This paper gives an overview on the use of Protégé in the Artequakt system, which integrated Protégé with a set of natural language tools to automatically extract knowledge about artists from web documents and instantiate a given ontology. Protégé was also linked to structured templates that generate documents from the knowledge fragments it maintains

Alani, Harith

Hall, Wendy

Kim, Sanghee

Lewis, Paul H.

Millard, David E.

Shadbolt, Nigel

Weal, Mark J.

English

Southampton (e-Prints Soton)

Using Protégé for Automatic Ontology Instantiation Harith Alani, Sanghee Kim, David E. Millard, Mark J. WealWendy Hall, Paul H. Lewis, Nigel ShadboltIntelligence, Agents, Multimedia Group  ECS School, University of Southampton, UK {ha, sk, dem, mjw, wh, phl, nrs}@ecs.soton.ac.ukABSTRACT This paper gives an overview on the use of Protégé in the Artequakt system, which integrated Protégé with a set of natural language tools to automatically extract knowledge about artists from web documents and instantiate a given ontology. Protégé was also linked to structured templates that generate documents from the knowledge fragments it maintains.  INTRODUCTION The value of most semantic web services will be dependent to a large extent on the richness and consistency of their underlying ontology instantiations. Ontology instantiation refers to the insertion of information into the Knowledge Base (KB), as described by the ontology (i.e. instance crea-tion). One rich source of knowledge to instantiate ontolo-gies is the web. However, automatically locating and re-trieving knowledge from the web can be a daunting task, especially with the current scarcity of semantic annotations. A number of approaches have been taken to speed up this process using a variety of techniques, such as Information Extraction (IE) from text, harvesting information off struc-tured documents, gathering knowledge from existing anno-tations, and accessing online databases and gazetteers. The Artequakt [1] system is concerned with automating the extraction of knowledge about the life and work of artists from web documents, instantiating a given ontology with this knowledge, and using it to generate tailored biogra-phies. Protégé [8] was chosen as the ontology and knowl-edge base (KB) component, which is responsible for the maintenance and supply of the gathered knowledge.   ARTEQUAKT Artequakt’s architecture (Fig. 1) comprises of three key areas. The first concerns the Knowledge Extraction tools used to extract factual information from documents and passing it to the knowledge server. The second key area is the Protégé Server, which is concerned with informa-tion management, storage, and supply. The final area is the Narrative Generation, which constructs the biogra-phies. These areas will be briefly described in the fol-lowing sections.  PROTÉGÉ SERVER The Artequakt ontology was mainly constructed from selected sections in the CIDOC Conceptual Reference Model (CRM1). The CRM ontology is designed to repre-sent artefacts, their production, ownership, location, etc. This ontology was modified for Artequakt and enriched with additional classes and relationships to represent personal information about artists, their family relations, relations with other artists, details of their work, etc. The Artequakt ontology was implemented in Protégé, and currently contains 42 classes and 228 slots.  A Java HTTP server was developed to allow external access to the Protégé KB. Around 15 different types of queries were encoded on this server to enable other tools to retrieve and update the knowledge base. Some of these queries are generic (e.g. instance slot value query), while others are more specific to our application (e.g. get a date description, an artist’s personal detail).                      