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ELU-KoOther # Description ELU-Ko is a module to find entities in Koren text. Based on the labels of the Dbpedia entities, the ELU-Ko module finds entity candidates in the given text. Then, it chooses entities among the candidates based on the scores of them. # Function ELU-Ko finds Korean Dbpedia entities in the given natural langauge text. For instance, suppose sentence "대한민국의 지방법원의 수는 총 18개이다." is given as input. We can find out that '대한민국' correspond to the entity http://ko.dbpedia.org/page/대한민국, that '지방법원' corresponds to http://ko.dbpedia.org/page/지방법원, and so on. The found entitie are returned as list for the output of the module. # Scope and limit ELU-Ko uses entities' disambiguation, redirection, and labels candidate choice. Therefore, those that is not mentioned in the Dbpedia cannot be found using the ELU-Ko. In addition, although ELU-Ko makes use of confidential score of label as well as neighboring entity candidates, there still exists several errors. # Language Dependency It only supports Korean text. (January 13, 2016, OKBQA3.5). However, the algorithm is independent to the language, and the coverage would be extended at OKBQA4(http://4.okbqa.org) You may use AGDIST for English entity detecton. Developer: Youngsik Kim {twilight@kaist.ac.kr} Maintainer: Jeong-uk Kim {prismriver@kaist.ac.kr}2016-03-05 06:58:19 UTCprismriver@kaist.ac.kr
En-Ko Local KB 2015Other # DBpedia local endpoint URI http://143.248.135.20:45103/sparql # Graph IRI # English DBpedia http://en.dbpedia2015-04.kaist.ac.kr http://en.dbpedia2015-10.kaist.ac.kr # Korean DBpedia http://ko.dbpedia2015-10.kaist.ac.kr http://ko.dbpedia2015.kaist.ac.kr 2016-06-23 02:16:22 UTCnam.sangha@gmail.com
SparqlatorQuery generation Description It is a wrapper to call the GraphFinder::sparqlator method, using the OKBQA framework API. It takes a template and a disambiguation structures which are produced by a template generation and a disambiguation modules, respectively, and produces a set of SPARQL queries which are supposed to represent the same query need represented by the template and disambiguation. Optionally, the parameter, max_hop may be set to specify the number of maximum hops for each path to be extended to. As it is to specify an integer value (between 1 and 3, usually), it may be simply encoded in the URL (see Web service URL example below). GraphFinder implements the triple variation operations proposed by [1]. Scope and limit The current version implements only the three operations, inversion, split, and instantiation, among the four proposed in [1]. Implementation of the last one, join is remained as a future work. Language dependency GraphFinder is language-independent, and so does the sparqlator module. References Jin-Dong Kim and Kevin Bretonnel Cohen, “Triple Pattern Variation Operations for Flexible Graph Search”, Proceedings of the 1st international workshop on Natural Language Interfaces for Web of Data (NLIWoD), 2014. 2015-08-28 00:33:26 UTCjindong.kim@gmail.com
Web InterfaceRendering OKBQA Web User Interface2016-06-24 01:07:19 UTCwiany11@kaist.ac.kr
TGM v1Template generation Description Templator is a module for dependency-driven SPARQL template generation from natural language. Templator takes in a question and generates pseudo-queries as well as a list of strings (so-called slots) for which data from the knowledge base is needed. Approach First, the question is linguistically analysed, annotating it with part-of-speech tags, dependency relations, and semantic role labels. Second, the resulting parse tree is transformed into a template, covering one possibility of the how natural language expressions correspond to constructs in the target SPARQL query. This is the template that is most faithful to the linguistic structure of the question. In order to also account for structural differences between the question and the target query, the template is modified by a sequence of steps that collapse or expand triples, yielding additional templates. The scoring of the templates follows a simple heuristics computing the number of nodes in the query body that are neither projection variables nor slots. In addition, each rewriting operation reduces the score by a predetermined factor. Function 1. NLP tools (e.g. the stanford parser) analyze the input question text into dependency parse tree. 2. Then Templator generates the pseudo query for SPARQL query based on the RULEs within dependency parse tree Scope and limit Current version (March, 2016) focused on the hand-crafted RULEs for dependency parse tree. So that the QA performance depends on the RULEs' quality and coverage. Issues and discusion * How to check/add/edit RULEs? ~/src/main/resources/rules/ - RULEs for English: SRL_rules_en.json - RULEs for Korean: SRL_rules_ko.json At this time (January 13, 2016, at OKBQA 3.5) we can not provide yet the web service to add/edit exist RULEs or use your own RULEs for the OKBQA platform. This web service would be provided before/during OKBQA 4 (http://4.okbqa.org) We believe that good RULEs would improve overall performance of (our/your own) QA system. * Does it works for Korean? It currently works for English and Korean. Yes. We've added simple RULEs for some Korean question words such as "무엇", "누구", "어떤", and "얼마나 많". Of course, this rules cover few cases. Now you can test this sentence: "어떤 강이 서울을 흐르는가?" using the following Sample curl command. * How to deploy it: $ git clone https://github.com/okbqa/templator $ cd templator $ mvn compile exec:java Source code Implementations are available on GitHub: github.com/okbqa/templategeneration github.com/okbqa/templator Contacts While general questions about Templator should be addressed to the original developer, Christina Unger {cunger@cit-ec.uni-bielefeld.de}, Younggyun Hahm {hahmyg@kaist.ac.kr} also can act as a contact point especially for the matter on Korean applications. 2015-08-28 01:21:29 UTCcunger@cit-ec.uni-bielefeld.de
