From Sartre to Frege in Three Steps: A* Search for Enriching Semantic Text Similarity Measures
Authors: Davide Colla, Marco Leontino, Enrico Mensa, Daniele P. Radicioni (Università di Torino, 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper, we illustrate a preliminary investigation on semantic text similarity. In particular, the proposed approach is aimed at complementing and enriching the categorization results obtained by employing standard distributional resources. We found that the paths connecting entities and concepts from documents provide interesting information on the connections between document pairs. Such a semantic browsing device enables further semantic processing, aimed at unveiling contexts and hidden connections (possibly not explicitly mentioned in the documents) between text documents.

Introduction
In the last few years, many efforts have been spent to extract information contained in text documents, and a large number of resources have been developed that allow exploring domain-based knowledge, defining a rich set of specific semantic relationships between nodes (Vrandecic and Krötzsch, 2014; Auer et al., 2007; Navigli and Ponzetto, 2012). Being able to extract and to make available the semantic content of documents is a challenging task, with beneficial impact on different applications, such as document categorisation (Carducci et al., 2019), keyword extraction (Colla et al., 2017), question answering, text summarisation, semantic text comparison, and on building explanations/justifications for similarity judgements (Colla et al., 2018) and more. In this paper, we present an approach aimed at extracting meaningful information contained in text documents, also based on background information contained in an encyclopedic resource such as Wikidata (Vrandecic and Krötzsch, 2014). Although our approach has been devised on a specific application domain (PhD theses in philosophy), we argue that it can be easily extended to further application settings. The approach focuses on the ability to extract relevant pieces of information from text documents, and to map them onto the nodes of a knowledge graph, obtained from semantic networks representing encyclopedic and lexicographic knowledge. In this way, it is possible to compare different documents based on their graphical description, which has a direct anchoring to their semantic content. We propose a system to assess the similarity between textual documents, hybridising the propositional approach (such as traditional statements expressed through RDF triples) with a distributional description (Harris, 1954) of the nodes contained in the knowledge graph, that are represented with word embeddings (Mikolov et al., 2013; Camacho-Collados et al., 2015; Speer et al., 2017). This step allows obtaining similarity measures (based on vector descriptions, and on pathfinding algorithms) and explanations (represented as paths over a semantic network) more focused on the semantic definition of concepts and entities involved in the analysis.

Conclusion
In this paper, we have investigated the possibility of enriching semantic text similarity measures via symbolic and human-readable knowledge. We have shown that distributional approaches allow for a satisfactory classification of documents belonging to different topics; however, our preliminary experimentation showed that they are not able to capture the subtle aspects characterizing documents in close areas. As we have argued, exploiting paths over graphs to explore connections between document pairs may be beneficial in making explicit domain-specific links between documents. As a future work, we could refine the methodology related to the extraction of the concepts in the Knowledge Graph, defining approaches based on specific domain-related ontologies. Two relevant works, to these ends, are the PhilOnto ontology, that represents the structure of philosophical literature (Grenon and Smith, 2011), and the InPho taxonomy (Buckner et al., 2007), combining automated information retrieval methods with knowledge from domain experts. Both resources will be employed in order to extract a more concise, meaningful, and discriminative Knowledge Graph.