Figure 1. Artequakt’s system architecture                                                                   1 http://cidoc.ics.forth.gr/index.html instantiate ontology extracted knowl-filtered resources RDF triplesretrieve knowledge domain repre-sentation generate biographies Knowledge Extraction Web GATEIE Tool Narrative GenerationProtégé ServerRDF   named entitiesWordNetpaintings info <kb:Person            kb:name=”Renoir"            rdfs:label="P_1"> </kb:Person> Armadilloquery expansiongeographic names KNOWLEDGE EXTRACTION Artequakt’s knowledge extraction tool (fully described in [2]) aims to identify and extract knowledge triples from text documents and to provide it as RDF triples for entry into the KB. Artequakt uses an ontology coupled with a general-purpose lexicon (WordNet), an entity-recogniser (GATE, [5]), and a wrapper (Armadillo, [4]) as supporting tools for identifying knowledge fragments.   The extraction process is launched when a user requests a biography for a specific artist that is not in the Protégé KB. The query is passed on to a search engine and the search results are analysed with respect to relevancy to the domain of artists. Selected documents are then analysed syntacti-cally and semantically to identify any relevant knowledge to extract. Below is an example of an extracted sentence: "Renoir was born in Limoges on February 25, 1841. " Documents are then passed on to GATE [5] to recognise the type of entities  in their text. Annotations provided by GATE for the above sentence highlight that ‘Renoir’ is a person’s name, ‘February 25, 1841’ is a date, and ‘Limoges’ is a place. Artequakt will then access the Pro-tégé knowledge server to map these entities to the ontol-ogy classes. If a match does not exist, WordNet will be used to expand the given terminology. At the end of this stage, three instances will be created for the Person, Place, and Date classes to represent the three extracted entities above.  After extracting the main entities and inserting them into the KB, the task now is to analyse the sentence further to identify the relations between those entities. Further in-teraction between the IE tool and Protégé will now be required to name those relations. In the example sen-tence above, the verb ‘born’ was expanded in WordNet to ‘birth’, which matches with two potential relations in the ontology; date_of_birth and place_of_birth.  By comparing the type of subjects and objects of these relations as expressed in the ontology, with those identi-fied earlier in the sentence, the following knowledge triples will be identified:    Figure 2. Knowledge triples extracted in Artequakt Artequakt’s IE component is also linked to an Armadillo [4] server (Fig. 1), which was specialised for the re-trieval of information about paintings (title, date, dimen-sions, etc). Armadillo is a wrapper induction tool devel-oped at Sheffield University to discover new linguistic and structural patterns automatically when extracting information from well-structured pages.  Artequakt’s IE process terminates by sending all ex-tracted knowledge to the Protégé server in RDF format.  ONTOLOGY INSTANTIATION When the Protégé server receives a new RDF file, it ac-tivates a feeder process to parse the given file and add its content to the KB. This will be followed by a consolida-tion process to find and resolve duplications. Knowledge Base Consolidation When acquiring knowledge from multiple sources, it becomes necessary to be able to compare this knowledge and merge any possible duplication to ensure the integ-rity and consistency of the KB. Consolidation in Artequakt is concerned with analysing and comparing relational values of instances to identify inconsistencies and duplications (detailed in [3]). This comparison is not always straightforward because rela-tional values are often extracted in different formats and specificity levels (e.g. synonymous place names, differ-ent date formats). Artequakt applies a set of heuristics and expansion methods in an attempt to match these val-ues. Consider the following sentences:  1.  Renoir was born in the 19th century in Limoges. 2.  Renoir was born in 1841 in Limoges, France. 3.  Renoir was born on Feb 25 1841 in France. Matching the above requires some temporal and geo-graphical reasoning. When a new place name is inserted into the KB, the Protégé server will access WordNet (using the JWordNet2 API) and expand the given place name to its synonyms, subparts, and superparts. This allows the system to relate ‘Limoges’ to ‘France’. Simple temporal reasoning was used to consolidate dates with respect to precision. In our previous example, the third date is used, as it is the most specific.  