Sorry, I meant the Stanbol NLP API, not Stanford in my previous e-mail. By the way, does Open NLP have the ability to build dependency trees?
2013/6/23 Cristian Petroaca <cristian.petro...@gmail.com> > 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 >> > >
