TY - CONF AU - Lenci, Alessandro AU - Montemagni, Simonetta AU - Pirrelli, Vito AU - Venturi, Giulia A2 - Casanovas, Pompeu A2 - Biasiotti, Maria Angela A2 - Francesconi, Enrico A2 - Sagri, Maria-Teresa T1 - NLP-based Ontology Learning from Legal Texts. A Case Study. T2 - LOAIT PB - CEUR-WS.org CY - PY - 2007/ M2 - VL - 321 IS - SP - 113 EP - 129 UR - http://dblp.uni-trier.de/db/conf/icail/loait2007.html#LenciMPV07 M3 - KW - learning KW - ol KW - ontology L1 - SN - N1 - dblp N1 - AB - ER - TY - CONF AU - Brewster, Christopher AU - Ciravegna, Fabio AU - Wilks, Yorick A2 - T1 - User-Centred Ontology Learning for Knowledge Management T2 - NLDB '02: Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers PB - Springer-Verlag CY - London, UK PY - 2002/ M2 - VL - IS - SP - 203 EP - 207 UR - http://portal.acm.org/citation.cfm?id=666125 M3 - KW - learning KW - ol KW - ontology L1 - SN - 3-540-00307-X N1 - User-Centred Ontology Learning for Knowledge Management N1 - AB - ER - TY - JOUR AU - Navigli, R. AU - Velardi, P. AU - Gangemi, A. T1 - Ontology learning and its application to automated terminology translation JO - Intelligent Systems, IEEE PY - 2003/jan-feb VL - 18 IS - 1 SP - 22 EP - 31 UR - http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=1179190 M3 - 10.1109/MIS.2003.1179190 KW - learning KW - ol KW - ontology L1 - SN - N1 - IEEE Xplore# Wrapper Result N1 - AB - Our OntoLearn system is an infrastructure for automated ontology learning from domain text. It is the only system, as far as we know, that uses natural language processing and machine learning techniques, and is part of a more general ontology engineering architecture. We describe the system and an experiment in which we used a machine-learned tourism ontology to automatically translate multiword terms from English to Italian. The method can apply to other domains without manual adaptation. ER - TY - JOUR AU - Jiang, Xing AU - Tan, Ah-Hwee T1 - CRCTOL: A semantic-based domain ontology learning system JO - J. Am. Soc. Inf. Sci. Technol. PY - 2010/ VL - 61 IS - 1 SP - 150 EP - 168 UR - http://portal.acm.org/citation.cfm?id=1672957.1672980&coll=portal&dl=ACM M3 - http://dx.doi.org/10.1002/asi.v61:1 KW - ol L1 - SN - N1 - CRCTOL: A semantic-based domain ontology learning system N1 - AB - Domain ontologies play an important role in supporting knowledge-based applications in the Semantic Web. To facilitate the building of ontologies, text mining techniques have been used to perform ontology learning from texts. However, traditional systems employ shallow natural language processing techniques and focus only on concept and taxonomic relation extraction. In this paper we present a system, known as Concept-Relation-Concept Tuple-based Ontology Learning (CRCTOL), for mining ontologies automatically from domain-specific documents. Specifically, CRCTOL adopts a full text parsing technique and employs a combination of statistical and lexico-syntactic methods, including a statistical algorithm that extracts key concepts from a document collection, a word sense disambiguation algorithm that disambiguates words in the key concepts, a rule-based algorithm that extracts relations between the key concepts, and a modified generalized association rule mining algorithm that prunes unimportant relations for ontology learning. As a result, the ontologies learned by CRCTOL are more concise and contain a richer semantics in terms of the range and number of semantic relations compared with alternative systems. We present two case studies where CRCTOL is used to build a terrorism domain ontology and a sport event domain ontology. At the component level, quantitative evaluation by comparing with Text-To-Onto and its successor Text2Onto has shown that CRCTOL is able to extract concepts and semantic relations with a significantly higher level of accuracy. At the ontology level, the quality of the learned ontologies is evaluated by either employing a set of quantitative and