggested I would start with extending the Stanford NLP with co-reference resolution but I think also with dependency trees because I also need to know the Subject of the sentence and the object that it affects, right?
Given that I need to extend the Stanford NLP API in Stanbol for co-reference and dependency trees, how do I proceed with this? Do I create 2 new sub-tasks to the already opened Jira? After that can I start implementing on my local copy of Stanbol and when I'm done I'll send you guys the patch fo review? Regards, Cristian 2013/6/18 Rupert Westenthaler <rupert.westentha...@gmail.com> > On Mon, Jun 17, 2013 at 10:18 PM, Cristian Petroaca > <cristian.petro...@gmail.com> wrote: > > Hi Rupert, > > > > Agreed on the SettingAnnotation/ParticipantAnnotation/OccurentAnnotation > > data structure. > > > > Should I open up a Jira for all of this in order to encapsulate this > > information and establish the goals and these initial steps towards these > > goals? > > Yes please. A JIRA issue for this work would be great. > > > How should I proceed further? Should I create some design documents that > > need to be reviewed? > > Usually it is the best to write design related text directly in JIRA > by using Markdown [1] syntax. This will allow us later to use this > text directly for the documentation on the Stanbol Webpage. > > best > Rupert > > > [1] http://daringfireball.net/projects/markdown/ > > > > Regards, > > Cristian > > > > > > 2013/6/17 Rupert Westenthaler <rupert.westentha...@gmail.com> > > > >> On Thu, Jun 13, 2013 at 8:22 PM, Cristian Petroaca > >> <cristian.petro...@gmail.com> wrote: > >> > HI Rupert, > >> > > >> > First of all thanks for the detailed suggestions. > >> > > >> > 2013/6/12 Rupert Westenthaler <rupert.westentha...@gmail.com> > >> > > >> >> Hi Cristian, all > >> >> > >> >> really interesting use case! > >> >> > >> >> In this mail I will try to give some suggestions on how this could > >> >> work out. This suggestions are mainly based on experiences and > lessons > >> >> learned in the LIVE [2] project where we built an information system > >> >> for the Olympic Games in Peking. While this Project excluded the > >> >> extraction of Events from unstructured text (because the Olympic > >> >> Information System was already providing event data as XML messages) > >> >> the semantic search capabilities of this system where very similar as > >> >> the one described by your use case. > >> >> > >> >> IMHO you are not only trying to extract relations, but a formal > >> >> representation of the situation described by the text. So lets assume > >> >> that the goal is to Annotate a Setting (or Situation) described in > the > >> >> text - a fise:SettingAnnotation. > >> >> > >> >> The DOLCE foundational ontology [1] gives some advices on how to > model > >> >> those. The important relation for modeling this Participation: > >> >> > >> >> PC(x, y, t) → (ED(x) ∧ PD(y) ∧ T(t)) > >> >> > >> >> where .. > >> >> > >> >> * ED are Endurants (continuants): Endurants do have an identity so > we > >> >> would typically refer to them as Entities referenced by a setting. > >> >> Note that this includes physical, non-physical as well as > >> >> social-objects. > >> >> * PD are Perdurants (occurrents): Perdurants are entities that > >> >> happen in time. This refers to Events, Activities ... > >> >> * PC are Participation: It is an time indexed relation where > >> >> Endurants participate in Perdurants > >> >> > >> >> Modeling this in RDF requires to define some intermediate resources > >> >> because RDF does not allow for n-ary relations. > >> >> > >> >> * fise:SettingAnnotation: It is really handy to define one resource > >> >> being the context for all described data. I would call this > >> >> "fise:SettingAnnotation" and define it as a sub-concept to > >> >> fise:Enhancement. All further enhancement about the extracted Setting > >> >> would define a "fise:in-setting" relation to it. > >> >> > >> >> * fise:ParticipantAnnotation: Is used to annotate that Endurant is > >> >> participating on a setting (fise:in-setting fise:SettingAnnotation). > >> >> The Endurant itself is described by existing fise:TextAnnotaion (the > >> >> mentions) and fise:EntityAnnotation (suggested Entities). Basically > >> >> the fise:ParticipantAnnotation will allow an EnhancementEngine to > >> >> state that several mentions (in possible different sentences) do > >> >> represent the same Endurant as participating in the Setting. In > >> >> addition it would be possible to use the dc:type property (similar as > >> >> for fise:TextAnnotation) to refer to the role(s) of an participant > >> >> (e.g. the set: Agent (intensionally performs an action) Cause > >> >> (unintentionally e.g. a mud slide), Patient (a passive role in an > >> >> activity) and Instrument (aids an process)), but I am wondering if > one > >> >> could extract those information. > >> >> > >> >> * fise:OccurrentAnnotation: is used to annotate a Perdurant in the > >> >> context of the Setting. Also fise:OccurrentAnnotation can link to > >> >> fise:TextAnnotaion (typically verbs in the text defining the > >> >> perdurant) as well as fise:EntityAnnotation suggesting well known > >> >> Events in a knowledge base (e.g. a Election in a country, or an > >> >> upraising ...). In addition fise:OccurrentAnnotation can define > >> >> dc:has-participant links to fise:ParticipantAnnotation. In this case > >> >> it is explicitly stated hat an Endurant (the > >> >> fise:ParticipantAnnotation) involved in this Perturant (the > >> >> fise:OccurrentAnnotation). As Occurrences are temporal indexed this > >> >> annotation should also support properties for defining the > >> >> xsd:dateTime for the start/end. > >> >> > >> >> > >> >> Indeed, an event based data structure makes a lot of sense with the > >> remark > >> > that you probably won't be able to always extract the date for a given > >> > setting(situation). > >> > There are 2 thing which are unclear though. > >> > > >> > 1. Perdurant : You could have situations in which the object upon > which > >> the > >> > Subject ( or Endurant ) is acting is not a transitory object ( such > as an > >> > event, activity ) but rather another Endurant. For example we can have > >> the > >> > phrase "USA invades Irak" where "USA" is the Endurant ( Subject ) > which > >> > performs the action of "invading" on another Eundurant, namely "Irak". > >> > > >> > >> By using CAOS, USA would be the Agent and Iraq the Patient. Both are > >> Endurants. The activity "invading" would be the Perdurant. So ideally > >> you would have a "fise:SettingAnnotation" with: > >> > >> * fise:ParticipantAnnotation for USA with the dc:type caos:Agent, > >> linking to a fise:TextAnnotation for "USA" and a fise:EntityAnnotation > >> linking to dbpedia:United_States > >> * fise:ParticipantAnnotation for Iraq with the dc:type caos:Patient, > >> linking to a fise:TextAnnotation for "Irak" and a > >> fise:EntityAnnotation linking to dbpedia:Iraq > >> * fise:OccurrentAnnotation for "invades" with the dc:type > >> caos:Activity, linking to a fise:TextAnnotation for "invades" > >> > >> > 2. Where does the verb, which links the Subject and the Object come > into > >> > this? I imagined that the Endurant would have a dc:"property" where > the > >> > property = verb which links to the Object in noun form. For example > take > >> > again the sentence "USA invades Irak". You would have the "USA" Entity > >> with > >> > dc:invader which points to the Object "Irak". The Endurant would have > as > >> > many dc:"property" elements as there are verbs which link it to an > >> Object. > >> > >> As explained above you would have a fise:OccurrentAnnotation that > >> represents the Perdurant. The information that the activity mention in > >> the text is "invades" would be by linking to a fise:TextAnnotation. If > >> you can also provide an Ontology for Tasks that defines > >> "myTasks:invade" the fise:OccurrentAnnotation could also link to an > >> fise:EntityAnnotation for this concept. > >> > >> best > >> Rupert > >> > >> > > >> > ### Consuming the data: > >> >> > >> >> I think this model should be sufficient for use-cases as described by > >> you. > >> >> > >> >> Users would be able to consume data on the setting level. This can be > >> >> done my simple retrieving all fise:ParticipantAnnotation as well as > >> >> fise:OccurrentAnnotation linked with a setting. BTW this was the > >> >> approach used in LIVE [2] for semantic search. It allows queries for > >> >> Settings that involve specific Entities e.g. you could filter for > >> >> Settings that involve a {Person}, activities:Arrested and a specific > >> >> {Upraising}. However note that with this approach you will get > results > >> >> for Setting where the {Person} participated and an other person was > >> >> arrested. > >> >> > >> >> An other possibility would be to process enhancement results on the > >> >> fise:OccurrentAnnotation. This would allow to a much higher > >> >> granularity level (e.g. it would allow to correctly answer the query > >> >> used as an example above). But I am wondering if the quality of the > >> >> Setting extraction will be sufficient for this. I have also doubts if > >> >> this can be still realized by using semantic indexing to Apache Solr > >> >> or if it would be better/necessary to store results in a TripleStore > >> >> and using SPARQL for retrieval. > >> >> > >> >> The methodology and query language used by YAGO [3] is also very > >> >> relevant for this (especially note chapter 7 SPOTL(X) > Representation). > >> >> > >> >> An other related Topic is the enrichment of Entities (especially > >> >> Events) in knowledge bases based on Settings extracted form > Documents. > >> >> As per definition - in DOLCE - Perdurants are temporal indexed. That > >> >> means that at the time when added to a knowledge base they might > still > >> >> be in process. So the creation, enriching and refinement of such > >> >> Entities in a the knowledge base seams to be critical for a System > >> >> like described in your use-case. > >> >> > >> >> On Tue, Jun 11, 2013 at 9:09 PM, Cristian Petroaca > >> >> <cristian.petro...