Ontology Tools Survey, Revisited
A new survey of ontology editors was conducted as a follow-up to an initial survey conducted in 2002. The results of the survey are summarized in this article. The results of the original survey may be found at www.xml.com/pub/a/2002/11/06/ontologies.html.
Ontologies are a way of specifying the structure of domain knowledge in a formal logic designed for machine processing. The effect on information technology (IT) is to shift the burden of capturing the meaning of data content from the procedural operations of algorithms and rules to the representation of the data itself.
Opening the International Semantic Web Conference in 2003, the conference chair Jim Hendler declared that "a little semantics goes a long way." The belief being that infusing even a little semantic quality into our data (residing in web pages, database tables, electronic documents, or whatever) can mean that data is more immediately, broadly, and profoundly usable by all applications aware of the knowledge-representation scheme -- the ontology.
For such reasons, there is a growing sense among researchers and practitioners that ontologies will play an important role in forthcoming information-management solutions. Several conditions predicate this current state of affairs.
State of Ontologies
Practical ontology languages are being adopted. For example, the W3C recently recommended the OWL language and RDF for building web ontologies. These language specifications were developed over several years both within and outside of the organization, and OWL is rapidly replacing its predecessor DAML+OIL with the blessing of the DAML Office in the Department of Defense, which funded much of its early development. Commensurate W3C standardization activities are now underway to expand the development framework for building and using web ontologies with web services, deductive rules, and optimized query languages.
Numerous commercial and open-source software tools are available for building and deploying ontologies, and for integrating inference systems with web and database infrastructures. Increasingly, these tools directly support the emerging web ontology standards, as well as related, standard-language efforts like Simple Common Logic (SCL as an offshoot of KIF) and ISO EXPRESS.
Reference to taxonomies and ontologies by vendors of mainstream enterprise-application-integration (EAI) solutions are becoming commonplace. Popularly tagged as semantic integration, vendors like Verity, Modulant, Unicorn, Semagix, and many more are offering platforms to interchange information among mutually heterogeneous resources including legacy databases, semi-structured repositories, industry-standard directories and vocabularies like ebXML, and streams of unstructured content as text and media. Ontologies, for example, are being used to guide the extraction of semantic content from collections of plain-text documents describing medical research, consumer products, and business topics.
Government initiatives to strengthen information technology capabilities of federal agencies and services are integrating the use of ontologies with existing infrastructures to perform incisive and far-reaching assessments of information flowing from disparate sources. Anti-terrorism intelligence analysis and command-level, combat-decision support are typical examples.
Major web search services like Google and Yahoo are using ontology-based approaches to find and organize content on the Web. Google's acquisition of Applied Semantics, Inc. -- one of the leading vendors of semantic extraction tools -- portends an active role for ontologies in their technology solutions.
In April of this year, Gartner, the market research firm, identified taxonomies/ontologies as one of the leading IT technologies, ranking it third in its list of the top 10 technologies forecast for 2005.
Also, ontologies are being used by business and government to help define and implement enterprise-level architecture frameworks that can enable the coherent interplay of information systems within an enterprise environment. Approaches like the Federal Enterprise Architecture (FEA) and OMG's Model Driven Architecture, for example, may benefit from ontology-mediated specifications.
Building an Ontology
You don't author an ontology as much as you construct it. Ontology building is not a very linear process, and you may approach the task from several perspectives at once, both top-down and bottom-up. It is also a substantially iterative process. Skeleton structures of core concepts are extended with more refined and more peripheral concepts, and these are more tightly interwoven with additional elaborating relations. While parts of this may sound like conventional software development, there are fundamental differences.
Procedural and object-oriented software, regardless of whether it is being coded imperatively or declaratively, uses structural aspects of the software to control program flow and use. Ontology languages primarily use structure to specify semantics. For example, while subclass inheritance in object-oriented languages is a mechanism of convenience that enables code reuse, subclass inheritance in an ontology language enables semantic interpretation of the data through classification, entailment, and restriction.
An ontology building process may span problem specification, domain knowledge acquisition and analysis, conceptual design and commitment to community ontologies, iterative construction and testing, publishing the ontology as a terminology, and possibly populating a conforming knowledge base with ontology individuals. While the process may be strictly a manual exercise, there are tools available that can automate portions of it.
For example, linguistic tools can analyze the content of domain documents in order to synthesize ontology terms themselves, or to extract content corresponding to a domain ontology as individuals forming a knowledge base. Building complex ontologies today usually relies on the manual composition of the ontology using an ontology editor for the chosen ontology languages(s).
The intent of this article is to summarize the manual editing tools currently available to practitioners interested in building structured ontologies suitable for information management and other applications. These tools may also have capabilities for automatically extracting information from domain documents. The article follows an earlier article (see Resources) summarizing some 56 ontology editors. That article also provides a useful introduction to building ontologies. Results from a new survey of ontology software providers were used to replace the original tool descriptions and add descriptions of 40 additional ontology editors. The descriptions identify tool characteristics in 13 categories as distinguished in Table 1.
The survey covers tools with ontology editing capabilities that can be used to build ontology schemas (terminologies) and/or instance data. These ontology editors may be available as standalone, plugin or online software, and need not be production level software with complete functionality and user support.
The survey results are presented in Table 1 as categorical descriptions of 94 ontology editors currently available to the ontology building community. The results include contact addresses for obtaining additional software information.
Room for Improvement
As part of the survey, each respondent was asked to answer the following question about what enhancement they would like to see in future ontology editors:
"What advancement in existing tools do you believe is needed most to improve our ability to build useful ontologies?"
Fifty-six percent of the respondents provided answers to this survey question. The results are summarized in Table 2 where individual answers are categorized by sorting them into 11 different areas of tool enhancement. The percentages appearing in the table indicate the proportion of respondents whose answer was categorized as relating to the indicated feature area.
Table 2. Top Tool Features to Enhance Ontology Editing
|Abstraction for knowledge modeling||18%|
|Visual/intuitive navigation of ontology||13%|
|Reasoning and problem solving facilities||12%|
|Ontology alignment and data resource integration||12%|
|Support of standard industry domain and core vocabularies||9%|
|Natural language processing||7%|
|Ontology language standardization||6%|
|Built-ins (wizards) for best practice methods||6%|
|Information extraction facilities||4%|
|Features to learn user's editing style and needs||3%|
|Collaborative development support||1%|
|Ontology support for contexts||1%|
The other top answers include: the use of reasoning facilities to help explore, compose and check ontologies; and the inclusion of facilities to help align ontologies with one another and integrate them with other data resources like enterprise databases. The remaining answers addressed enhanced support for industry domain standardization, natural language processing, collaborative development, and other enhancements mentioned by less than ten percent of respondents.
Collectively, the sentiment expressed by respondents centers on tool features to make building full-blown ontologies easier and more foolproof, especially for domain experts rather than ontologists. This sentiment echoes back a few decades to when practitioners were trying to use expert system shells productively. On the other hand, new tool features to help align domain and core ontologies including standard vocabularies are emerging as a more contemporary focus, more in concert with enterprise application integration and development trends.
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