=Paper=
{{Paper
|id=None
|storemode=property
|title=Demo: Enriching Text with RDF/OWL Encoded Senses
|pdfUrl=https://ceur-ws.org/Vol-658/paper496.pdf
|volume=Vol-658
|dblpUrl=https://dblp.org/rec/conf/semweb/RusuSDFM10
}}
==Demo: Enriching Text with RDF/OWL Encoded Senses==
https://ceur-ws.org/Vol-658/paper496.pdf
Demo: Enriching Text with RDF/OWL Encoded Senses
Delia Rusu, Tadej Štajner, Lorand Dali, Blaž Fortuna, Dunja Mladenić,
Jožef Stefan Institute, Ljubljana, Slovenia
{delia.rusu, tadej.stajner, lorand.dali, blaz.fortuna, dunja.mladenic}@ijs.si
Abstract. This demo paper describes an extension of the Enrycher text
enhancement system, which annotates words in context, from a text fragment,
with RDF/OWL encoded senses from WordNet and OpenCyc. The extension is
based on a general purpose disambiguation algorithm which takes advantage of
the structure and/or content of knowledge resources, reaching state-of-the-art
performance when compared to other knowledge-lean word sense
disambiguation algorithms.
Keywords: RDF/OWL word sense representation.
1 Introduction
A variety of Semantic Web resources in the Linked Open Data (LOD) cloud can serve
as knowledge bases for identifying word senses; more general, like DBpedia, W3C
WordNet, OpenCyc, or more domain specific like the Gene Ontology, just to name a
few of them. Moreover, these resources complement each other, as they span across
several domains from music to chemistry and biology.
Enrycher [8] is a service-oriented natural language processing and information
extraction framework. It annotates text at various levels, listing: subject – predicate –
object triplets (interesting statements) visually interconnected in a semantic graph
representation, co-referenced named entities linked to DBpedia, Yago and OpenCyc,
keywords and DMOZ categories. In this Demo paper we present an extension of
Enrycher1, relying on a general purpose algorithm which can take advantage of
several Semantic Web resources to disambiguate text. This extension annotates words
in context with RDF/OWL encoded senses from WordNet [1] and OpenCyc 2. Given
an input text fragment, every word or collocation (word sequence) will be annotated
with the appropriate sense in context, and linked to the associated RDF resources
defining the sense, in both WordNet and OpenCyc. The motivation behind adding this
extension is to provide richer disambiguated annotations of words that are not named
entities, and to improve the semantic graph quality, by merging nodes that refer to the
same disambiguated concept.
Word sense disambiguation (WSD) is defined as identifying the meaning of words
in a given context, and has become a prerequisite for several Semantic Web specific
1 Demo video: http://marquis.ijs.si/delia/
2 http://sw.opencyc.org/
tasks like ontology mapping and reasoning. WSD techniques have been previously
introduced to validate ontology mappings, by analyzing the semantics of the
ontological terms; they exploit ontological context, as well as information provided
by WordNet. Aside from WordNet, another knowledge resource, namely Wikipedia
has been used for building sense tagged corpora, which have further been employed
to train a classifier, obtaining promising results [4]. Wikipedia was also used to
automatically extend WordNet with semantic relations (such as synonymy, antonymy,
hyponymy, etc.) [6]. However, the existing disambiguation systems mainly retrieve
WordNet senses that are not readily usable for Semantic Web applications. Our
extension can be easily integrated in other applications that require WSD as a
preprocessing step, as word senses are labeled with the corresponding disambiguated
RDF/OWL resource. Moreover, we take advantage of ontologies to find word senses,
and in future work we plan to add some domain ontologies that can better
disambiguate domain specific terminology.
The paper is structured as follows: we start by describing the Enrycher extension
integration in Section 2, continue with presenting the disambiguation algorithm in
Section 3 and conclude with a section on future work and the demo presentation.
2 Enrycher Extension Integration
Our RDF/OWL word sense annotation extension of Enrycher relies on the Text
Preprocessing component which performs sentence splitting, tokenization, part-of-
speech tagging and keyword extraction based on a bag-of-words model (see Fig. 1).
Both WordNet 3.0 and OpenCyc are processed offline, in order to extract structure
and content information. By structure we refer to the semantic relations: synonymy,
hypernymy, etc. specific to WordNet, as well as the generalization, specialization, etc.
relations encoded in OpenCyc. The content is given by the WordNet glosses and the
OpenCyc comments, and provides descriptions of the word sense.
Fig. 1. Enrycher components and their dependencies.
Given an input text fragment, every word or collocation will be annotated with the
appropriate sense in context from the aforementioned knowledge resources. If
existent, both RDF resources corresponding to WordNet and OpenCyc will be linked.
