A semantic-based system for querying personal digital libraries

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-28640-0_4. Copyright @ Springer 2004.The decreasing cost and the increasing availability of new technologies is enabling people to create their own digital libraries. One of the main topic in personal digital libraries is allowing people to select interesting information among all the different digital formats available today (pdf, html, tiff, etc.). Moreover the increasing availability of these on-line libraries, as well as the advent of the so called Semantic Web [1], is raising the demand for converting paper documents into digital, possibly semantically annotated, documents. These motivations drove us to design a new system which could enable the user to interact and query documents independently from the digital formats in which they are represented. In order to achieve this independence from the format we consider all the digital documents contained in a digital library as images. Our system tries to automatically detect the layout of the digital documents and recognize the geometric regions of interest. All the extracted information is then encoded with respect to a reference ontology, so that the user can query his digital library by typing free text or browsing the ontology

About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN

Automated Retrieval of Non-Engineering Domain Solutions to Engineering Problems

Organised by: Cranfield UniversityBiological inspiration for engineering design has occurred through a variety of techniques such as creation and use of databases, keyword searches of biological information in natural-language format, prior knowledge of biology, and chance observations of nature. This research focuses on utilizing the reconciled Functional Basis function and flow terms to identify suitable biological inspiration for function based design. The organized search provides two levels of results: (1) associated with verb function only and (2) narrowed results associated with verb-noun (function-flow). A set of heuristics has been complied to promote efficient searching using this technique. An example for creating smart flooring is also presented and discussed.Mori Seiki – The Machine Tool Compan

Semantic integration of geospatial concepts - a study on land use land cover classification systems

In GI Science, one of the most important interoperability is needed in land use and land cover (LULC) data, because it is key to the evaluation of LULC's many environmental impacts throughout the globe (Foley et al. 2005). Accordingly, this research aims to address the interoperability of LULC information derived by different authorities using different classificatory approaches. LULC data are described by LULC classification systems. The interoperability of LULC data hinges on the semantic integration of LULC classification systems. Existing works on semantically integrating LULC classification systems has a major drawback in finding comparable semantic representations from textual descriptions. To tackle this problem, we borrowed the method of comparing documents in information retrieval, and applied it to comparing LULC category names and descriptions. The results showed significant improvement comparing to previous works. However, lexical semantic methods are not able to solve the semantic heterogeneities in LULC classification systems: the confounding conflict - LULC categories under similar labels and descriptions have different LULC status in reality, and the naming conflict - LULC categories under different labels represent similar LULC type. Without confirmation of their actual land cover status from remote sensing, lexical semantic method cannot achieve reliable matching. To discover confounding conflicts and reconcile naming conflicts, we developed an innovative method of applying remote sensing to the integration of LULC classification systems. Remote sensing is a means of observation on actual LULC status of individual parcels. We calculated parcel level statistics from spectral and textural data, and used these statistics to calculate category similarity. The matching results showed this approach fulfilled its goal - to overcome semantic heterogeneities and achieve more reliable and accurate matching between LULC classifications in the majority of cases. To overcome the limitations of either method, we combined the two by aggregating their output similarities, and achieve better integration. LULC categories that post noticeable differences between lexical semantics and remote sensing once again remind us of semantic heterogeneities in LULC classification systems that must to be overcome before LULC data from different sources become interoperable and serve as the key to understanding our highly interrelated Earth system

Business Ontology for Evaluating Corporate Social Responsibility

This paper presents a software solution that is developed to automatically classify companies by taking into account their level of social responsibility. The application is based on ontologies and on intelligent agents. In order to obtain the data needed to evaluate companies, we developed a web crawling module that analyzes the company’s website and the documents that are available online such as social responsibility report, mission statement, employment structure, etc. Based on a predefined CSR ontology, the web crawling module extracts the terms that are linked to corporate social responsibility. By taking into account the extracted qualitative data, an intelligent agent, previously trained on a set of companies, computes the qualitative values, which are then included in the classification model based on neural networks. The proposed ontology takes into consideration the guidelines proposed by the “ISO 26000 Standard for Social Responsibility”. Having this model, and being aware of the positive relationship between Corporate Social Responsibility and financial performance, an overall perspective on each company’s activity can be configured, this being useful not only to the company’s creditors, auditors, stockholders, but also to its consumers.corporate social responsibility, ISO 26000 Standard for Social Responsibility, ontology, web crawling, intelligent agent, corporate performance, POS tagging, opinion mining, sentiment analysis

Multi Domain Semantic Information Retrieval Based on Topic Model

Over the last decades, there have been remarkable shifts in the area of Information Retrieval (IR) as huge amount of information is increasingly accumulated on the Web. The gigantic information explosion increases the need for discovering new tools that retrieve meaningful knowledge from various complex information sources. Thus, techniques primarily used to search and extract important information from numerous database sources have been a key challenge in current IR systems. Topic modeling is one of the most recent techniquesthat discover hidden thematic structures from large data collections without human supervision. Several topic models have been proposed in various fields of study and have been utilized extensively for many applications. Latent Dirichlet Allocation (LDA) is the most well-known topic model that generates topics from large corpus of resources, such as text, images, and audio.It has been widely used in many areas in information retrieval and data mining, providing efficient way of identifying latent topics among document collections. However, LDA has a drawback that topic cohesion within a concept is attenuated when estimating infrequently occurring words. Moreover, LDAseems not to consider the meaning of words, but rather to infer hidden topics based on a statisticalapproach. However, LDA can cause either reduction in the quality of topic words or increase in loose relations between topics. In order to solve the previous problems, we propose a domain specific topic model that combines domain concepts with LDA. Two domain specific algorithms are suggested for solving the difficulties associated with LDA. The main strength of our proposed model comes from the fact that it narrows semantic concepts from broad domain knowledge to a specific one which solves the unknown domain problem. Our proposed model is extensively tested on various applications, query expansion, classification, and summarization, to demonstrate the effectiveness of the model. Experimental results show that the proposed model significantly increasesthe performance of applications

Enhancing information retrieval in folksonomies using ontology of place constructed from Gazetteer information

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesFolksonomy (from folk and taxonomy) is an approach to user metadata creation where users describe information objects with a free-form list of keywords (‘tags’). Folksonomy has have proved to be a useful information retrieval tool that support the emergence of “collective intelligence” or “bottom-up” light weight semantics. Since there are no guiding rules or restrictions on the users, folksonomy has some drawbacks and problems as lack of hierarchy, synonym control, and semantic precision. This research aims at enhancing information retrieval in folksonomy, particularly that of location information, by establishing explicit relationships between place name tags. To accomplish this, an automated approach is developed. The approach starts by retrieving tags from Flickr. The tags are then filtered to identify those that represent place names. Next, the gazetteer service that is a knowledge organization system for spatial information is used to query for the place names. The result of the search from the gazetteer and the feature types are used to construct an ontology of place. The ontology of place is formalized from place name concepts, where each place has a “Part-Of” relationship with its direct parent. The ontology is then formalized in OWL (Web Ontology Language). A search tool prototype is developed that extracts a place name and its parent name from the ontology and use them for searching in Flickr. The semantic richness added to Flickr search engine using our approach is tested and the results are evaluated