David Isern

Ontology-based semantic similarity: A new feature-based approach

Estimation of the semantic likeness between words is of great importance in many applications dealing with textual data such as natural language processing, knowledge acquisition and information retrieval. Semantic similarity measures exploit knowledge sources as the base to perform the estimations. In recent years, ontologies have grown in interest thanks to global initiatives such as the Semantic Web, offering an structured knowledge representation. Thanks to the possibilities that ontologies enable regarding semantic interpretation of terms many ontology-based similarity measures have been developed. According to the principle in which those measures base the similarity assessment and the way in which ontologies are exploited or complemented with other sources several families of measures can be identified. In this paper, we survey and classify most of the ontology-based approaches developed in order to evaluate their advantages and limitations and compare their expected performance both from theoretical and practical points of view. We also present a new ontology-based measure relying on the exploitation of taxonomical features. The evaluation and comparison of our approachs results against those reported by related works under a common framework suggest that our measure provides a high accuracy without some of the limitations observed in other works.

Ontology-based information content computation

The information content (IC) of a concept provides an estimation of its degree of generality/concreteness, a dimension which enables a better understanding of concepts semantics. As a result, IC has been successfully applied to the automatic assessment of the semantic similarity between concepts. In the past, IC has been estimated as the probability of appearance of concepts in corpora. However, the applicability and scalability of this method are hampered due to corpora dependency and data sparseness. More recently, some authors proposed IC-based measures using taxonomical features extracted from an ontology for a particular concept, obtaining promising results. In this paper, we analyse these ontology-based approaches for IC computation and propose several improvements aimed to better capture the semantic evidence modelled in the ontology for the particular concept. Our approach has been evaluated and compared with related works (both corpora and ontology-based ones) when applied to the task of semantic similarity estimation. Results obtained for a widely used benchmark show that our method enables similarity estimations which are better correlated with human judgements than related works.

Computer-based execution of clinical guidelines: A review

PURPOSE Clinical guidelines are useful tools to standardize and improve health care. The automation of the guideline execution process is a basic step towards its widespread use in medical centres. This paper presents an analysis and a comparison of eight systems that allow the enactment of clinical guidelines in a (semi) automatic fashion. METHODS This paper presents a review of the literature (2000-2007) collected from medical databases as well as international conferences in the medical informatics area. RESULTS Eight systems containing a guideline execution engine were selected. The language used to represent the guidelines as well as the architecture of these systems were compared. Different aspects have been assessed for each system, such as the integration with external elements or the coordination mechanisms used in the execution of clinical guidelines. Security and terminology issues complement the above study. CONCLUSIONS Although these systems could be beneficial for clinicians and patients, it is an ongoing research area, and they are not yet fully implemented and integrated into existing careflow management systems and hence used in daily practice in health care institutions.

SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities

SigTur/E-Destination is a Web-based system that provides personalized recommendations of touristic activities in the region of Tarragona. The activities are properly classified and labeled according to a specific ontology, which guides the reasoning process. The recommender takes into account many different kinds of data: demographic information, travel motivations, the actions of the user on the system, the ratings provided by the user, the opinions of users with similar demographic characteristics or similar tastes, etc. The system has been fully designed and implemented in the Science and Technology Park of Tourism and Leisure. The paper presents a numerical evaluation of the correlation between the recommendations and the users motivations, and a qualitative evaluation performed by end users.

Content annotation for the semantic web: an automatic web-based approach

Semantic Annotation is required to add machine-readable content to natural language text. A global initiative such as the Semantic Web directly depends on the annotation of massive amounts of textual Web resources. However, considering the amount of those resources, a manual semantic annotation of their contents is neither feasible nor scalable. In this paper we introduce a methodology to partially annotate textual content of Web resources in an automatic and unsupervised way. It uses several well-established learning techniques and heuristics to discover relevant entities in text and to associate them to classes of an input ontology by means of linguistic patterns. It also relies on the Web information distribution to assess the degree of semantic co-relation between entities and classes of the input domain ontology. Special efforts have been put in minimizing the amount of Web accesses required to evaluate entities in order to ensure the scalability of the approach. A manual evaluation has been carried out to test the methodology for several domains showing promising results.

