Ontology tools survey

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 , the conference chair Jim Hendler declared that "a little semantics goes a long way. 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. Practical ontology languages are being adopted. 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. 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. 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. 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. 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. Table 1. Summary Table of Editing Tools is here. 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:.

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. 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.

One ontology building trend not articulated in the survey responses, but highlighted in a dedicated session of the recent WWW Conference, is support for ontology languages built on RDF and the use of URIs as identifiers for referring to unique entities. Current attention to the Semantic Web and the language standardization it offers has resulted in the single most prominent change in ontology editors since the original survey in The issue arises in consideration of whether RDF is the best base language for implementing ontologies on the Web or elsewhere, and whether it affords the scalability necessary to implement very large ontologies and webs of ontologies, and whether it affords the representational power or expressiveness to build ontologies of the sophistication necessary for demanding applications.

This unifying aspect, for instance, may make it easier to establish, through collaboration and consensus, the utilitarian vocabularies as ontologies needed for far-flung cooperative and integrative applications using the Web.

Wearing the mantle of W3C standardization, OWL enjoys much more attention today than any other ontology language -- in or out of the Web world. Its detractors tend to single out its limits of expression, its inelegant syntax and, of course, its reliance on the RDF model of representation using triples. Some basic language constructs like lists and other collections are deemed cumbersome and in need of extension in new language implementations.

These shortcomings, if one chooses to see them as such, clearly add more to the ontology toolmaker's plate. The successful ontology editor may be expected to mask these kind of idiosyncrasies with higher level functionalities.

Such a consistent focus has not yet emerged in a suite of tools for building ontologies. Indeed, when this does happen it may be as part of a general enterprise level IDE. Ontologies also differ in respect to the scope and purpose of their content. The most prominent distinction is between the domain ontologies describing specific fields of endeavor, like medicine, and upper level ontologies describing the basic concepts and relationships invoked when information about any domain is expressed in natural language.

The synergy among ontologies -- exploitable by a vertical application -- springs from the cross-referencing between upper level ontologies and various domain ontologies. All ontologies have a part that historically has been called the terminological component. This is roughly analogous to what we know as the schema for a relational database or XML document. It defines the terms and structure of the ontology's area of interest. The second part, the assertional component, populates the ontology further with instances or individuals that manifest that terminological definition.

This extension can be separated in implementation from the ontology and maintained as a knowledge base. The dividing line, however, between treating a thing as a concept and treating it as an individual is usually an ontology-specific decision.

Whether the Ford Mustang GT is an individual Ford automobile, or the vehicle with license plate number AXL is an individual Ford as an instance of the subclass Ford Mustang GT , may vary between two valid automotive ontologies. Ontologies are not all built the same way. A number of possible languages can be used, including general logic programming languages like Prolog. More common, however, are languages that have evolved specifically to support ontology construction. There are also several languages based on a form of logic thought to be especially computable known as description logics.

When comparing ontology languages, what is given up for computability and simplicity is usually language expressiveness, which isn't always a bad deal. A language need only be as rich and expressive as is necessary to represent the nuance and intricacy of knowledge that the ontology's purpose and its developers demand. The basic steps in building an ontology are straightforward. Various methodologies exist to guide the theoretical approach taken, and numerous ontology building tools are available.

The problem is that these procedures have not coalesced into popular development styles or protocols, and the tools have not yet matured to the degree one expects in other software practices. Further, full support for the latest ontology languages is lacking. Assemble appropriate information resources and expertise that will define, with consensus and consistency, the terms used formally to describe things in the domain of interest.

These definitions must be collected so that they can be expressed in a common language selected for the ontology. Design the overall conceptual structure of the domain. This will likely involve identifying the domain's principal concrete concepts and their properties, identifying the relationships among the concepts, creating abstract concepts as organizing features, referencing or including supporting ontologies, distinguishing which concepts have instances, and applying other guidelines of your chosen methodology.

Add concepts, relations, and individuals to the level of detail necessary to satisfy the purposes of the ontology. Reconcile syntactic, logical, and semantic inconsistencies among the ontology elements. Consistency checking may also involve automatic classification that defines new concepts based on individual properties and class relationships. Incumbent on any ontology development effort is a final verification of the ontology by domain experts and the subsequent commitment of the ontology by publishing it within its intended deployment environment.

Software tools are available to accomplish most aspects of ontology development. While ontology editors are useful during each step outlined above, other types of ontology building tools are also needed along the way. Development projects often involve solutions using numerous ontologies from external sources as well as existing and newly developed in-house ontologies.

Ontologies from any source may progress through a series of versions. In the end, careful management of this collection of heterogeneous ontologies becomes necessary to keep track of them. Tools also help to map and link between them, compare them, reconcile and validate them, merge them, and convert them into other forms.

Ontologies may be derived from or transformed into forms such as W3C XML Schemas, database schemas, and UML to achieve integration with associated enterprise applications. Still other tools can help acquire, organize, and visualize the domain knowledge before and during the building of a formal ontology. When starting out on an ontology project, the first and reasonable reaction is to find a suitable ontology software editor.

It's hoped this broad summary of available editors will give prospective ontology developers a head start. This survey covers software tools that have ontology editing capabilities and are in use today. The tools may be useful for building ontology schemas terminological component alone or together with instance data.

Ontology browsers without an editing focus and other types of ontology building tools are not included. Otherwise, the objective was to identify as broad a cross-section of editing software as possible. The editing tools are not necessarily production level development tools, and some may offer only limited functionality and user support. Concise descriptions of each software tool were compiled and then reviewed by the organization currently providing the software for commercial, open, or restricted distribution.

The descriptions are factored into a dozen different categories covering important functions and features of the software. These categories appear in Table 1 summarizing the results. When possibly subtle distinctions in meaning or approach arose in these descriptions, we elected to retain the words of the tool provider. Despite the immaturity of the field, or perhaps because of it, we were able to identify a surprising number of ontology editors -- more than 50 overall.

Commercial products include standalone editors designed exclusively for building ontologies in any domain, and editors that are part of commercial software suites designed to deliver broad enterprise integration solutions. Other editing software is the outcome of academic and government funded projects investigating the technical application of ontologies.

Some editors are intended for building ontologies in a specific domain but still capable of general-purpose ontology building regardless of content focus. These ontology editors may have enhanced support for information standards unique to their target domain. Editors may also specifically support a broad upper level ontology, as in the case of the editing environment that has grown up around the unique Cyc ontology and is being released under the OpenCyc initiative.

The enterprise-oriented products have mostly started out as data integration tools like those from Unicorn Solutions and Modulant or as content management tools like Applied Semantics' offering.

These latter products are more likely to include linguistic classification and stochastic analysis capabilities to aid in information extraction from unstructured content. This information can potentially become instance data or extend the ontology itself. A few ontology editors included in the survey are actually software specification tools that are sufficiently general purpose to allow construction of domain ontologies.

These tools, like Microsoft's Visio for Enterprise Architects , use an object-oriented specification language to model an information domain in this case, the Object Role Modeling language. When ontology technologies emerged in the s, the focus on knowledge acquisition influenced the way new capabilities were put to use in the field. Early ontology editors, for example, adopted the popular KADS method for developing knowledge bases.

This orientation is not as evident in today's tools. Indeed, explicit support for a particular knowledge engineering methodology is not common. There is also increasing support for common upper level ontologies like WordNet , Cyc, and others. Ontology building today is a fragmented practice.

The situation, in part, is a result of the proliferation of logic languages and information models that have combined to yield even more ontology forms and editing environments. These tools and methodologies, along with the ontologies built with them, generally exist without proven interoperability.