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Learning and exploiting concept networks with ConNeKTion

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Abstract

Studying, understanding and exploiting the content of a document collection require automatic techniques that can effectively support the users in extracting useful information from it and reason with this information. Concept networks (e.g., taxonomies) may play a relevant role in this perspective, but are seldom available, and cannot be manually built and maintained cheaply and reliably. On the other hand, automated learning of these resources from text needs to be robust with respect to missing or partial knowledge, because often only sparse fragments of the target network can be extracted. This work presents ConNeKTion, a tool that is able to learn concept networks from plain text and to structure and enrich them by finding concept generalizations. The proposed methodologies are general and applicable to any language. It also provides functionalities for the exploitation of the learned knowledge, and a control panel that allows the user to comfortably carry out these activities. Several experiments and applications are reported, showing the usefulness and flexibility of ConNeKTion.

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Notes

  1. 1 A vertex-induced sub-graph is a subset of the vertexes of a graph together with all edges in the graph whose endpoints are both in the subset.

  2. 2 A weak component is defined as a maximal sub-graph in which there exists a path between all pairs of vertexes (considering undirected edges).

  3. 3 First the synsets of each word are extracted from WordNet, then, for each synset, all the associated domains in WordNet Domains are selected, and finally each domain is weighted according to the density function presented in [1], depending on the number of domains to which each synset belongs, on the number of synsets associated to each word, and on the number of words that make up the sentence. Each synset of a word is weighted based on the weights of the associated domains, and the one with highest weight is selected.

  4. 4 Note that this is different than the spreading activation algorithm [4], in that (1) graph traversal is not affected by weights on edges nor thresholds, (2) we focus on paths rather than nodes, and specifically we are interested in the path(s) between two particular nodes rather than in the whole graph activation, hence (3) in our approach setting the initial activation weight of start nodes makes no sense, and (4) this allows to exploit a bi-directional partial search rather than a mono-directional complete graph traversal.

  5. 5 Again, this is not a spreading activation, even if weights on edges are exploited.

  6. 6 A technique to semi-automatically extract a domain-specific ontology from free text without using external resources but focusing on Hub Words. After building the ontology, the ‘Hub Weight’ of a word t is computed as:

    $$W(t) = \alpha w_{0} + \beta n + \gamma \sum_{i=1}^{n} w(t_{i})$$

    where w 0 is a given initial weight, n is the number of relationships in which t is involved, w(t i ) is the t f∗i d f weight of the i-th word related to t, and α+β+γ=1. These elements, with some modifications, appear in the first three terms of our formula.

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Acknowledgments

This work was partially funded by the Italian PON 2007-2013 project PON02_00563_3489339 “Puglia@Service”.

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  1. Department of Computer Science, University of Bari, via E. Orabona, 4, Bari, Italy

    Fulvio Rotella, Fabio Leuzzi & Stefano Ferilli

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  1. Fulvio Rotella
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Correspondence to Stefano Ferilli.

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Rotella, F., Leuzzi, F. & Ferilli, S. Learning and exploiting concept networks with ConNeKTion. Appl Intell 42, 87–111 (2015). https://doi.org/10.1007/s10489-014-0543-z

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