The impact of the application of wood on the technological preparation for furniture manufacturing

This paper analyzes the influence of the application of wood on the technological preparation of furniture production. When it comes to furniture production the wood as a choice of material is the most used material today as it was in the past. It describes the physical and chemical properties of the wood, its technology for making furniture, as well as the influence of the choice of the type of wood on the application of furniture production. From the conducted research in furniture manufacturing companies it can be concluded that at the moment the most common wood for furniture production in North Macedonia is the beech, the second is the oak, and the third is the walnut. The technologies used to process these types of wood are up to date with world-class technology, and when economies are required, sometimes chipboard and mediapan are also used. The best type of wood for making furniture is the oak, but from the research it can be concluded that besides being less available from the beech, it is also more difficult for processing and requires different machining

Wood as a primary selection of material for furniture production

This paper analyzes the influence of wood as the primary choice of material for furniture production. The wood, with its physical, chemical and mechanical properties, stands out from other materials when making a selection of material for furniture production. The wood possesses excellent physical properties, hardness, strength and density, chemical properties, wood does not rust and mechanical properties, elasticity, bending strength and tensile strength. Each type of wood has its own specific propertiesthat make it unique, even the same kind of wood grown in a different place has different properties, the oak has great strength and is therefore difficult to work while the beech has less firmness then the oak and therefore it is easier for machining. Proper choice of material also exerts a great influence on the very design of the piece of furniture. The choice of material depends on which design will be used in the manufacture of furniture. A well thought-out design can be adapted to the choice of material sometimes, but with wood as the primary choice of material for furniture production the design itself gets on quality

Learning expressive linkage rules from sparse data

A central problem in the context of the Web of Data as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. There exists a large body of research on entity resolution. Interestingly, most of the existing research focuses on entity resolution on dense data, meaning data that does not contain too many missing values. This paper sets a different focus and explores learning expressive linkage rules from as well as applying these rules to sparse data, i.e. data exhibiting a large amount of missing values. Sparse data is a common challenge in application domains such as e-commerce, online hotel booking, or online recruiting. We propose and compare three entity resolution methods that employ genetic programming to learn expressive linkage rules from sparse data. First, we introduce the GenLinkGL algorithm which learns groups of matching rules and applies specific rules out of these groups depending on which values are missing from a pair of records. Next, we propose GenLinkSA, which employs selective aggregation operators within rules. These operators exclude misleading similarity scores (which result from missing values) from the aggregations, but on the other hand also penalize the uncertainty that results from missing values. Finally, we introduce GenLinkComb, an algorithm which combines the central ideas of the previous two into one integrated method. We evaluate all methods using six benchmark datasets: three of them are e-commerce product datasets, the other datasets describe restaurants, movies, and drugs. We show improvements of up to 16% F-measure compared to handwritten rules, on average 12% F-measure improvement compared to the original GenLink algorithm, 15% compared to EAGLE, 8% compared to FEBRL, and 5% compared to CoSum-P

Extracting attribute-value pairs from product specifications on the web

Comparison shopping portals integrate product offers from large numbers of e-shops in order to support consumers in their buying decisions. Product offers often consist of a title and a free-text product description, both describing product attributes that are considered relevant by the specific vendor. In addition, product offers might contain structured or semi-structured product specifications in the form of HTML tables and HTML lists. As product specifications often cover more product attributes than free-text descriptions, being able to extract attribute-value pairs from these specifications is a critical prerequisite for achieving good results in tasks such as product matching, product categorisation, faceted product search, and product recommendation. In this paper, we present an approach for extracting attribute value pairs from product specifications on the Web. We use supervised learning to classify the HTML tables and HTML lists within a web page as product specification or not. In order to extract attribute-value pairs from the HTML fragments identified by the specification detector, we again use supervised learning to classify columns as attribute column or value column. Compared to DEXTER, the current state-of-the-art approach for extracting attribute value pairs from product specifications, we introduce several new features for specification detection and support the extraction of attribute-value pairs from specifications having more than two columns. This allows us to improve the F-score up to 10% for extracting attribute-value pairs from tables and up to 3% for lists. In addition, we report the results of using duplicate-based schema matching to align the product attribute schemata of 32 different e-shops. This experiment confirms the suitability of duplicate-based schema matching for product data integration

Integrating Product Data from Websites offering Microdata Markup

Large numbers of websites have started to markup their content using standards such as Microdata, Microformats, and RDFa. The marked-up content elements comprise descriptions of people, organizations, places, events, products, ratings, and reviews. This development has accelerated in last years as major search engines such as Google, Bing and Yahoo! use the markup to improve their search results. Embedding semantic markup facilitates identifying content elements on webpages. However, the markup is mostly not as fine-grained as desirable for applications that aim to integrate data from large numbers of websites. This paper discusses the challenges that arise in the task of integrating descriptions of electronic products from several thousand e-shops that offer Microdata markup. We present a solution for each step of the data integration process including Microdata extraction, product classification, product feature extraction, identity resolution, and data fusion. We evaluate our processing pipeline using 1.9 million product offers from 9240 e-shops which we extracted from the Common Crawl 2012, a large public Web corpus

The WebDataCommons Microdata, RDFa and Microformat Dataset Series

In order to support web applications to understand the content of HTML pages an increasing number of websites have started to annotate structured data within their pages using markup formats such as Microdata, RDFa, Microformats. The annotations are used by Google, Yahoo!, Yandex, Bing and Facebook to enrich search results and to display entity descriptions within their applications. In this paper, we present a series of publicly accessible Microdata, RDFa, Microformats datasets that we have extracted from three large Web corpora dating 2010, 2012 and 2013. Altogether, the datasets consist of almost 30 billion RDF quads. The most recent of the datasets contains amongst other data over $211$ million product descriptions, 54 million reviews and 125 million postal addresses originating from thousands of websites. The availability of the datasets lays the foundation for further research on integrating and cleansing the data as well as for exploring its utility within different application contexts. As the dataset series covers four years, it can also be used to analyze the evolution of the adoption of the markup formats