At the end of the consolidation process, the knowledge extracted from the example sentences above will be merged into the triples given in figure 2.                                                                  Figure 3 shows some of the above knowledge, as well as three of Renoir’s paintings as found by Armadillo [4].  BIOGRAPHY GENERATION Once information is extracted, stored and consolidated, the Artequakt system repurposes it by automatically gen-erating biographies of the artists (more detail in [7]). The biographies are based on templates authored in the Fun-damental Open Hypermedia Model (FOHM) and stored in the Auld Linky contextual structure server [6].  date of birth  Renoir  25/2/1841place of birth  Limoges Renoir Each section of the template is instantiated with para-graphs or sentences generated from information held in Protégé. The KB informs the templates of the theme of the paragraphs (e.g. influences, family, style) and the generation tool selects the relevant ones and structures them in the desired form and order. Figure 4 is an exam-ple output of Artequakt. Very little text generation is used is currently (e.g. 1st and last sentences in fig. 4), but this will be the focus of the next phase.   2 http://jwn.sourceforge.net/                       Figure 3. Artequakt ontology with some instantiations.                                Figure 4. Biography for Renoir generated by Artequakt CONCLUSIONS This paper described a system that automatically extracts knowledge from the web to instantiate an ontology held in Protégé, then reassembles the knowledge in the form of biographies. This project explored the potential of integrat-ing Protégé with IE tools, narrative generation templates, a lexicon to extend its terminology, and with a variety of programs to manipulate its knowledge. Using an ontology in this context is aimed at increasing the system’s portability to other domains. Building a cross-domain system is one of the aims of this project, and will be fully investigated in the next stage of development. ACKNOWLEDGEMENTS  This research is funded in part by EU Framework 5 IST project “Sculpteur” IST-2001-35372, EPSRC IRC pro-ject “Equator” GR/N15986/01 and EPSRC IRC project “AKT” GR/N15764/01. REFERENCES [1]  Alani, H., S. Kim, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis and N. Shadbolt. Automatic Ontology-based Knowledge Extraction and Tailored Biography Generation from the Web. IEEE Intelligent Systems, 18(1): 14-21, 2003. [2]  Alani, H., S. Kim, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis and N. Shadbolt. Automatic Extraction of Knowledge from Web Documents. Workshop on Hu-man Language Technology for the Semantic Web and Web Services, 2nd Int. Semantic Web Conf. Sanibel Is-land, Florida, USA, 2003. [3]  Alani, H., S. Kim, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis and N. Shadbolt. Web based Knowledge Extraction and Consolidation for Automatic Ontology Instantiation. 2nd Int. Conf. Knowledge Capture (K-Cap'03), Workshop on Knowledge Markup and Se-mantic Annotation, Sanibel Island, FL, USA, 2003. [4]  Ciravegna, F., S. Chapman, A. Dingli, and Y. Wilks,  Learning to Harvest Information for the Semantic Web. Proc. 1st European Semantic Web Symposium, Heraklion, Greece, May 2004 [5]  Cunningham, H., D. Maynard, K. Bontcheva, V. Tab-lan. GATE: a framework and graphical development environment for robust NLP tools and applications. Proc. 40th Anniversary Meeting of the Association for Computational Linguistics, Phil, USA, 2002. [6] Michaelides, D.T., D.E. Millard, M.J. Weal, D. De Roure. Auld Leaky: A Contextual Open Hypermedia Link Server. Proc. 7th Hypermedia: Openness, Struc-tural Awareness, and Adaptivity, Heidelberg, 2001. [7] Millard, D.E., H. Alani, S. Kim, M.J. Weal, P.H. Lewis, W. Hall, and N. Shadbolt. Generating Adaptive Hypertext Content from the Semantic Web. 1st Int. Workshop on Hypermedia and the Semantic Web, Hy-perText'03, Nottingham, UK. 2003. [8]  Musen, M. A., R. W. Fergerson, W. E. Grosso, N. F. Noy, M. Y. Grubezy and J. H. Gennari. Component-based support for building knowledge-acquisition sys-tems. Intelligent Info. Processing Conf. (IIP), pp 18-22, Beijing, China, 2000. 