DisambiguatorTemplate generation Description Disambiguator is a module to disambiguate class, properties and resouces within the output of Templator. Each URIs depends on knowledge base what we use. Then SPARQL query would be generated from the output of Disambiguator and Templator using QueryGenerator. Function Disambiguator is a module to disambiguate class, properties and resouces within the output of Templator. For instance, the sentence "Which rivers flow through Seoul?", Disambiguator detects entities using FOX (rivers, flow, Seoul) and disambiguates entities using AGDISTIS. (class:river, dbo:city, res:Seoul) Scope and limit Currently (March, 2016), Disambiguator works for only English. FOX and AGDISTIS-en are used. Also, now Disambiguator identifies entitis within DBpedia URIs only. Issues and discusion Is it work for Korean also? Not yet (January 13, 2016, OKBQA3.5). We have developed AGDISTIS-ko for Korean to disambiguate entities, which has exactly same I/O as well as AGDISTIS. However, there are some remaining works. please refer bellow section. This task would be a one of major tasks at OKBQA4(http://4.okbqa.org) Do you want to develop Korean Disambiguator? If you want, there are two remaining development issue. 1. The NER module including this processe: NER and generate the output as well as the input of AGDISTIS-ko. 2. The Wrapper module: Input: Templator output Output: Disambiguator output Contacts Maintainer: Axel-Cyrille Ngonga Ngomo {ngonga@informatik.uni-leipzig.de} For information about Korean Disambiguator, please contact Younggyun Hahm {hahmyg@kaist.ac.kr}2016-01-12 17:43:11 UTChahmyg@kaist.ac.kr
TGM JAVA WRAPPERTemplate generation Description It is a wrapper for Java Template Generation Module(TGM). If you want to develop a new TGM module, you can use it as a template for OKBQA pipeline. Because it has a function for format validation of input and output, and is also developed using REST API. The sample input and output are written by Korean. But you can also using by English. 2016-01-13 10:40:32 UTCnam.sangha@gmail.com
TGM Python WrapperTemplate generation This is a template generation module wrapper for python programmers. In the codes, it takes a input by RESTful API and checks whether it is the right form of an input of TGM. The following of the codes are for programmers to implement their own template generation logic. The last of the codes takes an output from the results of programmers' logic and checks whether it is the right form of an TGM output, and then returns it to clients by RESTful API.2016-06-24 07:15:49 UTCjiseong@kaist.ac.kr
TGM v2Template generation Description Module for dependency-driven SPARQL template generation from natural language, for English and Korean. It takes in a question and generates pseudo-queries as well as a list of strings (so-called slots) for which data from the knowledge base is needed. Approach First, the question is linguistically analysed, annotating it with part-of-speech tags, dependency relations, and semantic role labels. Second, the resulting parse tree is transformed into a template, covering one possibility of the how natural language expressions correspond to constructs in the target SPARQL query. This is the template that is most faithful to the linguistic structure of the question. The scoring of the templates follows a simple heuristics computing the number of nodes in the query body that are neither projection variables nor slots. A documentation of the rule format can be found in the GitHub repository: https://github.com/okbqa/rocknrole/tree/master/src/main/resources/rules Scope and limit It relies on hand-crafted rules for transforming dependency parses into templates, so its quality and coverage is only as good as the rules. How to deploy it $ git clone https://github.com/okbqa/rocknrole $ cd rocknrole $ ./deploy.sh Contact For general questions and problems, get in touch with Christina Unger {cunger@cit-ec.uni-bielefeld.de}. For questions related to Korean, you can also get in touch with Younggyun Hahm {hahmyg@kaist.ac.kr}.2016-07-19 07:37:34 UTCcunger@cit-ec.uni-bielefeld.de
KO TGM 2016Template generation Description SenTGM is a SRDF-based Korean Template Generation module for generating SPARQL template from natural language question. SPARQL template follows 'templator' module made by christina unger. Function 1. Translate natural language question to declarative sentence by rules. 2. Run SRDF module for this declarative sentence. 3. Convert SRDF output to SPARQL template. Scope and Limit Translating NLQ to DS is developed very simple. If translate-sub-module is developed better, the overall performance will be raise. References 1. Sangha Nam, et. al., "SRDF: Korean Open Information Extraction using Singleton Property", ISWC (Poster) 2015.2016-07-21 02:26:02 UTCnam.sangha@gmail.com
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