Organizational structures supported by agent-oriented methodologies

Agent technology is a software paradigm that permits to implement large and complex distributed applications. In order to assist the development of multi-agent systems, agent-oriented methodologies (AOM) have been created in the last years to support modelling more and more complex applications. Even though agents are perceived as autonomous entities that act according to some objectives, they are also members of a society, and have to exchange information with other agents and maintain some relationships at an organizational level. Modern AOMs should be able to capture and represent organizational structures, defining interaction and collaboration patterns between agents, their internal roles and dependencies between groups of agents. This paper analyses the most notable AOMs, paying attention to the support and possibilities that they offer for modelling organizational structures with different levels of complexity. This work can help developers to select the most appropriate methodology taking into account the social and organizational requirements of the multi-agent system to be deployed.

Ontology-driven execution of clinical guidelines

Clinical guidelines (CG) contain general descriptions, defined by health care organisations, of the way in which a particular pathology should be treated. Their adoption in daily care offers several benefits to both patients and practitioners, such as the standardisation of the delivered care and the reduction of errors, but, at the same time, there are several issues that limit their application. CGs are designed to cover a disease taking into account the available evidence but are not designed to be deployed in a particular hospital or healthcare institution. CGs include general recommendations that should be translated according the particular settings before adoption in daily care. This adoption should also specify accountable information about the responsible actors of performing actions in healthcare teams in order to avoid errors arising during delegation/assignment of tasks. In addition, this enactment is not performed taking into account a central knowledge base or a single actor. This paper proposes the combination of a multi-agent system modelling complex healthcare organisations and knowledge representation techniques in order to build a general framework for enabling the enactment of CGs in the context of a medical centre. As a main contribution, the ontological paradigm and the expressiveness of modern ontology languages are used to design, implement and exploit a medico-organisational ontology aimed to provide the semantics required to support the execution of clinical guidelines. The knowledge-driven guideline enactment is managed by a multi-agent system modelling in a distributed fashion the clinical entities involved in the care delivery.

Automatic extraction of acronym definitions from the Web

Acronyms are widely used to abbreviate and stress important concepts. The discovery of the definitions associated to an acronym is an important matter in order to support language processing and knowledge-related tasks as information retrieval, ontology mapping or question answering. Acronyms represent a very dynamic and unbounded topic that is constantly evolving. Manual attempts to compose a global scale dictionary of acronym-definition pairs result in an overwhelming amount of work and limited results. Attending these shortcomings, this paper presents an automatic and unsupervised methodology to generate acronyms and extract their potential definitions from the Web. The method has been designed to minimise the set of constraints, offering a domain and -partially- language independent solution, and to exploit the Web in order to create large and general acronym-definition sets. Results have been manually evaluated against the largest manually built acronym repository: Acronym Finder. The evaluation shows that the proposed approach is able to improve the coverage of manual attempts maintaining a high precision.

Applying Agent Technology to Healthcare: The GruSMA Experience

The Multiagent Systems Group (GruSMA) is part of the Research Group on Artificial Intelligence at Spains University Rovira i Virgili. GruSMA specializes in agent technology, working in application areas such as mobile devices, transportation and tourism user services, automated ontology construction, and agent-based simulation of complex systems. Over the past five years, GruSMA has designed and developed many agent applications to address healthcare problems. Some of these systems are academic, proof-of-concept exercises, but they point to the kind of problems in medicine that intelligent agents can help solve in the near term

HeCaSe2: A Multi-agent Ontology-Driven Guideline Enactment Engine

HeCaSe2 is a multi-agent system that intends to help doctors to apply clinical guidelines to their patients in a semi-automatic fashion. HeCaSe2 agents need a lot of (scattered) information on health care organisations, as well as medical knowledge, in order to provide an efficient support to health care practitioners. Modelling all these data is certainly a hard task. The paper describes how the inclusion of an especially designed ontology allows different agents to coordinate their activities in the enactment of clinical guidelines.