Auld Leaky: A Contextual Open Hypermedia Link Server.

Automatic Extraction of Knowledge from Web Documents. Workshop on Human Language Technology for the Semantic Web and Web Services, 2 nd Int. Semantic Web Conf.

Automatic Ontologybased Knowledge Extraction and Tailored Biography Generation from the Web.

Componentbased support for building knowledge-acquisition systems.

GATE: a framework and graphical development environment for robust NLP tools and applications.

Learning to Harvest Information for the Semantic Web.

Web based Knowledge Extraction and Consolidation for Automatic Ontology Instantiation.

Using Protege for Automatic Ontology Instantiation

Open Research Online (The Open University)

Open Research OnlineThe Open University’s repository of research publicationsand other research outputsUsing Protege for automatic ontology instantiationConference or Workshop ItemHow to cite:Alani, Harith; Kim, Sanghee; Millard, David E.; Weal, Mark J.; Hall, Wendy; Lewis, Paul H. and Shadbolt,Nigel (2004). Using Protege for automatic ontology instantiation. In: 7th International Prote´ge´ Conference, 6-9 Jul2004, Bethesda, Maryland, USA.For guidance on citations see FAQs.c© 2004 The AuthorsVersion: Accepted ManuscriptLink(s) to article on publisher’s website:http://protege.stanford.edu/conference/2004/Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukUsing Protégé for Automatic Ontology Instantiation Harith Alani, Sanghee Kim, David E. Millard, Mark J. WealWendy Hall, Paul H. Lewis, Nigel ShadboltIntelligence, Agents, Multimedia Group  ECS School, University of Southampton, UK {ha, sk, dem, mjw, wh, phl, nrs}@ecs.soton.ac.ukABSTRACT This paper gives an overview on the use of Protégé in the Artequakt system, which integrated Protégé with a set of natural language tools to automatically extract knowledge about artists from web documents and instantiate a given ontology. Protégé was also linked to structured templates that generate documents from the knowledge fragments it maintains.  INTRODUCTION The value of most semantic web services will be dependent to a large extent on the richness and consistency of their underlying ontology instantiations. Ontology instantiation refers to the insertion of information into the Knowledge Base (KB), as described by the ontology (i.e. instance crea-tion). One rich source of knowledge to instantiate ontolo-gies is the web. However, automatically locating and re-trieving knowledge from the web can be a daunting task, especially with the current scarcity of semantic annotations. A number of approaches have been taken to speed up this process using a variety of techniques, such as Information Extraction (IE) from text, harvesting information off struc-tured documents, gathering knowledge from existing anno-tations, and accessing online databases and gazetteers. The Artequakt [1] system is concerned with automating the extraction of knowledge about the life and work of artists from web documents, instantiating a given ontology with this knowledge, and using it to generate tailored biogra-phies. Protégé [8] was chosen as the ontology and knowl-edge base (KB) component, which is responsible for the maintenance and supply of the gathered knowledge.   ARTEQUAKT Artequakt’s architecture (Fig. 1) comprises of three key areas. The first concerns the Knowledge Extraction tools used to extract factual information from documents and passing it to the knowledge server. The second key area is the Protégé Server, which is concerned with informa-tion management, storage, and supply. The final area is the Narrative Generation, which constructs the biogra-phies. These areas will be briefly described in the fol-lowing sections.  PROTÉGÉ SERVER The Artequakt ontology was mainly constructed from selected sections in the CIDOC Conceptual Reference Model (CRM1). The CRM ontology is designed to repre-sent artefacts, their production, ownership, location, etc. This ontology was modified for Artequakt and enriched with additional classes and relationships to represent personal information about artists, their family relations, relations with other artists, details of their work, etc. The Artequakt ontology was implemented in Protégé, and currently contains 42 classes and 228 slots.  A Java HTTP server was developed to allow external access to the Protégé KB. Around 15 different types of queries were encoded on this server to enable other tools to retrieve and update the knowledge base. Some of these queries are generic (e.g. instance slot value query), while others are more specific to our application (e.g. get a date description, an artist’s personal detail).                      