KWTR: Outreach to Industry
From semanticweb.org
Main Contributors: Alain Leger, Lyndon J B Nixon. See the list of contributors
- 1. CURRENT TRENDS IN SEMANTIC WEB (In the following part we intend to identify the state of the art of Semantic Web based theories, methods, applications and tools in your research field.)
- 1.1. One or more examples (case studies) in which semantic web has been used.
Name of the institutions: WorldWideJobs GmbH Industry / sector: Human Resources Business activities improved by the SW solutions: Search and retrieval Research Needs: Ontology matching, efficiency of storage and retrieval Name of the project: Wissensnetze (German funded national project) Tools and applications implemented in the project: Prototype with Web UI - Semantic Matching Engine Other (specify) OWL-DL ontologies developed based on existing industry specifications
- 1.2. The first 4 Semantic Web based tools used in your research fields.
Name: Protege Website: http://protege.stanford.edu Main characteristics: GUI based ontology editor Open problems: Ontology visualisation -- high relevance -- will be solved in the medium term Modularisation (for large ontologies) -- high relevance -- will be solved in the medium term Name: Jena Website: http://jena.sourceforge.net White page http://jena.sourceforge.net/documentation.html Main characteristics: Java API for Semantic Web applications Open problems: Name: Sesame Website: http://www.openrdf.org Main characteristics: RDF storage based on RDBMS Open problems: Scalability -- very high relevance -- will be solved in the medium term Standardized query support -- normal relevance -- will be solved in the short term Name: RACER Website: http://www.sts.tu-harburg.de/~r.f.moeller/racer/ Main characteristics: Description Logic reasoner Open problems: Efficiency when reasoning over large datasets -- high relevance -- will be solved in the medium term
- 1.3. A short summary of the first 3 best papers in the field.
Reference: DFrom SHIQ and RDF to OWL: the making of a Web Ontology Language. Short abstract: The OWL Web Ontology Language is a new formal language for representing ontologies in the Semantic Web. OWL has features from several families of representation languages, including primarily Description Logics and frames. OWL also shares many characteristics with RDF, the W3C base of the Semantic Web. In this paper we discuss how the philosophy and features of OWL can be traced back to these older formalisms, with modifications driven by several other constraints on OWL.. Several interesting problems have arisen where these influences on OWL have clashed.
Reference: Learning to Map between Ontologies on the Semantic Web. Short abstract: Ontologies play a prominent role on the Semantic Web. They make possible the widespread publication of machine understandable data, opening myriad opportunities for automated information processing. However, because of the Semantic Web's distributed nature, data on it will inevitably come from many different ontologies. Information processing across ontologies is not possible without knowing the semantic mappings between their elements. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web.We describe glue, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology glue finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that glue can work with all of them. This is in contrast to most existing approaches, which deal with a single similarity measure. Another key feature of glue is that it uses multiple learning strategies, each of which exploits a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend glue to incorporate commonsense knowledge and domain constraints into the matching process. For this purpose, we show that relaxation labeling, a well-known constraint optimization technique used in computer vision and other fields, can be adapted to work efficiently in our context. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that glue proposes highly accurate semantic mappings.
Reference: RDF Primer. Short abstract: The Resource Description Framework (RDF) is a language for representing information about resources in the World Wide Web. This Primer is designed to provide the reader with the basic knowledge required to effectively use RDF. It introduces the basic concepts of RDF and describes its XML syntax. It describes how to define RDF vocabularies using the RDF Vocabulary Description Language, and gives an overview of some deployed RDF applications. It also describes the content and purpose of other RDF specification documents.
- 1.4. A short list of open problems in theories and methods.
Choice of logical model on which to build KR (DL, FOL, F-Lofic, LP): normal relevance -- will be solved in the long term Ontology engineering methodologies: high relevance -- will be solved in the medium term Ontology alignment for mapping, merging etc. : normal relevance -- will be solved in the medium term
- 2. TRENDS ON THEORIES AND METHODS, SERVICES AND APPLICATIONS
- 2.1. Research projects in which contributors are involved, along
with a general description. Moreover, suggest for each project the possible future uses and applications related to the Semantic Web, the acceptance and diffusion in each period considered, the benefits, and the problems that will be probably occur.
