Natural Language Annotations for the Semantic Web

2002 ◽  
pp. 1317-1331 ◽  
Author(s):  
Boris Katz ◽  
Jimmy Lin ◽  
Dennis Quan
Keyword(s):  
Semantic Web ◽  
Natural Language

scholarly journals Processing Natural Language Queries in Semantic Web Applications

2021 ◽  
Author(s):  
Neli Zlatareva ◽  
Devansh Amin
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Web Applications

scholarly journals From Natural Language to RDF Graphs with Pregroups

2017 ◽  
Author(s):  
Antonin Delpeuch ◽  
Anne Preller
Keyword(s):  
Side Effects ◽  
Semantic Web ◽  
Natural Language ◽  
Relative Clauses ◽  
Semantic Category ◽  
Target Language ◽  
Pregroup Grammars ◽  
Unbounded Dependencies ◽  
Efficient Type ◽  
Rdf Graphs

We define an algorithm translating natural language sentences to the formal syntax of RDF, an existential conjunctive logic widely used on the Semantic Web. Our translationis based on pregroup grammars, an efficient type-logical grammatical framework with atransparent syntax-semantics interface. We introduce a restricted notion of side effects inthe semantic category of finitely generated free semimodules over {0,1} to that end.The translation gives an intensional counterpart to previous extensional models.We establish a one-to-one correspondence between extensional models and RDF models such that satisfaction is preserved. Our translation encompasses the expressivity of the target language and supports complex linguistic constructions like relative clauses and unbounded dependencies.

Geocoding Tweets Based on Semantic Web and Ontologies

2018 ◽  
pp. 372-392
Author(s):  
Imelda Escamilla ◽  
Miguel Torres Ruíz ◽  
Marco Moreno Ibarra ◽  
Vladimir Luna Soto ◽  
Rolando Quintero ◽  
...  
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Mexico City ◽  
Spatial Relationships ◽  
Spatial Analyses ◽  
Human Ability ◽  
Share Information

Human ability to understand approximate references to locations, disambiguated by means of context and reasoning about spatial relationships, is the key to describe spatial environments and to share information about them. In this paper, we propose an approach for geocoding that takes advantage of the spatial relationships contained in the text of tweets, using semantic web, ontologies and spatial analyses. Microblog text has special characteristics (e.g. slang, abbreviations, acronyms, etc.) and thus represents a special variation of natural language. The main objective of this work is to associate spatial relationships found in text with a spatial footprint, to determine the location of the event described in the tweet. The feasibility of the proposal is demonstrated using a corpus of 200,000 tweets posted in Spanish related with traffic events in Mexico City.

SEMCL

2010 ◽  
Vol 1 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Weisen Guo ◽  
Steven B. Kraines
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Global Knowledge ◽  
Semantic Web Technologies ◽  
Natural Languages ◽  
Web Technologies ◽  
Language Knowledge ◽  
Cross Language

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing (NLP) technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model (SEMCL) for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.

Mining information from sentences through Semantic Web data and Information Extraction tasks

2020 ◽  
pp. 016555152093438
Author(s):  
Jose L. Martinez-Rodriguez ◽  
Ivan Lopez-Arevalo ◽  
Ana B. Rios-Alvarado
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Information Extraction ◽  
Knowledge Base ◽  
Semantic Similarity ◽  
Similarity Measure ◽  
Real World ◽  
Semantic Similarity Measure ◽  
Web Standards ◽  
Extract Information

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed by computers. Thus, the challenge is to identify and extract the elements of information that can be represented. Hence, this article presents a strategy to extract information from sentences and its representation with Semantic Web standards. Our strategy involves Information Extraction tasks and a hybrid semantic similarity measure to get entities and relations that are later associated with individuals and properties from a Knowledge Base to create RDF triples (Subject–Predicate–Object structures). The experiments demonstrate the feasibility of our method and that it outperforms the accuracy provided by a pattern-based method from the literature.

Natural Language Generation from Graphs

2014 ◽  
Vol 08 (03) ◽  
pp. 335-384 ◽  
Author(s):  
Ngan T. Dong ◽  
Lawrence B. Holder
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Web Search ◽  
Open Data ◽  
Natural Language Generation ◽  
Language Generation ◽  
Expert User ◽  
Computer Representation ◽  
Description Framework ◽  
Rdf Graphs

The Resource Description Framework (RDF) is the primary language to describe information on the Semantic Web. The deployment of semantic web search from Google and Microsoft, the Linked Open Data Community project along with the announcement of schema.org by Yahoo, Bing and Google have significantly fostered the generation of data available in RDF format. Yet the RDF is a computer representation of data and thus is hard for the non-expert user to understand. We propose a Natural Language Generation (NLG) engine to generate English text from a small RDF graph. The Natural Language Generation from Graphs (NLGG) system uses an ontology skeleton, which contains hierarchies of concepts, relationships and attributes, along with handcrafted template information as the knowledge base. We performed two experiments to evaluate NLGG. First, NLGG is tested with RDF graphs extracted from four ontologies in different domains. A Simple Verbalizer is used to compare the results. NLGG consistently outperforms the Simple Verbalizer in all the test cases. In the second experiment, we compare the effort spent to make NLGG and NaturalOWL work with the M-PIRO ontology. Results show that NLGG generates acceptable text with much smaller effort.

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