Figure 1. Artequakt’s system architecture                                                                  1 http://cidoc.ics.forth.gr/index.html instantiate ontology extracted knowl-filtered resources RDF triplesretrieve knowledge domain repre-sentation generate biographies Knowledge Extraction Web GATEIE Tool Narrative GenerationProtégé ServerRDF  named entitiesWordNetpaintings info <kb:Person            kb:name=”Renoir"            rdfs:label="P_1"> </kb:Person> Armadilloquery expansiongeographic names KNOWLEDGE EXTRACTION Artequakt’s knowledge extraction tool (fully described in [2]) aims to identify and extract knowledge triples from text documents and to provide it as RDF triples for entry into the KB. Artequakt uses an ontology coupled with a general-purpose lexicon (WordNet), an entity-recogniser (GATE, [5]), and a wrapper (Armadillo, [4]) as supporting tools for identifying knowledge fragments.   The extraction process is launched when a user requests a biography for a specific artist that is not in the Protégé KB. The query is passed on to a search engine and the search results are analysed with respect to relevancy to the domain of artists. Selected documents are then analysed syntacti-cally and semantically to identify any relevant knowledge to extract. Below is an example of an extracted sentence: "Renoir was born in Limoges on February 25, 1841. " Documents are then passed on to GATE [5] to recognise the type of entities  in their text. Annotations provided by GATE for the above sentence highlight that ‘Renoir’ is a person’s name, ‘February 25, 1841’ is a date, and ‘Limoges’ is a place. Artequakt will then access the Pro-tégé knowledge server to map these entities to the ontol-ogy classes. If a match does not exist, WordNet will be used to expand the given terminology. At the end of this stage, three instances will be created for the Person, Place, and Date classes to represent the three extracted entities above.  After extracting the main entities and inserting them into the KB, the task now is to analyse the sentence further to identify the relations between those entities. Further in-teraction between the IE tool and Protégé will now be required to name those relations. In the example sen-tence above, the verb ‘born’ was expanded in WordNet to ‘birth’, which matches with two potential relations in the ontology; date_of_birth and place_of_birth.  By comparing the type of subjects and objects of these relations as expressed in the ontology, with those identi-fied earlier in the sentence, the following knowledge triples will be identified:    Figure 2. Knowledge triples extracted in Artequakt Artequakt’s IE component is also linked to an Armadillo [4] server (Fig. 1), which was specialised for the re-trieval of information about paintings (title, date, dimen-sions, etc). Armadillo is a wrapper induction tool devel-oped at Sheffield University to discover new linguistic and structural patterns automatically when extracting information from well-structured pages.  Artequakt’s IE process terminates by sending all ex-tracted knowledge to the Protégé server in RDF format.  ONTOLOGY INSTANTIATION When the Protégé server receives a new RDF file, it ac-tivates a feeder process to parse the given file and add its content to the KB. This will be followed by a consolida-tion process to find and resolve duplications. Knowledge Base Consolidation When acquiring knowledge from multiple sources, it becomes necessary to be able to compare this knowledge and merge any possible duplication to ensure the integ-rity and consistency of the KB. Consolidation in Artequakt is concerned with analysing and comparing relational values of instances to identify inconsistencies and duplications (detailed in [3]). This comparison is not always straightforward because rela-tional values are often extracted in different formats and specificity levels (e.g. synonymous place names, differ-ent date formats). Artequakt applies a set of heuristics and expansion methods in an attempt to match these val-ues. Consider the following sentences:  1. Renoir was born in the 19th century in Limoges. 2. Renoir was born in 1841 in Limoges, France. 3. Renoir was born on Feb 25 1841 in France. Matching the above requires some temporal and geo-graphical reasoning. When a new place name is inserted into the KB, the Protégé server will access WordNet (using the JWordNet2 API) and expand the given place name to its synonyms, subparts, and superparts. This allows the system to relate ‘Limoges’ to ‘France’. Simple temporal reasoning was used to consolidate dates with respect to precision. In our previous example, the third date is used, as it is the most specific.  