Gabor Melli's Knowledge Base (GM-RKB)

A Gabor Melli's knowledge base (GM-RKB) is a personal knowledge base in the form of a semantic wiki.

• Context:
  • It can (typically) be composed of GM-RKB Pages (concept pages, publication pages, person pages, ...)
  • It can (typically) have GM-RKB-Related Tasks.
  • It can (often) be a Semi-Structured Human-Readable Ontology.
  • It can support GM-RKB-based QA Tasks (such as domain-specific definitional QAs).
  • It can be supported by a GM-RKB Wiki System, which includes a GM-RKB Server within a GM-RKB system maintained by GM-RKB wikisite management (GM-RKB system management or GM-RKB content management).
  • It can be enhanced by a RKB Research Project.
  • It can be represented by a GM-RKB Snapshot, which can be the basis of a GM-RKB-based ontology, such as kddo1.
  • …
• Example(s):
  • the version of today, MediaWiki-based GM-RKB: http://GM-RKB.com.
  • the version of Jan 1, 2020.
  • the version of Jan 1, 2015.
  • the version of Jan 1, 2009, OddMuse-based.
  • …
• Counter-Example(s):
  • ProofWiki.
  • General Knowledge Base, such as Wikipedia.
  • Gabor Melli's Blog.
  • …
• See: SDOI Project, kddo Ontology, kdd09cma1 Corpus, Text Wikification System, WikiFixer.

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References

2019

• (Melli & Moreira, 2019) ⇒ Gabor Melli, and Olga Moreira (2019). "The GMRKB.com Semantic Wiki (2019)". SLKB@AKBC Accepted Abstract 9.
  • ABSTRACT: We introduce GM-RKB, a linguistically-rich online semantic wiki focusing on concepts and text from the scientific literature on machine learning and related fields of computing, statistics, mathematics, and physics. It systematically describes thousands of concepts for many machine learning-related tasks, systems and algorithms, along with their input/output data type and requirements. Further the text from thousands of scientific publications have their concept mentions semantically annotated and thus linked to concept entries in the wiki. To the best our knowledge, this interlinked ontology-corpus is one of the few scientific linguistically-rich semantic wiki resources freely available to the research community.

2012

• (Melli, 2012) ⇒ Gabor Melli. (2012). “Identifying Untyped Relation Mentions in a Corpus Given An Ontology.” In: Workshop Proceedings of TextGraphs-7 on Graph-based Methods for Natural Language Processing.
  • QUOTE: In this paper we present the SDOI_rmi text graph-based semi-supervised algorithm for the task for relation mention identification when the underlying concept mentions have already been identified and linked to an ontology. To overcome the lack of annotated data, we propose a labelling heuristic based on information extracted from the ontology.We evaluated the algorithm on the kdd09cma1 dataset using a leave-one-document-out framework and demonstrated an increase in F1 in performance over a co-occurrence based AllTrue baseline algorithm. An extrinsic evaluation of the predictions suggests a worthwhile precision on the more confidently predicted additions to the ontology.

2010a

• (Melli, 2010a) ⇒ Gabor Melli. (2010). “Concept Mentions within KDD-2009 Abstracts (kdd09cma1) Linked to a KDD Ontology (kddo1).” In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010).
  • ABSTRACT: We introduce the kddo1 ontology and semantically annotated kdd09cma1 corpus from the field of knowledge discovery in database (KDD) research. The corpus is based on the abstracts for the papers accepted into the KDD-2009 conference. Each abstract has its concept mentions identified and, where possible, linked to the appropriate concept in the ontology. The ontology is based on a human generated and readable semantic wiki focused on concepts and relationships for the domain along with other related topics, papers and researchers from information sciences. To our knowledge this is the first ontology and interlinked corpus for a subdiscipline within computing science. The dataset enables the evaluation of supervised approaches to semantic annotation of documents that contain a large number of high-level concepts relative the number of named entity mentions. We plan to continue to evolve the ontology based on the discovered relations within the corpus and to extend the corpus to cover other research paper abstracts from the domain. Both resources are publicly available at http://www.gabormelli.com/Projects/kdd/data/.

2010b

• (Melli & Ester, 2010) ⇒ Gabor Melli, and Martin Ester. (2010). “Supervised Identification and Linking of Concept Mentions to a Domain-Specific Ontology.” In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM 2010). doi:10.1145/1871437.1871712
  • ABSTRACT: We propose a purely supervised learning approach to the task of identifying concept mentions within a document and of linking these mentions to their corresponding concept in a given ontology. Concept mention identification is performed with a trained CRF sequential model. Each mention is associated with a set of candidate ontology concepts, and binary training feature vectors are generated for these pairings. We formalize the feature space to expand on those those proposed in the literature, and also propose the inclusion of features derived from the training corpus. Iterative classification is proposed as a method of handling collective decisions in a supervised manner. The approach, named SCMILO, is validated against the ability to identify the concept mentions within the 139 KDD-2009 conference paper abstracts, and to link these mentions to a domain-specific ontology for the field of data mining.

2008

• (Melli & McQuinn, 2008) ⇒ Gabor Melli, and Jerre McQuinn. (2008). “Requirements Specification Using Fact-Oriented Modeling: A Case Study and Generalization.” In: Proceedings of Workshop on Object-Role Modeling (ORM 2008). doi:10.1007/978-3-540-88875-8_98
  • QUOTE: Fact-oriented Modeling [3], [4], [7] is an technique that assists with the conceptual modeling of an IT Solution. The approach however has not yet been fully incorporated into software requirement specification standards [8], [9], [10], [12], [13], [14], [2]. With the introduction of such standards as Structured Business Vocabulary and Rules (SBVR) [5], [7] it is now possible to consistently employ Fact-oriented Modeling in the delivery of enterprise solutions.

     Fact-oriented Modeling depends upon a controlled vocabulary of Business Concepts which can be used by business and IT stakeholders to communicate in a common language, leaving little room for ambiguity. Many Microsoft legacy systems have physical data structures that do not reflect the business concepts and relationships that they support. Fact-oriented Modeling changes this paradigm by requiring that Business Concepts and the allowed actions and relationships between them are specified as Business Rules before the functional specification begins.

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