Semantic Search Engines

A good search engine locates what the user asks for. An exceptional one finds what the user really needs. Semantic search engines are able to make this distinction and to adapt the results to the actual intent of each query.

As the volume and variety of data continue to rise, keyword-driven search reduces its effectiveness. To provide relevant results it is necessary to understand the relationships between words. This challenge is successfully faced by semantic search engines, capable of understanding contextual meaning of terms.

Benefits of Semantic Search Engines

Semantics is the scientific discipline that studies the meaning of linguistic units and their combinations. Its application improves the efficiency and effectiveness of the search engines.

Semantic search has numerous benefits, including:

Time saving: A knowledge worker spends more than 8% of his workday searching for information. This percentage reaches 30% in the case of some executives. Therefore, semantic search engines are a key tool for a more efficient and effective knowledge management.
Accuracy: A word can have different meanings because of phenomena such as polysemy and synonymy. Disambiguation is the process of identifying which sense of a word is used in a given context. Semantic networks, which represent semantic relations between concepts, allow this word-sense disambiguation.
Increased revenue: One in three e-commerce customers uses internal search engines and their purchase decision depend to a large extent on the accuracy of the search results.
Natural Language: Semantic engines are able to manage queries expressed in natural language. Therefore, users can use full sentences rather than simple keywords.

A knowledge worker spends more than 8% of his workday searching for information.

Custom search engines

At 3.14, we are specialists in designing search engines tailored to each client’s needs. Depending on the objectives of the project, we combine artificial intelligence (AI), machine learning and natural language processing (NLP) techniques.

In order to maximize the desired results, our search engines combine the following features and functionalities:

Segmentation: Two people can refer to completely different concepts by using an identical term. Even the same person, depending on the time and context, may expect different results from a particular query. To solve these ambiguities, intelligent search engines take into account all the user information and compare it with data from other users who share similar characteristics. This segmentation process can be done by combining different criteria:
- Demographic segmentation: This category includes sex, age, education, occupation, or any other demographic variable that helps the system understand the user’s needs.
- Geographic segmentation: User location may modify or restrict the meaning of a query. Intelligent search engines take this factor into account to offer the most relevant results.
- Behavioral segmentation: Search history, consumption habits or any other user behavior data are key to being able to correctly interpret the actual search intent.
- Time segmentation: People’s interests vary by day and time. It is likely that the same user wants different answers depending on whether the query is posed during working hours or on weekends.
Recommendations: Based on user search history and similar users' preferences, our search engines are able to offer personalized recommendations. Thus, if a customer is searching an item in an e-commerce, the system can also suggest complementary products. Likewise, if a user is looking for a word, the system may offer him related terms. An intelligent search engine is able to learn from its own experience and gradually refine the effectiveness of its recommendations.
Advanced Search: This option allows users to refine their queries and, consequently, to limit the results. For instance, a user who is looking for a book can search by title or scan every page of every book.
Faceted search or search filters: A good search engine should allow users to filter the results obtained. If the filters are intelligent, they will vary according to the query. This feature is crucial if the search engine is integrated into a system that manages a wide range of information. For example, faceted search allows an e-commerce to apply some filters to a shirt (size, color, fabric ...) and very different ones to a laptop (operating system, processor, screen size, hard disk drive ...).
Sorting: The possibility of sorting the results helps to reduce the search time. For instance, it is useful if a user wants to find the cheapest product or the latest version of an item.
Autocomplete: By predicting users’ queries, autocomplete option avoids errata, helps in the use of more accurate terms and reduces search time.
Lemmatization: A powerful search engine not only locates the word that the user has entered into the search box, but also recognizes the corresponding lemma and its different inflectional forms (plural, feminine, conjugated forms ...).
Speed: Speed is an indispensable feature. Regardless of the amount and complexity of information, the search engine must satisfy the requirement of immediacy.
Usability: As with speed, usability is an essential requirement. User experience must be satisfactory from any device. Therefore, it is necessary to carefully decide the size and placement of the search box and the layout of the results pages. It is also important to follow standards and check consistency.

Our search engines bring together varied characteristics, but all the features are aligned towards the same goal: to offer the user exactly what he needs in the shortest possible time.