At the end of the consolidation process, the knowledge extracted from the example sentences above will be merged into the triples given in figure 2.                                                                  Figure 3 shows some of the above knowledge, as well as three of Renoir’s paintings as found by Armadillo [4].  BIOGRAPHY GENERATION Once information is extracted, stored and consolidated, the Artequakt system repurposes it by automatically gen-erating biographies of the artists (more detail in [7]). The biographies are based on templates authored in the Fun-damental Open Hypermedia Model (FOHM) and stored in the Auld Linky contextual structure server [6].  date of birth Renoir 25/2/1841place of birth LimogesRenoir Each section of the template is instantiated with para-graphs or sentences generated from information held in Protégé. The KB informs the templates of the theme of the paragraphs (e.g. influences, family, style) and the generation tool selects the relevant ones and structures them in the desired form and order. Figure 4 is an exam-ple output of Artequakt. Very little text generation is used is currently (e.g. 1st and last sentences in fig. 4), but this will be the focus of the next phase.  2 http://jwn.sourceforge.net/                       Figure 3. Artequakt ontology with some instantiations.                                Figure 4. Biography for Renoir generated by Artequakt CONCLUSIONS This paper described a system that automatically extracts knowledge from the web to instantiate an ontology held in Protégé, then reassembles the knowledge in the form of biographies. This project explored the potential of integrat-ing Protégé with IE tools, narrative generation templates, a lexicon to extend its terminology, and with a variety of programs to manipulate its knowledge. Using an ontology in this context is aimed at increasing the system’s portability to other domains. Building a cross-domain system is one of the aims of this project, and will be fully investigated in the next stage of development. ACKNOWLEDGEMENTS  This research is funded in part by EU Framework 5 IST project “Sculpteur” IST-2001-35372, EPSRC IRC pro-ject “Equator” GR/N15986/01 and EPSRC IRC project “AKT” GR/N15764/01. REFERENCES [1] Alani, H., S. Kim, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis and N. Shadbolt. Automatic Ontology-based Knowledge Extraction and Tailored Biography Generation from the Web. IEEE Intelligent Systems, 18(1): 14-21, 2003. [2] Alani, H., S. Kim, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis and N. Shadbolt. Automatic Extraction of Knowledge from Web Documents. Workshop on Hu-man Language Technology for the Semantic Web and Web Services, 2nd Int. Semantic Web Conf. Sanibel Is-land, Florida, USA, 2003. [3] Alani, H., S. Kim, D. E. Millard, M. J. Weal, W. Hall, P. H. Lewis and N. Shadbolt. Web based Knowledge Extraction and Consolidation for Automatic Ontology Instantiation. 2nd Int. Conf. Knowledge Capture (K-Cap'03), Workshop on Knowledge Markup and Se-mantic Annotation, Sanibel Island, FL, USA, 2003. [4] Ciravegna, F., S. Chapman, A. Dingli, and Y. Wilks,  Learning to Harvest Information for the Semantic Web. Proc. 1st European Semantic Web Symposium, Heraklion, Greece, May 2004 [5] Cunningham, H., D. Maynard, K. Bontcheva, V. Tab-lan. GATE: a framework and graphical development environment for robust NLP tools and applications. Proc. 40th Anniversary Meeting of the Association for Computational Linguistics, Phil, USA, 2002. [6] Michaelides, D.T., D.E. Millard, M.J. Weal, D. De Roure. Auld Leaky: A Contextual Open Hypermedia Link Server. Proc. 7th Hypermedia: Openness, Struc-tural Awareness, and Adaptivity, Heidelberg, 2001. [7] Millard, D.E., H. Alani, S. Kim, M.J. Weal, P.H. Lewis, W. Hall, and N. Shadbolt. Generating Adaptive Hypertext Content from the Semantic Web. 1st Int. Workshop on Hypermedia and the Semantic Web, Hy-perText'03, Nottingham, UK. 2003. [8] Musen, M. A., R. W. Fergerson, W. E. Grosso, N. F. Noy, M. Y. Grubezy and J. H. Gennari. Component-based support for building knowledge-acquisition sys-tems. Intelligent Info. Processing Conf. (IIP), pp 18-22, Beijing, China, 2000. 

Using Protege for automatic ontology instantiation

Using Protege for automatic ontology instantiation

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