@gmail.com> wrote: > >> >> > > >> >> > First of all I have to mention that I am new in the field of > semantic > >> >> > technologies, I've started to read about them in the last 4-5 > >> >> months.Having > >> >> > said that I have a high level overview of what is a good approach > to > >> >> solve > >> >> > this problem. There are a number of papers on the internet which > >> describe > >> >> > what steps need to be taken such as : named entity recognition, > >> >> > co-reference resolution, pos tagging and others. > >> >> > >> >> The Stanbol NLP processing module currently only supports sentence > >> >> detection, tokenization, POS tagging, Chunking, NER and lemma. > support > >> >> for co-reference resolution and dependency trees is currently > missing. > >> >> > >> >> Stanford NLP is already integrated with Stanbol [4]. At the moment it > >> >> only supports English, but I do already work to include the other > >> >> supported languages. Other NLP framework that is already integrated > >> >> with Stanbol are Freeling [5] and Talismane [6]. But note that for > all > >> >> those the integration excludes support for co-reference and > dependency > >> >> trees. > >> >> > >> >> Anyways I am confident that one can implement a first prototype by > >> >> only using Sentences and POS tags and - if available - Chunks (e.g. > >> >> Noun phrases). > >> >> > >> >> > >> > I assume that in the Stanbol context, a feature like Relation > extraction > >> > would be implemented as an EnhancementEngine? > >> > What kind of effort would be required for a co-reference resolution > tool > >> > integration into Stanbol? > >> > > >> > >> Yes in the end it would be an EnhancementEngine. But before we can > >> build such an engine we would need to > >> > >> * extend the Stanbol NLP processing API with Annotations for > co-reference > >> * add support for JSON Serialisation/Parsing for those annotation so > >> that the RESTful NLP Analysis Service can provide co-reference > >> information > >> > >> > At this moment I'll be focusing on 2 aspects: > >> > > >> > 1. Determine the best data structure to encapsulate the extracted > >> > information. I'll take a closer look at Dolce. > >> > >> Don't make to to complex. Defining a proper structure to represent > >> Events will only pay-off if we can also successfully extract such > >> information form processed texts. > >> > >> I would start with > >> > >> * fise:SettingAnnotation > >> * {fise:Enhancement} metadata > >> > >> * fise:ParticipantAnnotation > >> * {fise:Enhancement} metadata > >> * fise:inSetting {settingAnnotation} > >> * fise:hasMention {textAnnotation} > >> * fise:suggestion {entityAnnotation} (multiple if there are more > >> suggestions) > >> * dc:type one of fise:Agent, fise:Patient, fise:Instrument, > fise:Cause > >> > >> * fise:OccurrentAnnotation > >> * {fise:Enhancement} metadata > >> * fise:inSetting {settingAnnotation} > >> * fise:hasMention {textAnnotation} > >> * dc:type set to fise:Activity > >> > >> If it turns out that we can extract more, we can add more structure to > >> those annotations. We might also think about using an own namespace > >> for those extensions to the annotation structure. > >> > >> > 2. Determine how should all of this be integrated into Stanbol. > >> > >> Just create an EventExtractionEngine and configure a enhancement chain > >> that does NLP processing and EntityLinking. > >> > >> You should have a look at > >> > >> * SentimentSummarizationEngine [1] as it does a lot of things with NLP > >> processing results (e.g. connecting adjectives (via verbs) to > >> nouns/pronouns. So as long we can not use explicit dependency trees > >> you code will need to do similar things with Nouns, Pronouns and > >> Verbs. > >> > >> * Disambigutation-MLT engine, as it creates a Java representation of > >> present fise:TextAnnotation and fise:EntityAnnotation [2]. Something > >> similar will also be required by the EventExtractionEngine for fast > >> access to such annotations while iterating over the Sentences of the > >> text. > >> > >> > >> best > >> Rupert > >> > >> [1] > >> > https://svn.apache.org/repos/asf/stanbol/trunk/enhancement-engines/sentiment-summarization/src/main/java/org/apache/stanbol/enhancer/engines/sentiment/summarize/SentimentSummarizationEngine.java > >> [2] > >> > https://svn.apache.org/repos/asf/stanbol/trunk/enhancement-engines/disambiguation-mlt/src/main/java/org/apache/stanbol/enhancer/engine/disambiguation/mlt/DisambiguationData.java > >> > >> > > >> > Thanks > >> > > >> > Hope this helps to bootstrap this discussion > >> >> best > >> >> Rupert > >> >> > >> >> -- > >> >> | Rupert Westenthaler rupert.westentha...@gmail.com > >> >> | Bodenlehenstraße 11 ++43-699-11108907 > >> >> | A-5500 Bischofshofen > >> >> > >> > >> > >> > >> -- > >> | Rupert Westenthaler rupert.westentha...@gmail.com > >> | Bodenlehenstraße 11 ++43-699-11108907 > >> | A-5500 Bischofshofen > >> > > > > -- > | Rupert Westenthaler rupert.westentha...@gmail.com > | Bodenlehenstraße 11 ++43-699-11108907 > | A-5500 Bischofshofen >