qualitative methods including analyzing the graph structural property, comparison to WordNet, and expert rating, or directly comparing with a human-edited benchmark ontology, demonstrating the high quality of the ontologies learned. © 2010 Wiley Periodicals, Inc. ER - TY - THES AU - Sánchez, David T1 - Domain ontology learning from the web an unsupervised, automatic and domain independent approach PY - 2007/ PB - SP - EP - UR - http://www.worldcat.org/search?qt=worldcat_org_all&q=3836470691 M3 - KW - learning KW - ol KW - ontology L1 - N1 - N1 - AB - ER - TY - CONF AU - Buitelaar, Paul AU - Cimiano, Philipp AU - Haase, Peter AU - Sintek, Michael A2 - T1 - Towards Linguistically Grounded Ontologies T2 - 6th Annual European Semantic Web Conference (ESWC2009) PB - CY - PY - 2009/06 M2 - VL - IS - SP - 111 EP - 125 UR - http://www.cimiano.de/Publications/2009/eswc09/eswc09.pdf M3 - KW - nlp2rdf KW - ol KW - ontology L1 - SN - N1 - N1 - AB - In this paper we argue why it is necessary to associate linguistic information with ontologies and why more expressive models, beyond RDFS, OWL and SKOS, are needed to capture the relation between natural language constructs on the one hand and ontological entities on the other. We argue that in the light of tasks such as ontology-based information extraction, ontology learning and population from text and natural language generation from ontologies, currently available datamodels are not sufficient as they only allow to associate atomic terms without linguistic grounding or structure to ontology elements. Towards realizing a more expressive model for associating linguistic information to ontology elements, we base our work presented here on previously developed models (LingInfo, LexOnto, LMF) and present a new joint model for linguistic grounding of ontologies called LexInfo. LexInfo combines essential design aspects of LingInfo and LexOnto and builds on a sound model for representing computational lexica called LMF which has been recently approved as a standard under ISO. ER - TY - JOUR AU - Brewster, C AU - Jupp, S AU - Luciano, J AU - Shotton, D AU - Stevens, R D AU - Zhang, Z T1 - Issues in learning an ontology from text JO - BMC Bioinformatics PY - 2009/ VL - 10 Suppl 5 IS - SP - EP - UR - http://www.ncbi.nlm.nih.gov/pubmed/19426458 M3 - 10.1186/1471-2105-10-S5-S1 KW - learning KW - ol KW - ontology L1 - SN - N1 - Issues in learning an ontology from text. [BMC Bioinformatics. 2009] - PubMed result N1 - AB - BACKGROUND: Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus. RESULTS: Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/iffer/animalbehaviour/. CONCLUSION: We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning. ER - TY - THES AU - Brewster, Christopher T1 - Mind the Gap: Bridging from Text to Ontological Knowledge PY - 2008/ PB - Department of Computer Science, University of Sheffield SP - EP - UR - M3 - KW - knowledge KW - ol KW - ontology L1 - N1 - N1 - AB - ER - TY - JOUR AU - Cimiano, Philipp AU - V"olker, Johanna AU - Studer, Rudi T1 - Ontologies on Demand? -A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text Information JO - Information, Wissenschaft und Praxis PY - 2006/ VL - 57 IS - 6-7 SP - 315 EP - 320 UR - http://www.aifb.uni-karlsruhe.de/Publikationen/showPublikation?publ_id=1282 M3 - KW - learning KW - ol KW - ontology KW - survey L1 - SN - N1 - Ontologies on Demand? - A Description of the State-of-the-Art, Applications, Challenges and Trends for Ontology Learning from Text N1 - AB - ER - TY - CONF AU - Silva, L. De AU - Jayaratne, L. A2 - T1 - Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus T2 - Applications of Digital Information and Web Technologies, 2009. ICADIWT '09. Second International Conference on the PB - CY - PY - 2009/aug. M2 - VL - IS - SP - 446 EP - 451 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=5273826&arnumber=5273871&count=156&index=116 M3 - 10.1109/ICADIWT.2009.5273871 KW - learning KW - nlp KW - ol KW - ontology KW - wikipedia L1 - SN - N1 - Welcome to IEEE Xplore 2.0: Semi-automatic extraction and modeling of ontologies using Wikipedia XML Corpus N1 - AB - This paper introduces WikiOnto: a system that assists in the extraction and modeling of topic ontologies in a semi-automatic manner using a preprocessed document corpus derived from Wikipedia. Based on the Wikipedia XML Corpus, we present a three-tiered framework for extracting topic ontologies in quick time and a modeling environment to refine these ontologies. Using natural language processing (NLP) and other machine learning (ML) techniques along with a very rich document corpus, this system proposes a solution to a task that is generally considered extremely cumbersome. The initial results of the prototype suggest strong potential of the system to become highly successful in ontology extraction and modeling and also inspire further research on extracting ontologies from other semi-structured document corpora as well. ER - TY - CHAP AU - Völker, J. AU - Haase, P. AU - Hitzler, P. A2 - T1 - Learning Expressive Ontologies T2 - Ontology Learning and Population: Bridging the Gap between Text and Knowledge PB - IOS Press CY - PY - 2008/ VL - IS - SP - EP - UR - M3 - KW - axiom KW - learning KW - ol KW - ontology L1 - SN - N1 - My thesis references N1 - AB - ER - TY - CONF AU - Brank, Janez AU - Grobelnik, Marko AU - Mladenić, Dunja A2 - T1 - A Survey of Ontology Evaluation Techniques T2 - Proc. of 8th Int. multi-conf. Information Society PB - CY - PY - 2005/ M2 - VL - IS - SP - 166 EP - 169 UR - M3 - KW - evaluation KW - ol KW - ontology KW - survey L1 - SN - N1 - A nice survey of ontology evaluation methods, easy to read. N1 - AB - ER - TY - CONF AU - Plangprasopchok, A. AU - Lerman, K. A2 - T1 - Constructing folksonomies from user-specified relations on flickr T2 - WWW '09: Proceedings of the 18th international conference on World wide web PB - ACM CY - New York, NY, USA PY - 2009/ M2 - VL - IS - SP - 781 EP - 790 UR - http://www2009.org/proceedings/pdf/p781.pdf M3 - http://doi.acm.org/10.1145/1526709.1526814 KW - folksonomy KW - learning KW - ol KW - relation KW - tagging KW - taggingsurvey L1 - SN - 978-1-60558-487-4 N1 - N1 - AB - Automatic folksonomy construction from tags has attracted much attention recently. However, inferring hierarchical relations between concepts from tags has a drawback in that it is difficult to distinguish between more popular and more general concepts. Instead of tags we propose to use user-specified relations for learning folksonomy. We explore two statistical frameworks for aggregating many shallow individual hierarchies, expressed through the collection/set relations on the social photosharing site Flickr, into a common deeper folksonomy that reflects how a community organizes knowledge. Our approach addresses a number of challenges that arise while aggregating information from diverse users, namely noisy vocabulary, and variations in the granularity level of the concepts expressed. Our second contribution is a method for automatically evaluating learned folksonomy by comparing it to a reference taxonomy, e.g., the Web directory created by the Open Directory Project. Our empirical results suggest that user-specified relations are a good source of evidence for learning folksonomies. ER - TY - CONF AU - Zhou, Mianwei AU - Bao, Shenghua AU - Wu, Xian AU - Yu, Yong A2 - Aberer, Karl A2 - Choi, Key-Sun A2 - Noy, Natasha A2 - Allemang, Dean A2 - Lee, Kyung-Il A2 - Nixon, Lyndon J B A2 - Golbeck, Jennifer A2 - Mika, Peter A2 - Maynard, Diana A2 - Schreiber, Guus A2 - Cudré-Mauroux, Philippe T1 - An Unsupervised Model for Exploring Hierarchical Semantics from Social Annotations T2 - Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea PB - Springer Verlag CY - Berlin, Heidelberg PY - 2007/november M2 - VL - 4825 IS - SP - 673 EP - 686 UR - http://iswc2007.semanticweb.org/papers/673.pdf M3 - KW - folksonomy KW - ol KW - semantic KW - tagging KW - taggingsurvey L1 - SN - N1 - N1 - AB - This paper deals with the problem of exploring hierarchical semantics from social annotations. Recently, social annotation services have become more and more popular in Semantic Web. It allows users to arbitrarily annotate web resources, thus, largely lowers the barrier to cooperation. Furthermore, through providing abundant meta-data resources, social annotation might become a key to the development of Semantic Web. However, on the other hand, social annotation has its own apparent limitations, for instance, 1) ambiguity and synonym phenomena and 2) lack of hierarchical information. In this paper, we propose an unsupervised model to automatically derive hierarchical semantics from social annotations. Using a social bookmark service Del.icio.us as example, we demonstrate that the derived hierarchical semantics has the ability to compensate those shortcomings. We further apply our model on another data set from Flickr to testify our model's applicability on different environments. The experimental results demonstrate our model's effciency. ER - TY - CONF AU - Damme, Céline Van AU - Hepp, Martin AU - Siorpaes, Katharina A2 - T1 - FolksOntology: An Integrated Approach for Turning Folksonomies into Ontologies T2 - Bridging the Gap between Semantic Web and Web 2.0 (SemNet 2007) PB - CY - Innsbruck PY - 2007/ M2 - VL - IS - SP - 57 EP - 70 UR - http://www.kde.cs.uni-kassel.de/ws/eswc2007/proc/ProceedingsSemnet07.pdf M3 - KW - approach KW - folksonomies KW - integrated KW - ol KW - ontologies KW - tagging KW - taggingsurvey KW - toread KW - turning L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Benz, Dominik AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Semantics made by you and me: Self-emerging ontologies can capture the diversity of shared knowledge T2 - Proceedings of the 2nd Web Science Conference (WebSci10) PB - CY - Raleigh, NC, USA PY - 2010/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2010 KW - myown KW - ol KW - ontology KW - semantics KW - websci KW - websci10 L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Eda, Takeharu AU - Yoshikawa, Masatoshi AU - Uchiyama, Toshio AU - Uchiyama, Tadasu T1 - The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags. JO - World Wide Web PY - 2009/ VL - 12 IS - 4 SP - 421 EP - 440 UR - http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09 M3 - KW - analysis KW - folksonomy KW - ol KW - semantic KW - taxonomy KW - toread L1 - SN - N1 - dblp N1 - AB - ER - TY - CONF AU - Hjelm, Hans AU - Buitelaar, Paul A2 - Ghallab, Malik A2 - Spyropoulos, Constantine D. A2 - Fakotakis, Nikos A2 - Avouris, Nikolaos M. T1 - Multilingual Evidence Improves Clustering-based Taxonomy Extraction. T2 - ECAI PB - IOS Press CY - PY - 2008/ M2 - VL - 178 IS - SP - 288 EP - 292 UR - http://www.ling.su.se/staff/hans/artiklar/ecai2008-hjelm-buitelaar.pdf M3 - KW - antrag KW - learning KW - multilingual KW - ol KW - ontology L1 - SN - 978-1-58603-891-5 N1 - dblp N1 - AB - ER - TY - CONF AU - Sorg, Philipp AU - Cimiano, Philipp A2 - T1 - Cross-lingual Information Retrieval with Explicit Semantic Analysis T2 - Working Notes for the CLEF 2008 Workshop PB - CY - PY - 2008/ M2 - VL - IS - SP - EP - UR - http://www.aifb.kit.edu/images/7/7c/2008_1837_Sorg_Cross-lingual_I_1.pdf M3 - KW - cross KW - information KW - lingual KW - ol KW - ontology L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Hazman, Maryam AU - El-Beltagy, Samhaa R. AU - Rafea, Ahmed T1 - Ontology learning from domain specific web documents JO - International Journal of Metadata, Semantics and Ontologies PY - 2009/ VL - 4 IS - SP - 24 EP - 33(10) UR - http://www.ingentaconnect.com/content/ind/ijmso/2009/00000004/F0020001/art00003 M3 - doi:10.1504/IJMSO.2009.026251 KW - learning KW - ol KW - ontology L1 - SN - N1 - IngentaConnect Ontology learning from domain specific web documents N1 - AB - Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology. ER -