Construction Of Large Knowledge Bases Research Articles

Knowledge bases in the age of big data analytics

This tutorial gives an overview on state-of-the-art methods for the automatic construction of large knowledge bases and harnessing them for data and text analytics. It covers both big-data methods for building knowledge bases and knowledge bases being assets for big-data applications. The tutorial also points out challenges and research opportunities.

Commonsense knowledge, ontology and ordinary language

Over two decades ago a quite revolution overwhelmingly replaced knowledge- based approaches in natural language processing (NLP) by quantitative (e.g., statistical, corpus-based, machine learning) methods. Although it is our firm belief that purely quanti- tative approaches cannot be the only paradigm for NLP, dissatisfaction with purely engi- neering approaches to the construction of large knowledge bases for NLP are somewhat justified. In this paper we hope to demonstrate that both trends are partly misguided and that the time has come to enrich logical semantics with an ontological structure that reflects our commonsense view of the world and the way we talk about in ordinary language. In this paper it will be demonstrated that assuming such an ontological structure a number of challenges in the semantics of natural language (e.g., metonymy, intensionality, copredica- tion, nominal compounds, etc.) can be properly and uniformly addressed.

A high-level representation language for the construction and use of large knowledge bases

The “conceptual” Knowledge Representation Language (KRL) proper to a general environment for the construction of Large Knowledge Bases (LKBs) involves two different but complementary aspects. The coding of the elementary events occurring in the real world (“descriptive” data = “Snoopy is Charlie Brown's beagle”) is organized around a “semantic predicate”; the conceptual units constructed in this way take the name of “predictive occurrences.” Moreover, the predicative occurrences can be associated, giving rise to “binding occurrences.” On the other hand, the “classes” representing the “general categories”, to which can be reduced all the basic entities of the application domain which appear in the predicative occurrences, are defined in terms ofa specialization hierarchy (“lexicon” = “A beagle is a sort of hound/a hound is a dog. . .); analogously, the occurrences (and their generic “templates”) are classified according to their own specialized hierarchy (“grammar”). The “lexicon” and the “grammar,” together, set up the “definitional” component of the language.