AI Journal - Classic and Prominent Paper Awards

The AI Journal is pleased to announce two new awards for papers published in the journal:

- The AIJ Prominent Paper Award recognizes outstanding papers published not more than seven years ago in the AI Journal that are exceptional in their significance and impact.

- The AIJ Classic Paper Award recognizes outstanding papers published at least 15 calendar years ago in the AI Journal that are exceptional in their significance and impact.


The 2017 Classic and Prominent Paper Awards were given to the following papers:


Fast Planning Through Planning Graph Analysis. 
Avrim Blum, Merrick L. Furst
Artif. Intell. 90(1-2): 281-300 (1997)

This seminal paper changed the perspective on classical planning algorithms. 

Before the paper appeared, most of the planning approaches used back-chaining methods searching in plan space. Blum and Furst instead proposed to create a particular graph structure in an iterative deepening fashion for constraining
 a backward search from the goal, leading to a dramatic performance increase. Although the specific planning algorithm proposed by the authors did not prevail, the ideas behind the algorithm and the empirical methodology adopted, inspired 
current approaches such as SAT-based planning and heuristic search-based planning methods. The work also demonstrated that it is quite worthwhile to go off the beaten path and take a fresh view on existing algorithmic problems.

2017 PROMINENT PAPER AWARD - is shared by two papers this year

BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. 
Roberto Navigli, Simone Paolo Ponzetto
Artif. Intell. 193: 217-250 (2012) 


YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia. 
Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, Gerhard Weikum
Artif. Intell. 194: 28-61 (2013).

The prominent paper award for 2017 goes to two papers that have made outstanding outstanding contributions to the automatic construction of large knowledge bases and semantic networks from public domain sources such as Wikipedia and Wordnet. 

BabelNet is a multilingual lexical-semantic network that merges lexicographic information from open-source dictionaries.  Thanks to BabelNet, computational lexical semantic tasks such as disambiguation, information extraction, question answering, and, more in general, text understanding, can be performed for a large number of languages while at the same time preserving the connections across languages, so as to enable the joint analysis and disambiguation of text in multiple languages. 

YAGO2 is  a conveniently searchable, large-scale, highly accurate knowledge base of common facts in machine-processable form. It is invaluable for making sense of internet content and for supporting tasks such as semantic search and text disambiguation, and, in general, for building truly intelligent agents. The YAGO2 paper builds on the earlier YAGO system and focusses on the integration and construction of the spatial and temporal dimension in knowledge bases. 

Both  BabelNet and YAGO2 combine excellent science with a significant engineering effort.
They are not only enabling but also inspiring numerous other developments in artificial intelligence, natural language processing and the semantic web.

Here is the list of All Previous Award Winners.