Name of the project: Knowledge Web Type: EU Network of Excellence Duration: 4 years (2004-7) Partners: Research Institution: FU Berlin, Uni Trento, VUA, VUB, Uni Karlsruhe, INRIA among others Industrial Partners: France Telecom Core activities: Technological transfer to industry -- very high relevance -- will be solved in the medium/long term Semantic Web research -- high relevance -- will be solved in the short/medium term Education for Semantic Web -- very high relevance -- will be solved in the medium term Market opportunities: Semantic search -- normal acceptance and diffusion in the short term Semantic social networking -- high acceptance and diffusion in the medium term Semantic Web Services -- normal acceptance and diffusion in the long term Benefits for industry and practitioners Industry-capable tools and methods -- very high relevance Best practices and guidelines for ontology development and use -- high relevance Ontology evaluation and recommendation -- normal relevance Technological Problems (missing theories and methods) Ontology best practices -- high relevance -- will be solved in the medium term Semantic Web tool benchmarks -- high relevance -- will be solved in the short term Ontology evaluation and cost modelling -- high relevance -- will be solved in the short term Problems and missing tools: Ontology engineering tools -- high relevance -- will be solved in the medium term Ontology re-use support -- high relevance -- will be solved in the medium term Ontology alignment support -- normal relevance -- will be solved in the medium term Problems – Semantic Web culture is missing: Introduction of metadata generation in content creation processes -- high relevance -- will be solved in the medium term Industrial use of ontologies (business people are not logicians) -- very high relevance -- will be solved in the long term Other problems Ontology recommendation -- normal relevance -- will be solved in the long term On-the-fly ontology alignment -- low relevance -- will be solved in the long term Concept mapping -- normal relevance -- will be solved in the long term
Name of the project: TripCom Type: EU STREP Duration: 3 years (April 2006-2009) Partners: Research Institution: FU Berlin, TU Vienna, U Stuttgart, U Innsbruck, NUIG Galway Industrial Partners: Profium, Cefriel, Telefonica, Ontotext Core activities: Tuplespace computing -- normal relevance -- will be solved in the medium term Semantic Web Services -- high relevance -- will be solved in the long term Market opportunities: Communication middleware for semantic SOA applications -- normal acceptance and diffusion in the long term Web scale service-oriented computing -- high accceptance and diffusion in the medium term Support infrastructure for “intelligent” services -- high accceptance and diffusion in the long term Benefits for industry and practitioners Coordination of Web Service communication -- normal relevance Mediation between different business vocabularies -- high relevance Technological Problems (missing theories and methods) Semantic coordination of data -- normal relevance -- will be solved in the short term Representation of RDF in tuplespace -- normal relevance -- will be solved in the short term Problems and missing tools: Semantic tuplespace platform -- high relevance -- will be solved in the medium term Web-scale RDF storage -- very high relevance -- will be solved in the medium term Problems – Semantic Web culture is missing: Use of semantic models in business communication -- high relevance -- will be solved in the long term Use of semantics in Web Service development and application -- high relevance -- will be solved in the long term
Name of the project: Wissensnetze Type: German national project (BMBF) Duration: 2004-7 (4 years) Partners: Research Institution: FU Berlin, HU Berlin Industrial Partners: WorldWideJobs GmbH Core activities: Job portal prototype -- high relevance -- will be solved in the short term Implication of Sem Web technologies on e-business -- very high relevance -- will be solved in the medium term Market opportunities: Improved market transparency -- normal acceptance and diffusion in the medium term Strengthening job portals position as middlemen between job seekers and business -- high acceptance and diffusion in the medium term Benefits for industry and practitioners Business reaches more potential applicants -- very high relevance Relevance of suitable applicants optimized -- high relevance Technological Problems (missing theories and methods): Ontology matching methodologies -- high relevance -- will be solved in the medium term Problems – Missing tools: Ontology reuse support -- very high relevance -- will be solved in the medium term Problems – Semantic Web culture is missing: Real test data sets do not exist -- high relevance -- will be solved in the medium term
- 2.2. Some topics that will not be solved in short and medium term,
for each of them there is a short explanation of the main reasons and (if possible) some references.
Topics: Automatic high level annotation of multimedia Reason: requires non-trivial inference of concepts from a combination of low level media features and associated natural language text. While much progress is made on identifying single concepts, a complete annotation requires modelling sets of concepts and relationships between them as a representation of what a multimedia content contains. References: none
Topics: On-the-fly knowledge extraction and inference across heterogeneous knowledge Reason: Again, system-based extraction of semantics depends on complex natural language understanding as well as awareness of context to be able to formalize data correctly as knowledge. These formalisms will likely draw on different ontologies and name their concepts differently, so means to automatically align both schema and instances are required. References: none
Topics: A Web-scale Semantic Web Reason: using the Semantic Web means making use of reasoning to infer knowledge, and other approaches to have a common view on all knowledge (mapping, merging). This requires a very resource heavy logic-based approach which does not scale to the Web.
- 3. TRENDS ON TOOLS
- 3.1. A list of the most relevant semantic based demos in the area.
Name: Job Recruitment Prototype Website: Not yet public Description: Job portal with semantic search References: http://wissensnetze.ag-nbi.de Main features: RDF based input of job offers or applicants -- high relevance Ontology based matching between offers and applicants -- very high relevance Open problems: Performance of matching for large data sets -- very high relevance -- will be solved in the medium term Precision of matching -- high relevance -- will be solved in the short term Name: Hotel Search Prototype Website: Not yet public Description: Hotel search personalized to user profile References: http://reisewissen.ag-nbi.de Main features: Mapping of RDF and non-RDF vocabularies -- high relevance Ontology based ranking of hotels based on inference from user profile data -- very high relevance Open problems: Performance of reasoning -- very high relevance -- will be solved in the medium term Datatype queries -- high relevance -- will be solved in the short term Rules -- high relevance -- will be solved in the short term
- 3.2. A short description of tools that are still missing. A description of business activities and problems they should solve, will be provided.
Name Ontology engineering Description Tool based support for the creation and maintenance of an ontology Related business activities All ontology-based applications require the creation of an ontology Problems that the tool will solve Ontology creation -- high relevance to the business -- will be solved in the medium term Ontology re-use -- very high relevance to the business -- will be solved in the medium term Ontology evolution -- high relevance to the business -- will be solved in the medium term
- 4.Please fell free to add any comment or suggestion.