The following section elaborates on the proposed general purpose disambiguation
algorithm.
3 Word/Collocation Annotation
We have implemented an unsupervised semantic knowledge based word sense
disambiguation algorithm. It relies on the Viterbi algorithm for Hidden Markov
Model (HMM) part-of-speech tagging [2], and an initial version was described in [7].
The Viterbi algorithm is a common decoding algorithm for HMM, which was first
applied to speech and language processing in the context of speech recognition. We
have adapted the algorithm in order to determine, given the senses of words in a
sentence, the best sequence of senses that disambiguates the sentence. We start by
looking for the senses of nouns, verbs, adjectives and adverbs in one of the two
aforementioned knowledge resources. The sequence of observations O = o1o2...oT
will represent the T words we disambiguate, while the set of states Q = q1q2...qN
define the N senses for a given observation. The sequence of observation likelihoods
B=bi(ot) expresses the probability of an observation ot being generated from a state i.
They are obtained by computing the cosine similarity between an ambiguous word
description, as defined by the knowledge resource, and information provided by the
context (at the level of the sentence, paragraph, etc.). The transition probability matrix
A = a11a12…an1…anm is determined by computing the semantic relatedness between
the two senses in state i and j respectively. There have been several relatedness
measures proposed in the literature, some of them relying on the knowledge resource
structure, others on its content. We have implemented four such relatedness measures,
one of which exploiting the resource structure – Lexical Chains, while the others take
the resource content into account – Adapted Lesk, Vector and Vector Pairwise [5].
Fig. 2. Disambiguating the phrase Data mining algorithms using the proposed algorithm.
We explain the algorithm with the aid of the following example in Fig. 2. To
disambiguate the phrase “Data mining algorithms” using WordNet 3.0 as a sense
repository, we consider the senses of all words (the word “mining” having the sense
of “excavating” or “minelaying”), and in addition the sense of the collocation “data
mining” (data processing). We denote the sense part of speech and number in curly
brackets. The edges are labeled by state transitions. The collocation is modeled by
copying the corresponding sense state, and setting the transition between these two
states to 1.0. There is equal probability to reach any of the sense states of the first
word from the start state. Once the final state is reach, we back trace to find the states
with the highest associated scores.
We compared our system with others participating in the SemEval 2007 coarse
grained all words English disambiguation task based on WordNet senses, obtaining
precision/recall/F1 measures of 77.3, lower than the most frequent sense baseline of
78.9, but higher than the best unsupervised disambiguation algorithm participating in
the task (SUSSX-FR, based on parsing text and identifying the k nearest neighbors of
each word [3]) – 77.0. We also evaluated OpenCyc using a labor-on-demand
platform, asking people to determine the correct sense for a given word in context,
from a subset of OpenCyc sense definitions, obtaining an average F1 score of 37.55.
4 The Demo and Future Work
The demo will show how the implemented system’s web interface annotates
words/collocations in a given text fragment with RDF/OWL encoded senses from
WordNet and OpenCyc. We are also going to show how to make usage of the system
output programmatically, using the LarKC (the Large Knowledge Collider) platform,
in order to build Semantic Web applications that rely on WSD.
As for the future work, we plan to integrate other Semantic Web resources from
LOD datasets, such as DBpedia, and investigate differences in disambiguation results
when using distinct resources and the potential for combining different resources in
the same task. Additionally, we aim to apply our WSD algorithm to improve the
Enrycher generated semantic graphs.
References
1. Fellbaum, Ch., WordNet: An Electronic Lexical Database. MIT Press (1998)
2. Jurafsky, D., Martin, J. H. Speech and Language Processing: An introduction to natural
language processing, computational linguistics, and speech recognition. Prentice Hall Series
in Artificial Intelligence. (2008).
3. Koeling, R. and D. McCarthy. Sussx: WSD using Automatically Acquired Predominant
Senses. In Proceedings of the 4th SemEval. pp 314--317. Prague (2007).
4. Mihalcea, R., Using Wikipedia for Automatic Word Sense Disambiguation. In Proceedings
of the North American Chapter of the ACL (NAACL), Rochester, NY (2007)
5. Pedersen, T., Patwardhan, S. and Michelizzi, J. WordNet::Similarity - Measuring the
Relatedness of Concepts. In Proceedings of NAACL, pp 38--41, Boston, MA (2004).
6. Ponzetto, S.P., Navigli, R., Knowledge-rich Word Sense Disambiguation Rivaling
Supervised Systems. In Proceedings of the 48th ACL. pp 1522--1531. Uppsala, (2010).
7. Rusu, D., Fortuna, B. Mladenic, D. Improved Semantic Graphs with Word Sense
Disambiguation. Poster. 8th ISWC. Washington, DC (2009).
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