Query Sentences as Semantic (Sub) Networks

Booth, Joel; Eugenio, Barbara Di; Cruz, Isabel F.; Wolfson, Ouri

doi:10.1109/icsc.2009.84

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…Our method effectively uses large amounts of structured knowledge and can be used in principle with other such resources like, e.g., YAGO [25], provided they contain explicit semantic relations. Seminal work on representing natural language as semantic networks focused on queries [7]. Recently, graph-based representations from DBpedia have been explored by [28] for labeling topics, as obtained from a topic model, rather than providing structured representations of arbitrary texts.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-based graph document modeling

Schuhmacher

Ponzetto

2014

Proceedings of the 7th ACM International Conference on Web Search and Data Mining

108

107

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We propose a graph-based semantic model for representing document content. Our method relies on the use of a semantic network, namely the DBpedia knowledge base, for acquiring fine-grained information about entities and their semantic relations, thus resulting in a knowledge-rich document model. We demonstrate the benefits of these semantic representations in two tasks: entity ranking and computing document semantic similarity. To this end, we couple DBpedia's structure with an information-theoretic measure of concept association, based on its explicit semantic relations, and compute semantic similarity using a Graph Edit Distance based measure, which finds the optimal matching between the documents' entities using the Hungarian method. Experimental results show that our general model outperforms baselines built on top of traditional methods, and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks.

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Section: Related Workmentioning

confidence: 99%

Knowledge-based graph document modeling

Schuhmacher

Ponzetto

2014

Proceedings of the 7th ACM International Conference on Web Search and Data Mining

108

107

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Robust Natural Language Processing for Urban Trip Planning

Booth¹,

Eugenio

Cruz

et al. 2015

Applied Artificial Intelligence

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Navigating large, urban transportation networks is a complicated task. A user needs to negotiate the available modes of transportation, their schedules, and how they are interconnected. In this paper we present a Natural Language interface for trip planning in complex multimodal urban transportation networks. Our objective is to provide robust understanding of complex requests while giving the user flexibility in their language.We designed TRANQUYL, a Transportation Query Language for trip planning; we developed a usercentric ontology, that defines the concepts covered by the interface and allows for a broad vocabulary. NL2TRANQUYL, the software system built on these foundations, translates English requests into formal TRANQUYL queries. Through a detailed intrinsic evaluation, we show that NL2TRANQUYL is highly accurate and robust with respect to paraphrasing of requests as well as handling fragmented or telegraphic requests.

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Cognitive canonicalization of natural language queries using semantic strata

Roy

Zeng

2013

ACM Trans. Speech Lang. Process.

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Natural language search relies strongly on perceiving semantics in a query sentence. Semantics is captured by the relationship among the query words, represented as a network (graph). Such a network of words can be fed into larger ontologies, like DBpedia or Google Knowledge Graph, where they appear as subgraphs— fashioning the name subnetworks (subnets). Thus, subnet is a canonical form for interfacing a natural language query to a graph database and is an integral step for graph-based searching. In this article, we present a novel standalone NLP technique that leverages the cognitive psychology notion of semantic strata for semantic subnetwork extraction from natural language queries. The cognitive model describes some of the fundamental structures employed by the human cognition to construct semantic information in the brain, called semantic strata. We propose a computational model based on conditional random fields to capture the cognitive abstraction provided by semantic strata, facilitating cognitive canonicalization of the query. Our results, conducted on approximately 5000 queries, suggest that the cognitive canonicals based on semantic strata are capable of significantly improving parsing and role labeling performance beyond pure lexical approaches, such as parts-of-speech based techniques. We also find that cognitive canonicalized subnets are more semantically coherent compared to syntax trees when explored in graph ontologies like DBpedia and improve ranking of retrieved documents.

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Query Sentences as Semantic (Sub) Networks

Abstract: Abstract

Cited by 6 publications

References 10 publications

Knowledge-based graph document modeling

Knowledge-based graph document modeling

Robust Natural Language Processing for Urban Trip Planning

Cognitive canonicalization of natural language queries using semantic strata

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